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1. Systematic identification of interaction effects between genome- and environment-wide associations in type 2 diabetes mellitus

Author: Butte, Atul, Patel, CJ, Chen, R, Kodama, K, Ioannidis, JPA, and Butte, AJ
Abstract: Diseases such as type 2 diabetes (T2D) result from environmental and genetic factors, and risk varies considerably in the population. T2D-related genetic loci discovered to date explain only a small portion of the T2D heritability. Some heritability may be
Published: 2013

2. Insights into the genetic architecture of osteoarthritis from stage 1 of the arcOGEN study

Author: Panoutsopoulou, K, Southam, L, Elliott, KS, Wrayner, N, Zhai, G, Beazley, C, Thorleifsson, G, Arden, NK, Carr, A, Chapman, K, Deloukas, P, Doherty, M, McCaskie, A, Ollier, WER, Ralston, SH, Spector, TD, Valdes, AM, Wallis, GA, Wilkinson, JM, Arden, E, Battley, K, Blackburn, H, Blanco, FJ, Bumpstead, S, Cupples, LA, Day-Williams, AG, Dixon, K, Doherty, SA, Esko, T, Evangelou, E, Felson, D, Gomez-Reino, JJ, Gonzalez, A, Gordon, A, Gwilliam, R, Halldorsson, BV, Hauksson, VB, Hofman, A, Hunt, SE, Ioannidis, JPA, Ingvarsson, T, Jonsdottir, I, Jonsson, H, Keen, R, Kerkhof, HJM, Kloppenburg, MG, Koller, N, Lakenberg, N, Lane, NE, Lee, AT, Metspalu, A, Meulenbelt, I, Nevitt, MC, O'Neill, F, Parimi, N, Potter, SC, Rego-Perez, I, Riancho, JA, Sherburn, K, Slagboom, PE, Stefansson, K, Styrkarsdottir, U, Sumillera, M, Swift, D, Thorsteinsdottir, U, Tsezou, A, Uitterlinden, AG, van Meurs, JBJ, Watkins, B, Wheeler, M, Mitchell, S, Zhu, Y, Zmuda, JM, Consortium, arcOGEN, Zeggini, E, and Loughlin, J
Subjects: Biomedical and Clinical Sciences, Clinical Sciences, Immunology, Arthritis, Genetics, Prevention, Aging, Human Genome, Aetiology, 2.1 Biological and endogenous factors, Musculoskeletal, Case-Control Studies, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Multifactorial Inheritance, Osteoarthritis, Hip, Osteoarthritis, Knee, Polymorphism, Single Nucleotide, arcOGEN Consortium, Public Health and Health Services, Arthritis & Rheumatology, Clinical sciences
Abstract: ObjectivesThe genetic aetiology of osteoarthritis has not yet been elucidated. To enable a well-powered genome-wide association study (GWAS) for osteoarthritis, the authors have formed the arcOGEN Consortium, a UK-wide collaborative effort aiming to scan genome-wide over 7500 osteoarthritis cases in a two-stage genome-wide association scan. Here the authors report the findings of the stage 1 interim analysis.MethodsThe authors have performed a genome-wide association scan for knee and hip osteoarthritis in 3177 cases and 4894 population-based controls from the UK. Replication of promising signals was carried out in silico in five further scans (44,449 individuals), and de novo in 14 534 independent samples, all of European descent.ResultsNone of the association signals the authors identified reach genome-wide levels of statistical significance, therefore stressing the need for corroboration in sample sets of a larger size. Application of analytical approaches to examine the allelic architecture of disease to the stage 1 genome-wide association scan data suggests that osteoarthritis is a highly polygenic disease with multiple risk variants conferring small effects.ConclusionsIdentifying loci conferring susceptibility to osteoarthritis will require large-scale sample sizes and well-defined phenotypes to minimise heterogeneity.
Published: 2011

3. Comparison of the Accuracy of the 7-Item HADS Depression Subscale and 14-Item Total HADS for Screening for Major Depression: A Systematic Review and Individual Participant Data Meta-Analysis

Author: Wu, Y, Levis, B, Daray, FM, Ioannidis, JPA, Patten, SB, Cuijpers, P, Ziegelstein, RC, Gilbody, S, Fischer, FH, Fan, S, Sun, Y, He, C, Krishnan, A, Neupane, D, Bhandari, PM, Negeri, Z, Riehm, KE, Rice, DB, Azar, M, Yan, XW, Imran, M, Chiovitti, MJ, Boruff, JT, McMillan, D, Kloda, LA, Markham, S, Henry, M, Ismail, Z, Loiselle, CG, Mitchell, ND, Al-Adawi, S, Beck, KR, Beraldi, A, Bernstein, CN, Boye, B, Buel-Drabe, N, Bunevicius, A, Can, C, Carter, G, Chen, C-K, Cheung, G, Clover, K, Conroy, RM, Costa-Requena, G, Cukor, D, Dabscheck, E, De Souza, J, Downing, M, Feinstein, A, Ferentinos, PP, Flint, AJ, Gallagher, P, Gandy, M, Grassi, L, Haerter, M, Hernando, A, Jackson, ML, Jenewein, J, Jette, N, Juliao, M, Kjaergaard, M, Kohler, S, Konig, H-H, Krishna, LKR, Lee, Y, Loebner, M, Loosman, WL, Love, AW, Loewe, B, Malt, UF, Marrie, RA, Massardo, L, Matsuoka, Y, Mehnert, A, Michopoulos, I, Misery, L, Nelson, CJ, Ng, CG, O'Donnell, ML, O'Rourke, SJ, Ozturk, A, Pabst, A, Pasco, JA, Peceliuniene, J, Pintor, L, Ponsford, JL, Pulido, F, Quinn, TJ, Reme, SE, Reuter, K, Riedel-Heller, SG, Rooney, AG, Sanchez-Gonzalez, R, Saracino, RM, Schellekens, MPJ, Scherer, M, Schwarzbold, ML, Cankorur, VS, Sharpe, L, Sharpe, M, Simard, S, Singer, S, Stafford, L, Stone, J, Strobe, NA, Sultan, S, Teixeira, AL, Tiringer, I, Turner, A, Walker, J, Walterfang, M, Wang, L-J, Weyerer, SB, White, J, Wiese, B, Williams, LJ, Wong, L-Y, Benedetti, A, Thombsi, BD, Wu, Y, Levis, B, Daray, FM, Ioannidis, JPA, Patten, SB, Cuijpers, P, Ziegelstein, RC, Gilbody, S, Fischer, FH, Fan, S, Sun, Y, He, C, Krishnan, A, Neupane, D, Bhandari, PM, Negeri, Z, Riehm, KE, Rice, DB, Azar, M, Yan, XW, Imran, M, Chiovitti, MJ, Boruff, JT, McMillan, D, Kloda, LA, Markham, S, Henry, M, Ismail, Z, Loiselle, CG, Mitchell, ND, Al-Adawi, S, Beck, KR, Beraldi, A, Bernstein, CN, Boye, B, Buel-Drabe, N, Bunevicius, A, Can, C, Carter, G, Chen, C-K, Cheung, G, Clover, K, Conroy, RM, Costa-Requena, G, Cukor, D, Dabscheck, E, De Souza, J, Downing, M, Feinstein, A, Ferentinos, PP, Flint, AJ, Gallagher, P, Gandy, M, Grassi, L, Haerter, M, Hernando, A, Jackson, ML, Jenewein, J, Jette, N, Juliao, M, Kjaergaard, M, Kohler, S, Konig, H-H, Krishna, LKR, Lee, Y, Loebner, M, Loosman, WL, Love, AW, Loewe, B, Malt, UF, Marrie, RA, Massardo, L, Matsuoka, Y, Mehnert, A, Michopoulos, I, Misery, L, Nelson, CJ, Ng, CG, O'Donnell, ML, O'Rourke, SJ, Ozturk, A, Pabst, A, Pasco, JA, Peceliuniene, J, Pintor, L, Ponsford, JL, Pulido, F, Quinn, TJ, Reme, SE, Reuter, K, Riedel-Heller, SG, Rooney, AG, Sanchez-Gonzalez, R, Saracino, RM, Schellekens, MPJ, Scherer, M, Schwarzbold, ML, Cankorur, VS, Sharpe, L, Sharpe, M, Simard, S, Singer, S, Stafford, L, Stone, J, Strobe, NA, Sultan, S, Teixeira, AL, Tiringer, I, Turner, A, Walker, J, Walterfang, M, Wang, L-J, Weyerer, SB, White, J, Wiese, B, Williams, LJ, Wong, L-Y, Benedetti, A, and Thombsi, BD
Abstract: The seven-item Hospital Anxiety and Depression Scale Depression subscale (HADS-D) and the total score of the 14-item HADS (HADS-T) are both used for major depression screening. Compared to the HADS-D, the HADS-T includes anxiety items and requires more time to complete. We compared the screening accuracy of the HADS-D and HADS-T for major depression detection. We conducted an individual participant data meta-analysis and fit bivariate random effects models to assess diagnostic accuracy among participants with both HADS-D and HADS-T scores. We identified optimal cutoffs, estimated sensitivity and specificity with 95% confidence intervals, and compared screening accuracy across paired cutoffs via two-stage and individual-level models. We used a 0.05 equivalence margin to assess equivalency in sensitivity and specificity. 20,700 participants (2,285 major depression cases) from 98 studies were included. Cutoffs of ≥7 for the HADS-D (sensitivity 0.79 [0.75, 0.83], specificity 0.78 [0.75, 0.80]) and ≥15 for the HADS-T (sensitivity 0.79 [0.76, 0.82], specificity 0.81 [0.78, 0.83]) minimized the distance to the top-left corner of the receiver operating characteristic curve. Across all sets of paired cutoffs evaluated, differences of sensitivity between HADS-T and HADS-D ranged from -0.05 to 0.01 (0.00 at paired optimal cutoffs), and differences of specificity were within 0.03 for all cutoffs (0.02-0.03). The pattern was similar among outpatients, although the HADS-T was slightly (not nonequivalently) more specific among inpatients. The accuracy of HADS-T was equivalent to the HADS-D for detecting major depression. In most settings, the shorter HADS-D would be preferred. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
Published: 2023

4. Post-publication critique at top-ranked journals across scientific disciplines: a cross-sectional assessment of policies and practice

Author: Hardwicke, TE, Thibault, RT, Kosie, JE, Tzavella, L, Bendixen, T, Handcock, SA, Koeneke, VE, Ioannidis, JPA, Hardwicke, TE, Thibault, RT, Kosie, JE, Tzavella, L, Bendixen, T, Handcock, SA, Koeneke, VE, and Ioannidis, JPA
Abstract: Journals exert considerable control over letters, commentaries and online comments that criticize prior research (post-publication critique). We assessed policies (Study One) and practice (Study Two) related to post-publication critique at 15 top-ranked journals in each of 22 scientific disciplines (N = 330 journals). Two-hundred and seven (63%) journals accepted post-publication critique and often imposed limits on length (median 1000, interquartile range (IQR) 500-1200 words) and time-to-submit (median 12, IQR 4-26 weeks). The most restrictive limits were 175 words and two weeks; some policies imposed no limits. Of 2066 randomly sampled research articles published in 2018 by journals accepting post-publication critique, 39 (1.9%, 95% confidence interval [1.4, 2.6]) were linked to at least one post-publication critique (there were 58 post-publication critiques in total). Of the 58 post-publication critiques, 44 received an author reply, of which 41 asserted that original conclusions were unchanged. Clinical Medicine had the most active culture of post-publication critique: all journals accepted post-publication critique and published the most post-publication critique overall, but also imposed the strictest limits on length (median 400, IQR 400-550 words) and time-to-submit (median 4, IQR 4-6 weeks). Our findings suggest that top-ranked academic journals often pose serious barriers to the cultivation, documentation and dissemination of post-publication critique.
Published: 2022

5. Value of clinical research: Usefulness tool development and systematic review of 350 randomised controlled trials in preterm birth

Author: Bossuyt P, Hooft Jvt, Mol B, Noah Haber, Alfirevic Z, Ioannidis Jpa, Cathrine Axfors, Oudijk M, and Dijk Cv
Subjects: medicine.medical_specialty, Clinical research, business.industry, medicine, Medical physics, business, Value (mathematics)
Abstract: Objective: We developed a research usefulness tool collating published criteria and examined if randomised controlled trials (RCTs) addressing preterm birth were useful. Search Strategy: Cochrane library. Selection Criteria: Published RCTs within 56 preterm birth Cochrane reviews. Data Collection and Analysis: A usefulness tool was developed with eight criteria combining 13 items identified through literature searches and consensus. RCTs were evaluated for compliance with each item by multiple assessors (reviewer agreement 95-98%). Proportions with 95% confidence interval (CI) were calculated and compared for change over time using ≧ 2010 as a cut-off, with relative risks (RR). Main Results: Among 350 selected RCTs, only 38 (11%, 95% CI 8-15%) met half of the usefulness criteria. Compared to trials before 2010, recent trials used composite or surrogate (less informative) outcomes more often (13% vs 25%, RR 1.87, 95% CI 1.19-2.93). Only 17 trials reflected real life (pragmatism) in design (5%, 95% CI 3-8%), with no improvements over time. No trials reported involvement of mothers to reflect patients’ top priorities in question definition or outcomes selection. Recent trials were more transparent with prospective registration (0.5% vs 28%, RR 58, 95% CI 8-420%), availability of protocol (0.5% vs 15%, RR 32, 95% CI 4-237%) and data sharing statements (2% vs 8%, RR 3, 95% CI 1-10%). Conclusion: Clinical trials in preterm birth lacked many usefulness features, with one tenth of trials meeting half of the items evaluated. Use of informative outcomes, patient centeredness, pragmatism and transparency should be key targets for future research planning.
Published: 2021

6. Commentary: Time to improve the reporting of harms in randomized controlled trials

Author: Junqueira, D, Phillips, R, Zorzela, L, Golder, S, Yoon, L, Moher, D, Ioannidis, JPA, Vohra, S, and National Institute for Health Research
Subjects: Clinical trials, Reporting, Harms, Epidemiology, Adverse events, Consort, 01 Mathematical Sciences, 11 Medical and Health Sciences
Published: 2021

7. Selective cutoff reporting in studies of the accuracy of the Patient Health Questionnaire-9 and Edinburgh Postnatal Depression Scale: Comparison of results based on published cutoffs versus all cutoffs using individual participant data meta-analysis

Author: Neupane, D, Levis, B, Bhandari, PM, Thombs, BD, Benedetti, A, Sun, Y, He, C, Wu, Y, Krishnan, A, Negeri, Z, Imran, M, Rice, DB, Riehm, KE, Saadat, N, Azar, M, Sanchez, TA, Chiovitti, MJ, Levis, AW, Boruff, JT, Cuijpers, P, Gilbody, S, Ioannidis, JPA, Kloda, LA, Patten, SB, Shrier, I, Ziegelstein, RC, Comeau, L, Mitchell, ND, Tonelli, M, Vigod, SN, Akena, DH, Alvarado, R, Arroll, B, Bakare, MO, Baradaran, HR, Beck, CT, Bombardier, CH, Bunevicius, A, Carter, G, Chagas, MH, Chaudron, LH, Cholera, R, Clover, K, Conwell, Y, Castro e Couto, T, de Man-van Ginkel, JM, Delgadillo, J, Fann, JR, Favez, N, Fung, D, Garcia-Esteve, L, Gelaye, B, Goodyear-Smith, F, Hyphantis, T, Inagaki, M, Ismail, K, Jetté, N, Khalifa, DS, Khamseh, ME, Kohlhoff, J, Kozinszky, Z, Kusminskas, L, Liu, SI, Lotrakul, M, Loureiro, SR, Löwe, B, Sidik, SM, Nakić Radoš, S, Osório, FL, Pawlby, SJ, Pence, BW, Rochat, TJ, Rooney, AG, Sharp, DJ, Stafford, L, Su, KP, Sung, SC, Tadinac, M, Darius Tandon, S, Thiagayson, P, Töreki, A, Torres-Giménez, A, Turner, Alyna, van der Feltz-Cornelis, CM, Vega-Dienstmaier, JM, Vöhringer, PA, White, J, Whooley, MA, Winkley, K, Yamada, M, Neupane, D, Levis, B, Bhandari, PM, Thombs, BD, Benedetti, A, Sun, Y, He, C, Wu, Y, Krishnan, A, Negeri, Z, Imran, M, Rice, DB, Riehm, KE, Saadat, N, Azar, M, Sanchez, TA, Chiovitti, MJ, Levis, AW, Boruff, JT, Cuijpers, P, Gilbody, S, Ioannidis, JPA, Kloda, LA, Patten, SB, Shrier, I, Ziegelstein, RC, Comeau, L, Mitchell, ND, Tonelli, M, Vigod, SN, Akena, DH, Alvarado, R, Arroll, B, Bakare, MO, Baradaran, HR, Beck, CT, Bombardier, CH, Bunevicius, A, Carter, G, Chagas, MH, Chaudron, LH, Cholera, R, Clover, K, Conwell, Y, Castro e Couto, T, de Man-van Ginkel, JM, Delgadillo, J, Fann, JR, Favez, N, Fung, D, Garcia-Esteve, L, Gelaye, B, Goodyear-Smith, F, Hyphantis, T, Inagaki, M, Ismail, K, Jetté, N, Khalifa, DS, Khamseh, ME, Kohlhoff, J, Kozinszky, Z, Kusminskas, L, Liu, SI, Lotrakul, M, Loureiro, SR, Löwe, B, Sidik, SM, Nakić Radoš, S, Osório, FL, Pawlby, SJ, Pence, BW, Rochat, TJ, Rooney, AG, Sharp, DJ, Stafford, L, Su, KP, Sung, SC, Tadinac, M, Darius Tandon, S, Thiagayson, P, Töreki, A, Torres-Giménez, A, Turner, Alyna, van der Feltz-Cornelis, CM, Vega-Dienstmaier, JM, Vöhringer, PA, White, J, Whooley, MA, Winkley, K, and Yamada, M
Abstract: Objectives: Selectively reported results from only well-performing cutoffs in diagnostic accuracy studies may bias estimates in meta-analyses. We investigated cutoff reporting patterns for the Patient Health Questionnaire-9 (PHQ-9; standard cutoff 10) and Edinburgh Postnatal Depression Scale (EPDS; no standard cutoff, commonly used 10–13) and compared accuracy estimates based on published cutoffs versus all cutoffs. Methods: We conducted bivariate random effects meta-analyses using individual participant data to compare accuracy from published versus all cutoffs. Results: For the PHQ-9 (30 studies, N = 11,773), published results underestimated sensitivity for cutoffs below 10 (median difference: −0.06) and overestimated for cutoffs above 10 (median difference: 0.07). EPDS (19 studies, N = 3637) sensitivity estimates from published results were similar for cutoffs below 10 (median difference: 0.00) but higher for cutoffs above 13 (median difference: 0.14). Specificity estimates from published and all cutoffs were similar for both tools. The mean cutoff of all reported cutoffs in PHQ-9 studies with optimal cutoff below 10 was 8.8 compared to 11.8 for those with optimal cutoffs above 10. Mean for EPDS studies with optimal cutoffs below 10 was 9.9 compared to 11.8 for those with optimal cutoffs greater than 10. Conclusion: Selective cutoff reporting was more pronounced for the PHQ-9 than EPDS.
Published: 2021

8. Probability of Major Depression Classification Based on the SCID, CIDI, and MINI Diagnostic Interviews: A Synthesis of Three Individual Participant Data Meta-Analyses.

Author: Wu, Y, Levis, B, Ioannidis, JPA, Benedetti, A, Thombs, BD, DEPRESsion Screening Data (DEPRESSD) Collaboration, Wu, Y, Levis, B, Ioannidis, JPA, Benedetti, A, Thombs, BD, and DEPRESsion Screening Data (DEPRESSD) Collaboration
Abstract: INTRODUCTION: Three previous individual participant data meta-analyses (IPDMAs) reported that, compared to the Structured Clinical Interview for the DSM (SCID), alternative reference standards, primarily the Composite International Diagnostic Interview (CIDI) and the Mini International Neuropsychiatric Interview (MINI), tended to misclassify major depression status, when controlling for depression symptom severity. However, there was an important lack of precision in the results. OBJECTIVE: To compare the odds of the major depression classification based on the SCID, CIDI, and MINI. METHODS: We included and standardized data from 3 IPDMA databases. For each IPDMA, separately, we fitted binomial generalized linear mixed models to compare the adjusted odds ratios (aORs) of major depression classification, controlling for symptom severity and characteristics of participants, and the interaction between interview and symptom severity. Next, we synthesized results using a DerSimonian-Laird random-effects meta-analysis. RESULTS: In total, 69,405 participants (7,574 [11%] with major depression) from 212 studies were included. Controlling for symptom severity and participant characteristics, the MINI (74 studies; 25,749 participants) classified major depression more often than the SCID (108 studies; 21,953 participants; aOR 1.46; 95% confidence interval [CI] 1.11-1.92]). Classification odds for the CIDI (30 studies; 21,703 participants) and the SCID did not differ overall (aOR 1.19; 95% CI 0.79-1.75); however, as screening scores increased, the aOR increased less for the CIDI than the SCID (interaction aOR 0.64; 95% CI 0.52-0.80). CONCLUSIONS: Compared to the SCID, the MINI classified major depression more often. The odds of the depression classification with the CIDI increased less as symptom levels increased. Interpretation of research that uses diagnostic interviews to classify depression should consider the interview characteristics.
Published: 2021

9. Association between convalescent plasma treatment and mortality in COVID-19: a collaborative systematic review and meta-analysis of randomized clinical trials

Author: Axfors, C, Janiaud, P, Schmitt, AM, Van't Hooft, J, Smith, ER, Haber, NA, Abayomi, A, Abduljalil, M, Abdulrahman, A, Acosta-Ampudia, Y, Aguilar-Guisado, M, Al-Beidh, F, Alejandria, MM, Alfonso, RN, Ali, M, AlQahtani, M, AlZamrooni, A, Anaya, J-M, Ang, MAC, Aomar, IF, Argumanis, LE, Averyanov, A, Baklaushev, VP, Balionis, O, Benfield, T, Berry, S, Birocco, N, Bonifacio, LB, Bowen, AC, Bown, A, Cabello-Gutierrez, C, Camacho, B, Camacho-Ortiz, A, Campbell-Lee, S, Cao, DH, Cardesa, A, Carnate, JM, Castillo, GJJ, Cavallo, R, Chowdhury, FR, Chowdhury, FUH, Ciccone, G, Cingolani, A, Climacosa, FMM, Compernolle, V, Cortez, CFN, Neto, AC, D'Antico, S, Daly, J, Danielle, F, Davis, JS, De Rosa, FG, Denholm, JT, Denkinger, CM, Desmecht, D, Diaz-Coronado, JC, Diaz Ponce-Medrano, JA, Donneau, A-F, Dumagay, TE, Dunachie, S, Dungog, CC, Erinoso, O, Escasa, IMS, Estcourt, LJ, Evans, A, Evasan, ALM, Fareli, CJ, Fernandez-Sanchez, V, Galassi, C, Gallo, JE, Garcia, PJ, Garcia, PL, Garcia, JA, Garigliany, M, Garza-Gonzalez, E, Gauiran, DT, Gaviria Garcia, PA, Giron-Gonzalez, J-A, Gomez-Almaguer, D, Gordon, AC, Gothot, A, Grass Guaqueta, JS, Green, C, Grimaldi, D, Hammond, NE, Harvala, H, Heralde, FM, Herrick, J, Higgins, AM, Hills, TE, Hines, J, Holm, K, Hoque, A, Hoste, E, Ignacio, JM, Ivanov, A, Janssen, M, Jennings, JH, Jha, V, King, RAN, Kjeldsen-Kragh, J, Klenerman, P, Kotecha, A, Krapp, F, Labanca, L, Laing, E, Landin-Olsson, M, Laterre, P-F, Lim, L-L, Lim, J, Ljungquist, O, Llaca-Diaz, JM, Lopez-Robles, C, Lopez-Cardenas, S, Lopez-Plaza, I, Lucero, JAC, Lundgren, M, Macias, J, Maganito, SC, Malundo, AFG, Manrique, RD, Manzini, PM, Marcos, M, Marquez, I, Javier Martinez-Marcos, F, Mata, AM, McArthur, CJ, McQuilten, ZK, McVerry, BJ, Menon, DK, Meyfroidt, G, Mirasol, MAL, Misset, B, Molton, JS, Mondragon, A, Monsalve, DM, Choghakabodi, PM, Morpeth, SC, Mouncey, PR, Moutschen, M, Muller-Tidow, C, Murphy, E, Najdovski, T, Nichol, AD, Nielsen, H, Novak, RM, O'Sullivan, MVN, Olalla, J, Osibogun, A, Osikomaiya, B, Oyonarte, S, Pardo-Oviedo, JM, Patel, MC, Paterson, DL, Pena-Perez, CA, Perez-Calatayud, AA, Perez-Alba, E, Perkina, A, Perry, N, Pouladzadeh, M, Poyato, I, Price, DJ, Quero, AKH, Rahman, MM, Rahman, MS, Ramesh, M, Ramirez-Santana, C, Rasmussen, M, Rees, MA, Rego, E, Roberts, JA, Roberts, DJ, Rodriguez, Y, Rodriguez-Bano, J, Rogers, BA, Rojas, M, Romero, A, Rowan, KM, Saccona, F, Safdarian, M, Santos, MCM, Sasadeusz, J, Scozzari, G, Shankar-Hari, M, Sharma, G, Snelling, T, Soto, A, Tagayuna, PY, Tang, A, Tatem, G, Teofili, L, Tong, SYC, Turgeon, AF, Veloso, JD, Venkatesh, B, Ventura-Enriquez, Y, Webb, SA, Wiese, L, Wiken, C, Wood, EM, Yusubalieva, GM, Zacharowski, K, Zarychanski, R, Khanna, N, Moher, D, Goodman, SN, Ioannidis, JPA, Hemkens, LG, Axfors, C, Janiaud, P, Schmitt, AM, Van't Hooft, J, Smith, ER, Haber, NA, Abayomi, A, Abduljalil, M, Abdulrahman, A, Acosta-Ampudia, Y, Aguilar-Guisado, M, Al-Beidh, F, Alejandria, MM, Alfonso, RN, Ali, M, AlQahtani, M, AlZamrooni, A, Anaya, J-M, Ang, MAC, Aomar, IF, Argumanis, LE, Averyanov, A, Baklaushev, VP, Balionis, O, Benfield, T, Berry, S, Birocco, N, Bonifacio, LB, Bowen, AC, Bown, A, Cabello-Gutierrez, C, Camacho, B, Camacho-Ortiz, A, Campbell-Lee, S, Cao, DH, Cardesa, A, Carnate, JM, Castillo, GJJ, Cavallo, R, Chowdhury, FR, Chowdhury, FUH, Ciccone, G, Cingolani, A, Climacosa, FMM, Compernolle, V, Cortez, CFN, Neto, AC, D'Antico, S, Daly, J, Danielle, F, Davis, JS, De Rosa, FG, Denholm, JT, Denkinger, CM, Desmecht, D, Diaz-Coronado, JC, Diaz Ponce-Medrano, JA, Donneau, A-F, Dumagay, TE, Dunachie, S, Dungog, CC, Erinoso, O, Escasa, IMS, Estcourt, LJ, Evans, A, Evasan, ALM, Fareli, CJ, Fernandez-Sanchez, V, Galassi, C, Gallo, JE, Garcia, PJ, Garcia, PL, Garcia, JA, Garigliany, M, Garza-Gonzalez, E, Gauiran, DT, Gaviria Garcia, PA, Giron-Gonzalez, J-A, Gomez-Almaguer, D, Gordon, AC, Gothot, A, Grass Guaqueta, JS, Green, C, Grimaldi, D, Hammond, NE, Harvala, H, Heralde, FM, Herrick, J, Higgins, AM, Hills, TE, Hines, J, Holm, K, Hoque, A, Hoste, E, Ignacio, JM, Ivanov, A, Janssen, M, Jennings, JH, Jha, V, King, RAN, Kjeldsen-Kragh, J, Klenerman, P, Kotecha, A, Krapp, F, Labanca, L, Laing, E, Landin-Olsson, M, Laterre, P-F, Lim, L-L, Lim, J, Ljungquist, O, Llaca-Diaz, JM, Lopez-Robles, C, Lopez-Cardenas, S, Lopez-Plaza, I, Lucero, JAC, Lundgren, M, Macias, J, Maganito, SC, Malundo, AFG, Manrique, RD, Manzini, PM, Marcos, M, Marquez, I, Javier Martinez-Marcos, F, Mata, AM, McArthur, CJ, McQuilten, ZK, McVerry, BJ, Menon, DK, Meyfroidt, G, Mirasol, MAL, Misset, B, Molton, JS, Mondragon, A, Monsalve, DM, Choghakabodi, PM, Morpeth, SC, Mouncey, PR, Moutschen, M, Muller-Tidow, C, Murphy, E, Najdovski, T, Nichol, AD, Nielsen, H, Novak, RM, O'Sullivan, MVN, Olalla, J, Osibogun, A, Osikomaiya, B, Oyonarte, S, Pardo-Oviedo, JM, Patel, MC, Paterson, DL, Pena-Perez, CA, Perez-Calatayud, AA, Perez-Alba, E, Perkina, A, Perry, N, Pouladzadeh, M, Poyato, I, Price, DJ, Quero, AKH, Rahman, MM, Rahman, MS, Ramesh, M, Ramirez-Santana, C, Rasmussen, M, Rees, MA, Rego, E, Roberts, JA, Roberts, DJ, Rodriguez, Y, Rodriguez-Bano, J, Rogers, BA, Rojas, M, Romero, A, Rowan, KM, Saccona, F, Safdarian, M, Santos, MCM, Sasadeusz, J, Scozzari, G, Shankar-Hari, M, Sharma, G, Snelling, T, Soto, A, Tagayuna, PY, Tang, A, Tatem, G, Teofili, L, Tong, SYC, Turgeon, AF, Veloso, JD, Venkatesh, B, Ventura-Enriquez, Y, Webb, SA, Wiese, L, Wiken, C, Wood, EM, Yusubalieva, GM, Zacharowski, K, Zarychanski, R, Khanna, N, Moher, D, Goodman, SN, Ioannidis, JPA, and Hemkens, LG
Abstract: BACKGROUND: Convalescent plasma has been widely used to treat COVID-19 and is under investigation in numerous randomized clinical trials, but results are publicly available only for a small number of trials. The objective of this study was to assess the benefits of convalescent plasma treatment compared to placebo or no treatment and all-cause mortality in patients with COVID-19, using data from all available randomized clinical trials, including unpublished and ongoing trials (Open Science Framework, https://doi.org/10.17605/OSF.IO/GEHFX ). METHODS: In this collaborative systematic review and meta-analysis, clinical trial registries (ClinicalTrials.gov, WHO International Clinical Trials Registry Platform), the Cochrane COVID-19 register, the LOVE database, and PubMed were searched until April 8, 2021. Investigators of trials registered by March 1, 2021, without published results were contacted via email. Eligible were ongoing, discontinued and completed randomized clinical trials that compared convalescent plasma with placebo or no treatment in COVID-19 patients, regardless of setting or treatment schedule. Aggregated mortality data were extracted from publications or provided by investigators of unpublished trials and combined using the Hartung-Knapp-Sidik-Jonkman random effects model. We investigated the contribution of unpublished trials to the overall evidence. RESULTS: A total of 16,477 patients were included in 33 trials (20 unpublished with 3190 patients, 13 published with 13,287 patients). 32 trials enrolled only hospitalized patients (including 3 with only intensive care unit patients). Risk of bias was low for 29/33 trials. Of 8495 patients who received convalescent plasma, 1997 died (23%), and of 7982 control patients, 1952 died (24%). The combined risk ratio for all-cause mortality was 0.97 (95% confidence interval: 0.92; 1.02) with between-study heterogeneity not beyond chance (I2 = 0%). The RECOVERY trial had 69.8% and the unpublished evidence 25.3% o
Published: 2021

10. External validation of a shortened screening tool using individual participant data meta-analysis: A case study of the Patient Health Questionnaire-Dep-4.

Author: Harel, D, Levis, B, Sun, Y, Fischer, F, Ioannidis, JPA, Cuijpers, P, Patten, SB, Ziegelstein, RC, Markham, S, Benedetti, A, Thombs, BD, DEPRESsion Screening Data DEPRESSD PHQ Collaboration, Harel, D, Levis, B, Sun, Y, Fischer, F, Ioannidis, JPA, Cuijpers, P, Patten, SB, Ziegelstein, RC, Markham, S, Benedetti, A, Thombs, BD, and DEPRESsion Screening Data DEPRESSD PHQ Collaboration
Abstract: Shortened versions of self-reported questionnaires may be used to reduce respondent burden. When shortened screening tools are used, it is desirable to maintain equivalent diagnostic accuracy to full-length forms. This manuscript presents a case study that illustrates how external data and individual participant data meta-analysis can be used to assess the equivalence in diagnostic accuracy between a shortened and full-length form. This case study compares the Patient Health Questionnaire-9 (PHQ-9) and a 4-item shortened version (PHQ-Dep-4) that was previously developed using optimal test assembly methods. Using a large database of 75 primary studies (34,698 participants, 3,392 major depression cases), we evaluated whether the PHQ-Dep-4 cutoff of ≥ 4 maintained equivalent diagnostic accuracy to a PHQ-9 cutoff of ≥ 10. Using this external validation dataset, a PHQ-Dep-4 cutoff of ≥ 4 maximized the sum of sensitivity and specificity, with a sensitivity of 0.88 (95% CI 0.81, 0.93), 0.68 (95% CI 0.56, 0.78), and 0.80 (95% CI 0.73, 0.85) for the semi-structured, fully structured, and MINI reference standard categories, respectively, and a specificity of 0.79 (95% CI 0.74, 0.83), 0.85 (95% CI 0.78, 0.90), and 0.83 (95% CI 0.80, 0.86) for the semi-structured, fully structured, and MINI reference standard categories, respectively. While equivalence with a PHQ-9 cutoff of ≥ 10 was not established, we found the sensitivity of the PHQ-Dep-4 to be non-inferior to that of the PHQ-9, and the specificity of the PHQ-Dep-4 to be marginally smaller than the PHQ-9.
Published: 2021

11. Change in age distribution of COVID-19 deaths with the introduction of COVID-19 vaccination

Author: Pastorino, Roberta, Pezzullo, Angelo Maria, Villani, Leonardo, Causio, Francesco Andrea, Axfors, C, Contopoulos-Ioannidis, Dg, Boccia, Stefania, Ioannidis, Jpa, Pastorino R (ORCID:0000-0001-5013-0733), Pezzullo AM (ORCID:0000-0002-8252-4654), Villani L (ORCID:0000-0001-9375-8731), Causio FA, Boccia S (ORCID:0000-0002-1864-749X), Pastorino, Roberta, Pezzullo, Angelo Maria, Villani, Leonardo, Causio, Francesco Andrea, Axfors, C, Contopoulos-Ioannidis, Dg, Boccia, Stefania, Ioannidis, Jpa, Pastorino R (ORCID:0000-0001-5013-0733), Pezzullo AM (ORCID:0000-0002-8252-4654), Villani L (ORCID:0000-0001-9375-8731), Causio FA, and Boccia S (ORCID:0000-0002-1864-749X)
Abstract: Objectives: Most countries initially deployed COVID-19 vaccines preferentially in elderly populations. We aimed to evaluate whether population-level vaccine effectiveness is heralded by an increase in the relative proportion of deaths among non-elderly populations that were less covered by vaccination programs. Eligible data: We collected data from 40 countries on age-stratified COVID-19 deaths during the vaccination period (1/14/2021-5/31/2021) and two control periods (entire pre-vaccination period and excluding the first wave). Main outcome measures: We meta-analyzed the proportion of deaths in different age groups in vaccination versus control periods in (1) countries with low vaccination rates; (2) countries with age-independent vaccination policies; and (3) countries with standard age-dependent vaccination policies. Results: Countries that prioritized vaccination among older people saw an increasing share of deaths among 0-69 year old people in the vaccination versus the two control periods (summary proportion ratio 1.32 [95 CI% 1.24-1.41] and 1.35 [95 CI% 1.26-1.44)]. No such change was seen on average in countries with age-independent vaccination policies (1.05 [95 CI% 0.78-1.41 and 0.97 [95 CI% 0.95-1.00], respectively) and limited vaccination (0.93 [95 CI% 0.85-1.01] and 0.95 [95 CI% 0.87-1.03], respectively). Proportion ratios were associated with the difference of vaccination rates in elderly versus non-elderly people. No significant changes occurred in the share of deaths in age 0-49 among all 0-69 deaths in the vaccination versus pre-vaccination periods. Conclusions: The substantial shift in the age distribution of COVID-19 deaths in countries that rapidly implemented vaccination predominantly among elderly provides evidence for the population level-effectiveness of COVID-19 vaccination and a favorable evolution of the pandemic towards endemicity with fewer elderly deaths. Keywords: COVID-19; Death; Population data; Vaccination.
Published: 2021

12. Probability of Major Depression Classification Based on the SCID, CIDI, and MINI Diagnostic Interviews: A Synthesis of Three Individual Participant Data Meta-Analyses

Author: Wu Y, Levis B, Ioannidis JPA, Benedetti A, Thombs BD, and DEPRESsion Screening Data (DEPRESSD) Collaboration
Subjects: Psychiatry, 1701 Psychology
Abstract: INTRODUCTION:Three previous individual participant data meta-analyses (IPDMAs) reported that, compared to the Structured Clinical Interview for the DSM (SCID), alternative reference standards, primarily the Composite International Diagnostic Interview (CIDI) and the Mini International Neuropsychiatric Interview (MINI), tended to misclassify major depression status, when controlling for depression symptom severity. However, there was an important lack of precision in the results. OBJECTIVE:To compare the odds of the major depression classification based on the SCID, CIDI, and MINI. METHODS:We included and standardized data from 3 IPDMA databases. For each IPDMA, separately, we fitted binomial generalized linear mixed models to compare the adjusted odds ratios (aORs) of major depression classification, controlling for symptom severity and characteristics of participants, and the interaction between interview and symptom severity. Next, we synthesized results using a DerSimonian-Laird random-effects meta-analysis. RESULTS:In total, 69,405 participants (7,574 [11%] with major depression) from 212 studies were included. Controlling for symptom severity and participant characteristics, the MINI (74 studies; 25,749 participants) classified major depression more often than the SCID (108 studies; 21,953 participants; aOR 1.46; 95% confidence interval [CI] 1.11-1.92]). Classification odds for the CIDI (30 studies; 21,703 participants) and the SCID did not differ overall (aOR 1.19; 95% CI 0.79-1.75); however, as screening scores increased, the aOR increased less for the CIDI than the SCID (interaction aOR 0.64; 95% CI 0.52-0.80). CONCLUSIONS:Compared to the SCID, the MINI classified major depression more often. The odds of the depression classification with the CIDI increased less as symptom levels increased. Interpretation of research that uses diagnostic interviews to classify depression should consider the interview characteristics.
Published: 2020

13. Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies in medical imaging

Author: Nagendran, M, Chen, Y, Lovejoy, CA, Gordon, AC, Komorowski, M, Harvey, H, Topol, EJ, Ioannidis, JPA, Collins, GS, and Maruthappu, M
Abstract: Objective: To systematically examine the design, reporting standards, risk of bias, and claims of studies comparing the performance of diagnostic deep learning algorithms for medical imaging with that of expert clinicians. Design: Systematic review. Data sources: Medline, Embase, Cochrane Central Register of Controlled Trials, and the World Health Organization trial registry from 2010 to June 2019. Eligibility criteria for selecting studies: Randomised trial registrations and non-randomised studies comparing the performance of a deep learning algorithm in medical imaging with a contemporary group of one or more expert clinicians. Medical imaging has seen a growing interest in deep learning research. The main distinguishing feature of convolutional neural networks (CNNs) in deep learning is that when CNNs are fed with raw data, they develop their own representations needed for pattern recognition. The algorithm learns for itself the features of an image that are important for classification rather than being told by humans which features to use. The selected studies aimed to use medical imaging for predicting absolute risk of existing disease or classification into diagnostic groups (eg, disease or non-disease). For example, raw chest radiographs tagged with a label such as pneumothorax or no pneumothorax and the CNN learning which pixel patterns suggest pneumothorax. Review methods: Adherence to reporting standards was assessed by using CONSORT (consolidated standards of reporting trials) for randomised studies and TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) for non-randomised studies. Risk of bias was assessed by using the Cochrane risk of bias tool for randomised studies and PROBAST (prediction model risk of bias assessment tool) for non-randomised studies. Results: Only 10 records were found for deep learning randomised clinical trials, two of which have been published (with low risk of bias, except for lack of blinding, and high adherence to reporting standards) and eight are ongoing. Of 81 non-randomised clinical trials identified, only nine were prospective and just six were tested in a real world clinical setting. The median number of experts in the comparator group was only four (interquartile range 2-9). Full access to all datasets and code was severely limited (unavailable in 95% and 93% of studies, respectively). The overall risk of bias was high in 58 of 81 studies and adherence to reporting standards was suboptimal ( Conclusions: Few prospective deep learning studies and randomised trials exist in medical imaging. Most non-randomised trials are not prospective, are at high risk of bias, and deviate from existing reporting standards. Data and code availability are lacking in most studies, and human comparator groups are often small. Future studies should diminish risk of bias, enhance real world clinical relevance, improve reporting and transparency, and appropriately temper conclusions.
Published: 2020

14. The Accuracy of the Patient Health Questionnaire-9 Algorithm for Screening to Detect Major Depression: An Individual Participant Data Meta-Analysis

Author: He, C, Levis, B, Riehm, KE, Saadat, N, Levis, AW, Azar, M, Rice, DB, Krishnan, A, Wu, Y, Sun, Y, Imran, M, Boruff, J, Cuijpers, P, Gilbody, S, Ioannidis, JPA, Kloda, LA, McMillan, D, Patten, SB, Shrier, I, Ziegelstein, RC, Turner, Alyna, He, C, Levis, B, Riehm, KE, Saadat, N, Levis, AW, Azar, M, Rice, DB, Krishnan, A, Wu, Y, Sun, Y, Imran, M, Boruff, J, Cuijpers, P, Gilbody, S, Ioannidis, JPA, Kloda, LA, McMillan, D, Patten, SB, Shrier, I, Ziegelstein, RC, and Turner, Alyna
Published: 2020

15. Probability of major depression diagnostic classification based on the SCID, CIDI and MINI diagnostic interviews controlling for Hospital Anxiety and Depression Scale - Depression subscale scores: An individual participant data meta-analysis of 73 primary studies

Author: Wu, Y, Levis, B, Sun, Y, Krishnan, A, He, C, Riehm, KE, Rice, DB, Azar, M, Yan, XW, Neupane, D, Bhandari, PM, Imran, M, Chiovitti, MJ, Saadat, N, Boruff, JT, Cuijpers, P, Gilbody, S, McMillan, D, Ioannidis, JPA, Kloda, LA, Patten, SB, Shrier, I, Ziegelstein, RC, Henry, M, Ismail, Z, Loiselle, CG, Mitchell, ND, Tonelli, M, Al-Adawi, S, Beraldi, A, Braeken, APBM, Bueel-Drabe, N, Bunevicius, A, Carter, G, Chen, C-K, Cheung, G, Clover, K, Conroy, RM, Cukor, D, Rocha e Silva, CE, Dabscheck, E, Daray, FM, Douven, E, Downing, MG, Feinstein, A, Ferentinos, PP, Fischer, FH, Flint, AJ, Fujimori, M, Gallagher, P, Gandy, M, Goebel, S, Grassi, L, Haerter, M, Jenewein, J, Jette, N, Juliao, M, Kim, J-M, Kim, S-W, Kjaergaard, M, Kohler, S, Loosman, WL, Loewe, B, Martin-Santos, R, Massardo, L, Matsuoka, Y, Mehnert, A, Michopoulos, I, Misery, L, Navines, R, O'Donnell, ML, Ozturk, A, Peceliuniene, J, Pintor, L, Ponsford, JL, Quinn, TJ, Reme, SE, Reuter, K, Rooney, AG, Sanchez-Gonzalez, R, Schwarzbold, ML, Cankorur, VS, Shaaban, J, Sharpe, L, Sharpe, M, Simard, S, Singer, S, Stafford, L, Stone, J, Sultan, S, Teixeira, AL, Tiringer, I, Turner, A, Walker, J, Walterfang, M, Wang, L-J, White, J, Wong, DK, Benedetti, A, Thombs, BD, Wu, Y, Levis, B, Sun, Y, Krishnan, A, He, C, Riehm, KE, Rice, DB, Azar, M, Yan, XW, Neupane, D, Bhandari, PM, Imran, M, Chiovitti, MJ, Saadat, N, Boruff, JT, Cuijpers, P, Gilbody, S, McMillan, D, Ioannidis, JPA, Kloda, LA, Patten, SB, Shrier, I, Ziegelstein, RC, Henry, M, Ismail, Z, Loiselle, CG, Mitchell, ND, Tonelli, M, Al-Adawi, S, Beraldi, A, Braeken, APBM, Bueel-Drabe, N, Bunevicius, A, Carter, G, Chen, C-K, Cheung, G, Clover, K, Conroy, RM, Cukor, D, Rocha e Silva, CE, Dabscheck, E, Daray, FM, Douven, E, Downing, MG, Feinstein, A, Ferentinos, PP, Fischer, FH, Flint, AJ, Fujimori, M, Gallagher, P, Gandy, M, Goebel, S, Grassi, L, Haerter, M, Jenewein, J, Jette, N, Juliao, M, Kim, J-M, Kim, S-W, Kjaergaard, M, Kohler, S, Loosman, WL, Loewe, B, Martin-Santos, R, Massardo, L, Matsuoka, Y, Mehnert, A, Michopoulos, I, Misery, L, Navines, R, O'Donnell, ML, Ozturk, A, Peceliuniene, J, Pintor, L, Ponsford, JL, Quinn, TJ, Reme, SE, Reuter, K, Rooney, AG, Sanchez-Gonzalez, R, Schwarzbold, ML, Cankorur, VS, Shaaban, J, Sharpe, L, Sharpe, M, Simard, S, Singer, S, Stafford, L, Stone, J, Sultan, S, Teixeira, AL, Tiringer, I, Turner, A, Walker, J, Walterfang, M, Wang, L-J, White, J, Wong, DK, Benedetti, A, and Thombs, BD
Abstract: OBJECTIVE: Two previous individual participant data meta-analyses (IPDMAs) found that different diagnostic interviews classify different proportions of people as having major depression overall or by symptom levels. We compared the odds of major depression classification across diagnostic interviews among studies that administered the Depression subscale of the Hospital Anxiety and Depression Scale (HADS-D). METHODS: Data accrued for an IPDMA on HADS-D diagnostic accuracy were analysed. We fit binomial generalized linear mixed models to compare odds of major depression classification for the Structured Clinical Interview for DSM (SCID), Composite International Diagnostic Interview (CIDI), and Mini International Neuropsychiatric Interview (MINI), controlling for HADS-D scores and participant characteristics with and without an interaction term between interview and HADS-D scores. RESULTS: There were 15,856 participants (1942 [12%] with major depression) from 73 studies, including 15,335 (97%) non-psychiatric medical patients, 164 (1%) partners of medical patients, and 357 (2%) healthy adults. The MINI (27 studies, 7345 participants, 1066 major depression cases) classified participants as having major depression more often than the CIDI (10 studies, 3023 participants, 269 cases) (adjusted odds ratio [aOR] = 1.70 (0.84, 3.43)) and the semi-structured SCID (36 studies, 5488 participants, 607 cases) (aOR = 1.52 (1.01, 2.30)). The odds ratio for major depression classification with the CIDI was less likely to increase as HADS-D scores increased than for the SCID (interaction aOR = 0.92 (0.88, 0.96)). CONCLUSION: Compared to the SCID, the MINI may diagnose more participants as having major depression, and the CIDI may be less responsive to symptom severity.
Published: 2020

16. Depression prevalence using the HADS-D compared to SCID major depression classification: An individual participant data meta-analysis

Author: Brehaut, E, Neupane, D, Levis, B, Wu, Y, Sun, Y, Krishnan, A, He, C, Bhandari, PM, Negeri, Z, Riehm, KE, Rice, DB, Azar, M, Yan, XW, Imran, M, Chiovitti, MJ, Saadat, N, Cuijpers, P, Ioannidis, JPA, Markham, S, Patten, SB, Ziegelstein, RC, Henry, M, Ismail, Z, Loiselle, CG, Mitchell, ND, Tonelli, M, Boruff, JT, Kloda, LA, Beraldi, A, Braeken, APBM, Carter, G, Clover, K, Conroy, RM, Cukor, D, da Rocha E Silva, CE, De Souza, J, Downing, MG, Feinstein, A, Ferentinos, PP, Fischer, FH, Flint, AJ, Fujimori, M, Gallagher, P, Goebel, S, Jette, N, Juliao, M, Keller, M, Kjaergaard, M, Love, AW, Loewe, B, Martin-Santos, R, Michopoulos, I, Navines, R, O'Rourke, SJ, Ozturk, A, Pintor, L, Ponsford, JL, Rooney, AG, Sanchez-Gonzalez, R, Schwarzbold, ML, Sharpe, M, Simard, S, Singer, S, Stone, J, Tung, K-Y, Turner, A, Walker, J, Walterfang, M, White, J, Benedetti, A, Thombs, BD, Brehaut, E, Neupane, D, Levis, B, Wu, Y, Sun, Y, Krishnan, A, He, C, Bhandari, PM, Negeri, Z, Riehm, KE, Rice, DB, Azar, M, Yan, XW, Imran, M, Chiovitti, MJ, Saadat, N, Cuijpers, P, Ioannidis, JPA, Markham, S, Patten, SB, Ziegelstein, RC, Henry, M, Ismail, Z, Loiselle, CG, Mitchell, ND, Tonelli, M, Boruff, JT, Kloda, LA, Beraldi, A, Braeken, APBM, Carter, G, Clover, K, Conroy, RM, Cukor, D, da Rocha E Silva, CE, De Souza, J, Downing, MG, Feinstein, A, Ferentinos, PP, Fischer, FH, Flint, AJ, Fujimori, M, Gallagher, P, Goebel, S, Jette, N, Juliao, M, Keller, M, Kjaergaard, M, Love, AW, Loewe, B, Martin-Santos, R, Michopoulos, I, Navines, R, O'Rourke, SJ, Ozturk, A, Pintor, L, Ponsford, JL, Rooney, AG, Sanchez-Gonzalez, R, Schwarzbold, ML, Sharpe, M, Simard, S, Singer, S, Stone, J, Tung, K-Y, Turner, A, Walker, J, Walterfang, M, White, J, Benedetti, A, and Thombs, BD
Abstract: OBJECTIVES: Validated diagnostic interviews are required to classify depression status and estimate prevalence of disorder, but screening tools are often used instead. We used individual participant data meta-analysis to compare prevalence based on standard Hospital Anxiety and Depression Scale - depression subscale (HADS-D) cutoffs of ≥8 and ≥11 versus Structured Clinical Interview for DSM (SCID) major depression and determined if an alternative HADS-D cutoff could more accurately estimate prevalence. METHODS: We searched Medline, Medline In-Process & Other Non-Indexed Citations via Ovid, PsycINFO, and Web of Science (inception-July 11, 2016) for studies comparing HADS-D scores to SCID major depression status. Pooled prevalence and pooled differences in prevalence for HADS-D cutoffs versus SCID major depression were estimated. RESULTS: 6005 participants (689 SCID major depression cases) from 41 primary studies were included. Pooled prevalence was 24.5% (95% Confidence Interval (CI): 20.5%, 29.0%) for HADS-D ≥8, 10.7% (95% CI: 8.3%, 13.8%) for HADS-D ≥11, and 11.6% (95% CI: 9.2%, 14.6%) for SCID major depression. HADS-D ≥11 was closest to SCID major depression prevalence, but the 95% prediction interval for the difference that could be expected for HADS-D ≥11 versus SCID in a new study was -21.1% to 19.5%. CONCLUSIONS: HADS-D ≥8 substantially overestimates depression prevalence. Of all possible cutoff thresholds, HADS-D ≥11 was closest to the SCID, but there was substantial heterogeneity in the difference between HADS-D ≥11 and SCID-based estimates. HADS-D should not be used as a substitute for a validated diagnostic interview.
Published: 2020

17. Equivalency of the diagnostic accuracy of the PHQ-8 and PHQ-9: a systematic review and individual participant data meta-analysis.

Author: Wu, Y, Levis, B, Riehm, KE, Saadat, N, Levis, AW, Azar, M, Rice, DB, Boruff, J, Cuijpers, P, Gilbody, S, Ioannidis, JPA, Kloda, LA, McMillan, D, Patten, SB, Shrier, I, Ziegelstein, RC, Akena, DH, Arroll, B, Ayalon, L, Baradaran, HR, Baron, M, Bombardier, CH, Butterworth, P, Carter, G, Chagas, MH, Chan, JCN, Cholera, R, Conwell, Y, de Man-van Ginkel, JM, Fann, JR, Fischer, FH, Fung, D, Gelaye, B, Goodyear-Smith, F, Greeno, CG, Hall, BJ, Harrison, PA, Härter, M, Hegerl, U, Hides, L, Hobfoll, SE, Hudson, M, Hyphantis, T, Inagaki, MD, Jetté, N, Khamseh, ME, Kiely, KM, Kwan, Y, Lamers, F, Liu, S-I, Lotrakul, M, Loureiro, SR, Löwe, B, McGuire, A, Mohd-Sidik, S, Munhoz, TN, Muramatsu, K, Osório, FL, Patel, V, Pence, BW, Persoons, P, Picardi, A, Reuter, K, Rooney, AG, Santos, IS, Shaaban, J, Sidebottom, A, Simning, A, Stafford, MD, Sung, S, Tan, PLL, Turner, A, van Weert, HC, White, J, Whooley, MA, Winkley, K, Yamada, M, Benedetti, A, Thombs, BD, Wu, Y, Levis, B, Riehm, KE, Saadat, N, Levis, AW, Azar, M, Rice, DB, Boruff, J, Cuijpers, P, Gilbody, S, Ioannidis, JPA, Kloda, LA, McMillan, D, Patten, SB, Shrier, I, Ziegelstein, RC, Akena, DH, Arroll, B, Ayalon, L, Baradaran, HR, Baron, M, Bombardier, CH, Butterworth, P, Carter, G, Chagas, MH, Chan, JCN, Cholera, R, Conwell, Y, de Man-van Ginkel, JM, Fann, JR, Fischer, FH, Fung, D, Gelaye, B, Goodyear-Smith, F, Greeno, CG, Hall, BJ, Harrison, PA, Härter, M, Hegerl, U, Hides, L, Hobfoll, SE, Hudson, M, Hyphantis, T, Inagaki, MD, Jetté, N, Khamseh, ME, Kiely, KM, Kwan, Y, Lamers, F, Liu, S-I, Lotrakul, M, Loureiro, SR, Löwe, B, McGuire, A, Mohd-Sidik, S, Munhoz, TN, Muramatsu, K, Osório, FL, Patel, V, Pence, BW, Persoons, P, Picardi, A, Reuter, K, Rooney, AG, Santos, IS, Shaaban, J, Sidebottom, A, Simning, A, Stafford, MD, Sung, S, Tan, PLL, Turner, A, van Weert, HC, White, J, Whooley, MA, Winkley, K, Yamada, M, Benedetti, A, and Thombs, BD
Abstract: BACKGROUND: Item 9 of the Patient Health Questionnaire-9 (PHQ-9) queries about thoughts of death and self-harm, but not suicidality. Although it is sometimes used to assess suicide risk, most positive responses are not associated with suicidality. The PHQ-8, which omits Item 9, is thus increasingly used in research. We assessed equivalency of total score correlations and the diagnostic accuracy to detect major depression of the PHQ-8 and PHQ-9. METHODS: We conducted an individual patient data meta-analysis. We fit bivariate random-effects models to assess diagnostic accuracy. RESULTS: 16 742 participants (2097 major depression cases) from 54 studies were included. The correlation between PHQ-8 and PHQ-9 scores was 0.996 (95% confidence interval 0.996 to 0.996). The standard cutoff score of 10 for the PHQ-9 maximized sensitivity + specificity for the PHQ-8 among studies that used a semi-structured diagnostic interview reference standard (N = 27). At cutoff 10, the PHQ-8 was less sensitive by 0.02 (-0.06 to 0.00) and more specific by 0.01 (0.00 to 0.01) among those studies (N = 27), with similar results for studies that used other types of interviews (N = 27). For all 54 primary studies combined, across all cutoffs, the PHQ-8 was less sensitive than the PHQ-9 by 0.00 to 0.05 (0.03 at cutoff 10), and specificity was within 0.01 for all cutoffs (0.00 to 0.01). CONCLUSIONS: PHQ-8 and PHQ-9 total scores were similar. Sensitivity may be minimally reduced with the PHQ-8, but specificity is similar.
Published: 2020

18. Estimating the sample mean and standard deviation from commonly reported quantiles in meta-analysis

Author: McGrath, S, Zhao, X, Steele, R, Thombs, BD, Benedetti, A, Levis, B, Riehm, KE, Saadat, N, Levis, AW, Azar, M, Rice, DB, Sun, Y, Krishnan, A, He, C, Wu, Y, Bhandari, PM, Neupane, D, Imran, M, Boruff, J, Cuijpers, P, Gilbody, S, Ioannidis, JPA, Kloda, LA, McMillan, D, Patten, SB, Shrier, I, Ziegelstein, RC, Akena, DH, Arroll, B, Ayalon, L, Baradaran, HR, Baron, M, Beraldi, A, Bombardier, CH, Butterworth, P, Carter, G, Chagas, MH, Chan, JCN, Cholera, R, Chowdhary, N, Clover, K, Conwell, Y, Ginkel, JMDM-V, Delgadillo, J, Fann, JR, Fischer, FH, Fischler, B, Fung, D, Gelaye, B, Goodyear-Smith, F, Greeno, CG, Hall, BJ, Harrison, PA, Harter, M, Hegerl, U, Hides, L, Hobfoll, SE, Hudson, M, Hyphantis, T, Inagaki, M, Ismail, K, Jette, N, Khamseh, ME, Kiely, KM, Kwan, Y, Lamers, F, Liu, S-I, Lotrakul, M, Loureiro, SR, Loewe, B, Marsh, L, McGuire, A, Sidik, SM, Munhoz, TN, Muramatsu, K, Osorio, FL, Patel, V, Pence, BW, Persoons, P, Picardi, A, Reuter, K, Rooney, AG, Santos, IS, Shaaban, J, Sidebottom, A, Simning, A, Stafford, L, Sung, SC, Tan, PLL, Turner, A, van der Feltz-Cornelis, CM, van Weert, HC, Vohringer, PA, White, J, Whooley, MA, Winkley, K, Yamada, M, Zhang, Y, McGrath, S, Zhao, X, Steele, R, Thombs, BD, Benedetti, A, Levis, B, Riehm, KE, Saadat, N, Levis, AW, Azar, M, Rice, DB, Sun, Y, Krishnan, A, He, C, Wu, Y, Bhandari, PM, Neupane, D, Imran, M, Boruff, J, Cuijpers, P, Gilbody, S, Ioannidis, JPA, Kloda, LA, McMillan, D, Patten, SB, Shrier, I, Ziegelstein, RC, Akena, DH, Arroll, B, Ayalon, L, Baradaran, HR, Baron, M, Beraldi, A, Bombardier, CH, Butterworth, P, Carter, G, Chagas, MH, Chan, JCN, Cholera, R, Chowdhary, N, Clover, K, Conwell, Y, Ginkel, JMDM-V, Delgadillo, J, Fann, JR, Fischer, FH, Fischler, B, Fung, D, Gelaye, B, Goodyear-Smith, F, Greeno, CG, Hall, BJ, Harrison, PA, Harter, M, Hegerl, U, Hides, L, Hobfoll, SE, Hudson, M, Hyphantis, T, Inagaki, M, Ismail, K, Jette, N, Khamseh, ME, Kiely, KM, Kwan, Y, Lamers, F, Liu, S-I, Lotrakul, M, Loureiro, SR, Loewe, B, Marsh, L, McGuire, A, Sidik, SM, Munhoz, TN, Muramatsu, K, Osorio, FL, Patel, V, Pence, BW, Persoons, P, Picardi, A, Reuter, K, Rooney, AG, Santos, IS, Shaaban, J, Sidebottom, A, Simning, A, Stafford, L, Sung, SC, Tan, PLL, Turner, A, van der Feltz-Cornelis, CM, van Weert, HC, Vohringer, PA, White, J, Whooley, MA, Winkley, K, Yamada, M, and Zhang, Y
Abstract: Researchers increasingly use meta-analysis to synthesize the results of several studies in order to estimate a common effect. When the outcome variable is continuous, standard meta-analytic approaches assume that the primary studies report the sample mean and standard deviation of the outcome. However, when the outcome is skewed, authors sometimes summarize the data by reporting the sample median and one or both of (i) the minimum and maximum values and (ii) the first and third quartiles, but do not report the mean or standard deviation. To include these studies in meta-analysis, several methods have been developed to estimate the sample mean and standard deviation from the reported summary data. A major limitation of these widely used methods is that they assume that the outcome distribution is normal, which is unlikely to be tenable for studies reporting medians. We propose two novel approaches to estimate the sample mean and standard deviation when data are suspected to be non-normal. Our simulation results and empirical assessments show that the proposed methods often perform better than the existing methods when applied to non-normal data.
Published: 2020

19. Patient Health Questionnaire-9 scores do not accurately estimate depression prevalence: individual participant data meta-analysis.

Author: Levis, B, Benedetti, A, Ioannidis, JPA, Sun, Y, Negeri, Z, He, C, Wu, Y, Krishnan, A, Bhandari, PM, Neupane, D, Imran, M, Rice, DB, Riehm, KE, Saadat, N, Azar, M, Boruff, J, Cuijpers, P, Gilbody, S, Kloda, LA, McMillan, D, Patten, SB, Shrier, I, Ziegelstein, RC, Alamri, SH, Amtmann, D, Ayalon, L, Baradaran, HR, Beraldi, A, Bernstein, CN, Bhana, A, Bombardier, CH, Carter, G, Chagas, MH, Chibanda, D, Clover, K, Conwell, Y, Diez-Quevedo, C, Fann, JR, Fischer, FH, Gholizadeh, L, Gibson, LJ, Green, EP, Greeno, CG, Hall, BJ, Haroz, EE, Ismail, K, Jetté, N, Khamseh, ME, Kwan, Y, Lara, MA, Liu, S-I, Loureiro, SR, Löwe, B, Marrie, RA, Marsh, L, McGuire, A, Muramatsu, K, Navarrete, L, Osório, FL, Petersen, I, Picardi, A, Pugh, SL, Quinn, TJ, Rooney, AG, Shinn, EH, Sidebottom, A, Spangenberg, L, Tan, PLL, Taylor-Rowan, M, Turner, A, van Weert, HC, Vöhringer, PA, Wagner, LI, White, J, Winkley, K, Thombs, BD, Levis, B, Benedetti, A, Ioannidis, JPA, Sun, Y, Negeri, Z, He, C, Wu, Y, Krishnan, A, Bhandari, PM, Neupane, D, Imran, M, Rice, DB, Riehm, KE, Saadat, N, Azar, M, Boruff, J, Cuijpers, P, Gilbody, S, Kloda, LA, McMillan, D, Patten, SB, Shrier, I, Ziegelstein, RC, Alamri, SH, Amtmann, D, Ayalon, L, Baradaran, HR, Beraldi, A, Bernstein, CN, Bhana, A, Bombardier, CH, Carter, G, Chagas, MH, Chibanda, D, Clover, K, Conwell, Y, Diez-Quevedo, C, Fann, JR, Fischer, FH, Gholizadeh, L, Gibson, LJ, Green, EP, Greeno, CG, Hall, BJ, Haroz, EE, Ismail, K, Jetté, N, Khamseh, ME, Kwan, Y, Lara, MA, Liu, S-I, Loureiro, SR, Löwe, B, Marrie, RA, Marsh, L, McGuire, A, Muramatsu, K, Navarrete, L, Osório, FL, Petersen, I, Picardi, A, Pugh, SL, Quinn, TJ, Rooney, AG, Shinn, EH, Sidebottom, A, Spangenberg, L, Tan, PLL, Taylor-Rowan, M, Turner, A, van Weert, HC, Vöhringer, PA, Wagner, LI, White, J, Winkley, K, and Thombs, BD
Abstract: OBJECTIVES: Depression symptom questionnaires are not for diagnostic classification. Patient Health Questionnaire-9 (PHQ-9) scores ≥10 are nonetheless often used to estimate depression prevalence. We compared PHQ-9 ≥10 prevalence to Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders (SCID) major depression prevalence and assessed whether an alternative PHQ-9 cutoff could more accurately estimate prevalence. STUDY DESIGN AND SETTING: Individual participant data meta-analysis of datasets comparing PHQ-9 scores to SCID major depression status. RESULTS: A total of 9,242 participants (1,389 SCID major depression cases) from 44 primary studies were included. Pooled PHQ-9 ≥10 prevalence was 24.6% (95% confidence interval [CI]: 20.8%, 28.9%); pooled SCID major depression prevalence was 12.1% (95% CI: 9.6%, 15.2%); and pooled difference was 11.9% (95% CI: 9.3%, 14.6%). The mean study-level PHQ-9 ≥10 to SCID-based prevalence ratio was 2.5 times. PHQ-9 ≥14 and the PHQ-9 diagnostic algorithm provided prevalence closest to SCID major depression prevalence, but study-level prevalence differed from SCID-based prevalence by an average absolute difference of 4.8% for PHQ-9 ≥14 (95% prediction interval: -13.6%, 14.5%) and 5.6% for the PHQ-9 diagnostic algorithm (95% prediction interval: -16.4%, 15.0%). CONCLUSION: PHQ-9 ≥10 substantially overestimates depression prevalence. There is too much heterogeneity to correct statistically in individual studies.
Published: 2020

20. Forecasting for COVID-19 has failed

Author: Ioannidis, JPA, Cripps, S, Tanner, MA, Ioannidis, JPA, Cripps, S, and Tanner, MA
Abstract: Epidemic forecasting has a dubious track-record, and its failures became more prominent with COVID-19. Poor data input, wrong modeling assumptions, high sensitivity of estimates, lack of incorporation of epidemiological features, poor past evidence on effects of available interventions, lack of transparency, errors, lack of determinacy, consideration of only one or a few dimensions of the problem at hand, lack of expertise in crucial disciplines, groupthink and bandwagon effects, and selective reporting are some of the causes of these failures. Nevertheless, epidemic forecasting is unlikely to be abandoned. Some (but not all) of these problems can be fixed. Careful modeling of predictive distributions rather than focusing on point estimates, considering multiple dimensions of impact, and continuously reappraising models based on their validated performance may help. If extreme values are considered, extremes should be considered for the consequences of multiple dimensions of impact so as to continuously calibrate predictive insights and decision-making. When major decisions (e.g. draconian lockdowns) are based on forecasts, the harms (in terms of health, economy, and society at large) and the asymmetry of risks need to be approached in a holistic fashion, considering the totality of the evidence.
Published: 2020

21. Data access: Toward unrestricted use of public genomic data

Author: Amann, RI, Baichoo, S, Blencowe, BJ, Bork, P, Borodovsky, M, Brooksbank, C, Chain, PSG, Colwell, RR, Daffonchio, DG, Danchin, A, De Lorenzo, V, Dorrestein, PC, Finn, RD, Fraser, CM, Gilbert, JA, Hallam, SJ, Hugenholtz, P, Ioannidis, JPA, Jansson, JK, Kim, JF, Klenk, HP, Klotz, MG, Knight, R, Konstantinidis, KT, Kyrpides, NC, Mason, CE, McHardy, AC, Meyer, F, Ouzounis, CA, Patrinos, AAN, Podar, M, Pollard, KS, Ravel, J, Muñoz, AR, Roberts, RJ, Rosselló-Móra, R, Sansone, SA, Schloss, PD, Schriml, LM, Setubal, JC, Sorek, R, Stevens, RL, Tiedje, JM, Turjanski, A, Tyson, GW, Ussery, DW, Weinstock, GM, White, O, Whitman, WB, and Xenarios, I
Subjects: General Science & Technology, MD Multidisciplinary
Published: 2019

22. Comparison of major depression diagnostic classification probability using the SCID, CIDI, and MINI diagnostic interviews among women in pregnancy or postpartum: An individual participant data meta-analysis

Author: Levis, B, McMillan, D, Sun, Y, He, C, Rice, DB, Krishnan, A, Wu, Y, Azar, M, Sanchez, TA, Chiovitti, MJ, Bhandari, PM, Neupane, D, Saadat, N, Riehm, KE, Imran, M, Boruff, JT, Cuijpers, P, Gilbody, S, Ioannidis, JPA, Kloda, LA, Patten, SB, Shrier, I, Ziegelstein, RC, Comeau, L, Mitchell, ND, Tonelli, M, Vigod, SN, Aceti, F, Alvarado, R, Alvarado-Esquivel, C, Bakare, MO, Barnes, J, Beck, CT, Bindt, C, Boyce, PM, Bunevicius, A, Couto, TCE, Chaudron, LH, Correa, H, de Figueiredo, FP, Eapen, V, Fernandes, M, Figueiredo, B, Fisher, JRW, Garcia-Esteve, L, Giardinelli, L, Helle, N, Howard, LM, Khalifa, DS, Kohlhoff, J, Kusminskas, L, Kozinszky, Z, Lelli, L, Leonardou, AA, Lewis, BA, Maes, M, Meuti, V, Nakić Radoš, S, Navarro García, P, Nishi, D, Okitundu Luwa E-Andjafono, D, Robertson-Blackmore, E, Rochat, TJ, Rowe, HJ, Siu, BWM, Skalkidou, A, Stein, A, Stewart, RC, Su, KP, Sundström-Poromaa, I, Tadinac, M, Tandon, SD, Tendais, I, Thiagayson, P, Töreki, A, Torres-Giménez, A, Tran, TD, Trevillion, K, Turner, K, Vega-Dienstmaier, JM, Wynter, K, Yonkers, KA, Benedetti, A, Thombs, BD, Levis, B, McMillan, D, Sun, Y, He, C, Rice, DB, Krishnan, A, Wu, Y, Azar, M, Sanchez, TA, Chiovitti, MJ, Bhandari, PM, Neupane, D, Saadat, N, Riehm, KE, Imran, M, Boruff, JT, Cuijpers, P, Gilbody, S, Ioannidis, JPA, Kloda, LA, Patten, SB, Shrier, I, Ziegelstein, RC, Comeau, L, Mitchell, ND, Tonelli, M, Vigod, SN, Aceti, F, Alvarado, R, Alvarado-Esquivel, C, Bakare, MO, Barnes, J, Beck, CT, Bindt, C, Boyce, PM, Bunevicius, A, Couto, TCE, Chaudron, LH, Correa, H, de Figueiredo, FP, Eapen, V, Fernandes, M, Figueiredo, B, Fisher, JRW, Garcia-Esteve, L, Giardinelli, L, Helle, N, Howard, LM, Khalifa, DS, Kohlhoff, J, Kusminskas, L, Kozinszky, Z, Lelli, L, Leonardou, AA, Lewis, BA, Maes, M, Meuti, V, Nakić Radoš, S, Navarro García, P, Nishi, D, Okitundu Luwa E-Andjafono, D, Robertson-Blackmore, E, Rochat, TJ, Rowe, HJ, Siu, BWM, Skalkidou, A, Stein, A, Stewart, RC, Su, KP, Sundström-Poromaa, I, Tadinac, M, Tandon, SD, Tendais, I, Thiagayson, P, Töreki, A, Torres-Giménez, A, Tran, TD, Trevillion, K, Turner, K, Vega-Dienstmaier, JM, Wynter, K, Yonkers, KA, Benedetti, A, and Thombs, BD
Abstract: Objectives: A previous individual participant data meta-analysis (IPDMA) identified differences in major depression classification rates between different diagnostic interviews, controlling for depressive symptoms on the basis of the Patient Health Questionnaire-9. We aimed to determine whether similar results would be seen in a different population, using studies that administered the Edinburgh Postnatal Depression Scale (EPDS) in pregnancy or postpartum. Methods: Data accrued for an EPDS diagnostic accuracy IPDMA were analysed. Binomial generalised linear mixed models were fit to compare depression classification odds for the Mini International Neuropsychiatric Interview (MINI), Composite International Diagnostic Interview (CIDI), and Structured Clinical Interview for DSM (SCID), controlling for EPDS scores and participant characteristics. Results: Among fully structured interviews, the MINI (15 studies, 2,532 participants, 342 major depression cases) classified depression more often than the CIDI (3 studies, 2,948 participants, 194 major depression cases; adjusted odds ratio [aOR] = 3.72, 95% confidence interval [CI] [1.21, 11.43]). Compared with the semistructured SCID (28 studies, 7,403 participants, 1,027 major depression cases), odds with the CIDI (interaction aOR = 0.88, 95% CI [0.85, 0.92]) and MINI (interaction aOR = 0.95, 95% CI [0.92, 0.99]) increased less as EPDS scores increased. Conclusion: Different interviews may not classify major depression equivalently.
Published: 2019

23. Shortening self-report mental health symptom measures through optimal test assembly methods: Development and validation of the Patient Health Questionnaire-Depression-4

Author: Ishihara, M, Harel, D, Levis, B, Levis, AW, Riehm, KE, Saadat, N, Azar, M, Rice, DB, Sanchez, TA, Chiovitti, MJ, Cuijpers, P, Gilbody, S, Ioannidis, JPA, Kloda, LA, McMillan, D, Patten, SB, Shrier, I, Arroll, B, Bombardier, CH, Butterworth, P, Carter, G, Clover, K, Conwell, Y, Goodyear-Smith, F, Greeno, CG, Hambridge, J, Harrison, PA, Hudson, M, Jetté, N, Kiely, KM, McGuire, A, Pence, BW, Rooney, AG, Sidebottom, A, Simning, A, Turner, A, White, J, Whooley, MA, Winkley, K, Benedetti, A, Thombs, BD, Ishihara, M, Harel, D, Levis, B, Levis, AW, Riehm, KE, Saadat, N, Azar, M, Rice, DB, Sanchez, TA, Chiovitti, MJ, Cuijpers, P, Gilbody, S, Ioannidis, JPA, Kloda, LA, McMillan, D, Patten, SB, Shrier, I, Arroll, B, Bombardier, CH, Butterworth, P, Carter, G, Clover, K, Conwell, Y, Goodyear-Smith, F, Greeno, CG, Hambridge, J, Harrison, PA, Hudson, M, Jetté, N, Kiely, KM, McGuire, A, Pence, BW, Rooney, AG, Sidebottom, A, Simning, A, Turner, A, White, J, Whooley, MA, Winkley, K, Benedetti, A, and Thombs, BD
Abstract: Background: The objective of this study was to develop and validate a short form of the Patient Health Questionnaire-9 (PHQ-9), a self-report questionnaire for assessing depressive symptomatology, using objective criteria. Methods: Responses on the PHQ-9 were obtained from 7,850 English-speaking participants enrolled in 20 primary diagnostic test accuracy studies. PHQ unidimensionality was verified using confirmatory factor analysis, and an item response theory model was fit. Optimal test assembly (OTA) methods identified a maximally precise short form for each possible length between one and eight items, including and excluding the ninth item. The final short form was selected based on prespecified validity, reliability, and diagnostic accuracy criteria. Results: A four-item short form of the PHQ (PHQ-Dep-4) was selected. The PHQ-Dep-4 had a Cronbach's alpha of 0.805. Sensitivity and specificity of the PHQ-Dep-4 were 0.788 and 0.837, respectively, and were statistically equivalent to the PHQ-9 (sensitivity = 0.761, specificity = 0.866). The correlation of total scores with the full PHQ-9 was high (r = 0.919). Conclusion: The PHQ-Dep-4 is a valid short form with minimal loss of information of scores when compared to the full-length PHQ-9. Although OTA methods have been used to shorten patient-reported outcome measures based on objective, prespecified criteria, further studies are required to validate this general procedure for broader use in health research. Furthermore, due to unexamined heterogeneity, there is a need to replicate the results of this study in different patient populations.
Published: 2019

24. Mother-to-child transmission of HIV: developing integration of healthcare programmes with clinical, social and basic research studies Report of the International Workshop held at Chobe Marina Lodge, Kasane, Botswana, 21–25 January 2003*

Author: Menu, E, Scarlatti, G, Barré-Sinoussi, F, Gray, G, Bollinger, B, Ioannidis, JPA, Miotti, P, and Osborne, C
Published: 2003

25. Machine learning and artificial intelligence research for patient benefit: 20 critical questions on transparency, replicability, ethics, and effectiveness

Author: Harry Hemingway, Rayid Ghani, Pall Jonsson, Franz J. Király, Moons Kgm., Christopher Holmes, Richard Branson, Sebastian J. Vollmer, McAllister Ksl., Gergo Bohner, Gary S. Collins, Bilal A. Mateen, A Jonas, Mark Birse, Ioannidis Jpa., Puja R. Myles, David Granger, and Sarah Cumbers
Subjects: Research groups, Best practice, MEDLINE, 030204 cardiovascular system & hematology, Machine learning, computer.software_genre, Machine Learning, 03 medical and health sciences, 0302 clinical medicine, Artificial Intelligence, Surveys and Questionnaires, Humans, 030212 general & internal medicine, Data collection, business.industry, Data Collection, Reproducibility of Results, General Medicine, Transparency (behavior), Patient benefit, Ethical concerns, Artificial intelligence, business, Psychology, computer, Algorithms
Abstract: Machine learning, artificial intelligence, and other modern statistical methods are providing new opportunities to operationalise previously untapped and rapidly growing sources of data for patient benefit. Despite much promising research currently being undertaken, particularly in imaging, the literature as a whole lacks transparency, clear reporting to facilitate replicability, exploration for potential ethical concerns, and clear demonstrations of effectiveness. Among the many reasons why these problems exist, one of the most important (for which we provide a preliminary solution here) is the current lack of best practice guidance specific to machine learning and artificial intelligence. However, we believe that interdisciplinary groups pursuing research and impact projects involving machine learning and artificial intelligence for health would benefit from explicitly addressing a series of questions concerning transparency, reproducibility, ethics, and effectiveness (TREE). The 20 critical questions proposed here provide a framework for research groups to inform the design, conduct, and reporting; for editors and peer reviewers to evaluate contributions to the literature; and for patients, clinicians and policy makers to critically appraise where new findings may deliver patient benefit.
Published: 2020

26. Redefine statistical significance

Author: Benjamin, D, Berger, J, Johannesson, M, Nosek, B, Wagenmakers, E, Berk, R, Bollen, K, Brembs, B, Brown, L, Camerer, C, Cesarini, D, Chambers, C, Clyde, M, Cook, T, De Boeck, P, Dienes, Z, Dreber, A, Easwaran, K, Efferson, C, Fehr, E, Fidler, F, Field, A, Forster, M, George, E, Gonzalez, R, Goodman, S, Green, E, Green, D, Greenwald, A, Hadfield, J, Hedges, L, Held, L, Hua Ho, T, Hoijtink, H, Hruschka, D, Imai, K, Imbens, G, Ioannidis, J, Jeon, M, Jones, J, Kirchler, M, Laibson, D, List, J, Little, R, Lupia, A, Machery, E, Maxwell, S, Mccarthy, M, Moore, D, Morgan, S, Munafó, M, Nakagawa, S, Nyhan, B, Parker, T, Pericchi, L, Perugini, M, Rouder, J, Rousseau, J, Savalei, V, Schönbrodt, F, Sellke, T, Sinclair, B, Tingley, D, Van Zandt, T, Vazire, S, Watts, D, Winship, C, Wolpert, R, Xie, Y, Young, C, Zinman, J, Johnson, V, Benjamin, DJ, Berger, JO, Nosek, BA, Wagenmakers, EJ, Bollen, KA, Chambers, CD, Cook, TD, Field, AP, George, EI, Green, DP, Greenwald, AG, Hadfield, JD, Hedges, LV, Hruschka, DJ, Ioannidis, JPA, Jones, JH, Maxwell, SE, McCarthy, M, Moore, DA, Morgan, SL, Parker, TH, Schönbrodt, FD, Watts, DJ, Wolpert, RL, Johnson, VE, Benjamin, D, Berger, J, Johannesson, M, Nosek, B, Wagenmakers, E, Berk, R, Bollen, K, Brembs, B, Brown, L, Camerer, C, Cesarini, D, Chambers, C, Clyde, M, Cook, T, De Boeck, P, Dienes, Z, Dreber, A, Easwaran, K, Efferson, C, Fehr, E, Fidler, F, Field, A, Forster, M, George, E, Gonzalez, R, Goodman, S, Green, E, Green, D, Greenwald, A, Hadfield, J, Hedges, L, Held, L, Hua Ho, T, Hoijtink, H, Hruschka, D, Imai, K, Imbens, G, Ioannidis, J, Jeon, M, Jones, J, Kirchler, M, Laibson, D, List, J, Little, R, Lupia, A, Machery, E, Maxwell, S, Mccarthy, M, Moore, D, Morgan, S, Munafó, M, Nakagawa, S, Nyhan, B, Parker, T, Pericchi, L, Perugini, M, Rouder, J, Rousseau, J, Savalei, V, Schönbrodt, F, Sellke, T, Sinclair, B, Tingley, D, Van Zandt, T, Vazire, S, Watts, D, Winship, C, Wolpert, R, Xie, Y, Young, C, Zinman, J, Johnson, V, Benjamin, DJ, Berger, JO, Nosek, BA, Wagenmakers, EJ, Bollen, KA, Chambers, CD, Cook, TD, Field, AP, George, EI, Green, DP, Greenwald, AG, Hadfield, JD, Hedges, LV, Hruschka, DJ, Ioannidis, JPA, Jones, JH, Maxwell, SE, McCarthy, M, Moore, DA, Morgan, SL, Parker, TH, Schönbrodt, FD, Watts, DJ, Wolpert, RL, and Johnson, VE
Published: 2018

27. The association of depression and all-cause and cause-specific mortality: An umbrella review of systematic reviews and meta-analyses

Author: Machado, MO, Veronese, N, Sanches, M, Stubbs, B, Koyanagi, A, Thompson, T, Tzoulaki, I, Solmi, M, Vancampfort, D, Schuch, FB, Maes, Michael, Fava, GA, Ioannidis, JPA, Carvalho, AF, Machado, MO, Veronese, N, Sanches, M, Stubbs, B, Koyanagi, A, Thompson, T, Tzoulaki, I, Solmi, M, Vancampfort, D, Schuch, FB, Maes, Michael, Fava, GA, Ioannidis, JPA, and Carvalho, AF
Published: 2018

28. Erratum to:Methods for evaluating medical tests and biomarkers

Author: Gopalakrishna, G, Langendam, M, Scholten, R, Bossuyt, P, Leeflang, M, Noel-Storr, A, Thomas, J, Marshall, I, Wallace, B, Whiting, P, Davenport, C, GopalaKrishna, G, De Salis, I, Mallett, S, Wolff, R, Riley, R, Westwood, M, Kleinen, J, Collins, G, Reitsma, H, Moons, K, Zapf, A, Hoyer, A, Kramer, K, Kuss, O, Ensor, J, Deeks, JJ, Martin, EC, Riley, RD, Rücker, G, Steinhauser, S, Schumacher, M, Snell, K, Willis, B, Debray, T, Deeks, J, Di Ruffano, LF, Taylor-Phillips, S, Hyde, C, Taylor, SA, Batnagar, G, STREAMLINE COLON Investigators, STREAMLINE LUNG Investigators, METRIC Investigators, Seedat, F, Clarke, A, Byron, S, Nixon, F, Albrow, R, Walker, T, Deakin, C, Zhelev, Z, Hunt, H, Yang, Y, Abel, L, Buchanan, J, Fanshawe, T, Shinkins, B, Wynants, L, Verbakel, J, Van Huffel, S, Timmerman, D, Van Calster, B, Zwinderman, A, Oke, J, O'Sullivan, J, Perera, R, Nicholson, B, Bromley, HL, Roberts, TE, Francis, A, Petrie, D, Mann, GB, Malottki, K, Smith, H, Billingham, L, Sitch, A, Gerke, O, Holm-Vilstrup, M, Segtnan, EA, Halekoh, U, Høilund-Carlsen, PF, Francq, BG, Dinnes, J, Parkes, J, Gregory, W, Hewison, J, Altman, D, Rosenberg, W, Selby, P, Asselineau, J, Perez, P, Paye, A, Bessede, E, Proust-Lima, C, Naaktgeboren, C, De Groot, J, Rutjes, A, Reitsma, J, Ogundimu, E, Cook, J, Le Manach, Y, Vergouwe, Y, Pajouheshnia, R, Groenwold, R, Peelen, L, Nieboer, D, De Cock, B, Pencina, MJ, Steyerberg, EW, Cooper, J, Parsons, N, Stinton, C, Smith, S, Dickens, A, Jordan, R, Enocson, A, Fitzmaurice, D, Adab, P, Boachie, C, Vidmar, G, Freeman, K, Connock, M, Court, R, Moons, C, Harris, J, Mumford, A, Plummer, Z, Lee, K, Reeves, B, Rogers, C, Verheyden, V, Angelini, GD, Murphy, GJ, Huddy, J, Ni, M, Good, K, Cooke, G, Hanna, G, Ma, J, Moons, KGMC, De Groot, JAH, Altman, DG, Reitsma, JB, Collins, GS, Moons, KGM, Kamarudin, AN, Kolamunnage-Dona, R, Cox, T, Borsci, S, Pérez, T, Pardo, MC, Candela-Toha, A, Muriel, A, Zamora, J, Sanghera, S, Mohiuddin, S, Martin, R, Donovan, J, Coast, J, Seo, MK, Cairns, J, Mitchell, E, Smith, A, Wright, J, Hall, P, Messenger, M, Calder, N, Wickramasekera, N, Vinall-Collier, K, Lewington, A, Damen, J, Cairns, D, Hutchinson, M, Sturgeon, C, Mitchel, L, Kift, R, Christakoudi, S, Rungall, M, Mobillo, P, Montero, R, Tsui, T-L, Kon, SP, Tucker, B, Sacks, S, Farmer, C, Strom, T, Chowdhury, P, Rebollo-Mesa, I, Hernandez-Fuentes, M, Damen, JAAG, Debray, TPA, Heus, P, Hooft, L, Scholten, RJPM, Schuit, E, Tzoulaki, I, Lassale, CM, Siontis, GCM, Chiocchia, V, Roberts, C, Schlüssel, MM, Gerry, S, Black, JA, Van der Schouw, YT, Peelen, LM, Spence, G, McCartney, D, Van den Bruel, A, Lasserson, D, Hayward, G, Vach, W, De Jong, A, Burggraaff, C, Hoekstra, O, Zijlstra, J, De Vet, H, Graziadio, S, Allen, J, Johnston, L, O'Leary, R, Power, M, Johnson, L, Waters, R, Simpson, J, Fanshawe, TR, Phillips, P, Plumb, A, Helbren, E, Halligan, S, Gale, A, Sekula, P, Sauerbrei, W, Forman, JR, Dutton, SJ, Takwoingi, Y, Hensor, EM, Nichols, TE, Kempf, E, Porcher, R, De Beyer, J, Hopewell, S, Dennis, J, Shields, B, Jones, A, Henley, W, Pearson, E, Hattersley, A, MASTERMIND consortium, Scheibler, F, Rummer, A, Sturtz, S, Großelfinger, R, Banister, K, Ramsay, C, Azuara-Blanco, A, Burr, J, Kumarasamy, M, Bourne, R, Uchegbu, I, Murphy, J, Carter, A, Marti, J, Eatock, J, Robotham, J, Dudareva, M, Gilchrist, M, Holmes, A, Monaghan, P, Lord, S, StJohn, A, Sandberg, S, Cobbaert, C, Lennartz, L, Verhagen-Kamerbeek, W, Ebert, C, Horvath, A, Test Evaluation Working Group of the European Federation of Clinical Chemistry and Laboratory Medicine, Jenniskens, K, Peters, J, Grigore, B, Ukoumunne, O, Levis, B, Benedetti, A, Levis, AW, Ioannidis, JPA, Shrier, I, Cuijpers, P, Gilbody, S, Kloda, LA, McMillan, D, Patten, SB, Steele, RJ, Ziegelstein, RC, Bombardier, CH, Osório, FDL, Fann, JR, Gjerdingen, D, Lamers, F, Lotrakul, M, Loureiro, SR, Löwe, B, Shaaban, J, Stafford, L, Van Weert, HCPM, Whooley, MA, Williams, LS, Wittkampf, KA, Yeung, AS, Thombs, BD, Cooper, C, Nieto, T, Smith, C, Tucker, O, Dretzke, J, Beggs, A, Rai, N, Bayliss, S, Stevens, S, Mallet, S, Sundar, S, Hall, E, Porta, N, Estelles, DL, De Bono, J, CTC-STOP protocol development group, and National Institute for Health Research
Subjects: medicine.medical_specialty, Astrophysics::High Energy Astrophysical Phenomena, MEDLINE, 030204 cardiovascular system & hematology, BTC (Bristol Trials Centre), MASTERMIND consortium, 03 medical and health sciences, 0302 clinical medicine, medicine, 030212 general & internal medicine, Intensive care medicine, CTC-STOP protocol development group, lcsh:R5-920, business.industry, Test Evaluation Working Group of the European Federation of Clinical Chemistry and Laboratory Medicine, Published Erratum, STREAMLINE COLON Investigators, 3. Good health, STREAMLINE LUNG Investigators, Centre for Surgical Research, Family medicine, METRIC Investigators, High Energy Physics::Experiment, Erratum, business, lcsh:Medicine (General)
Abstract: [This corrects the article DOI: 10.1186/s41512-016-0001-y.].
Published: 2017

29. Empirical assessment of published effect sizes and power in the recent cognitive neuroscience and psychology literature

Author: Szucs, D, Ioannidis, JPA, Szucs, Denes [0000-0002-9477-0801], and Apollo - University of Cambridge Repository
Subjects: FOS: Psychology, Cognitive Neuroscience, Surveys and Questionnaires, Publications, Humans, Psychology, Empirical Research, Probability
Abstract: We have empirically assessed the distribution of published effect sizes and estimated power by analyzing 26,841 statistical records from 3,801 cognitive neuroscience and psychology papers published recently. The reported median effect size was D = 0.93 (interquartile range: 0.64-1.46) for nominally statistically significant results and D = 0.24 (0.11-0.42) for nonsignificant results. Median power to detect small, medium, and large effects was 0.12, 0.44, and 0.73, reflecting no improvement through the past half-century. This is so because sample sizes have remained small. Assuming similar true effect sizes in both disciplines, power was lower in cognitive neuroscience than in psychology. Journal impact factors negatively correlated with power. Assuming a realistic range of prior probabilities for null hypotheses, false report probability is likely to exceed 50% for the whole literature. In light of our findings, the recently reported low replication success in psychology is realistic, and worse performance may be expected for cognitive neuroscience.
Published: 2017

30. Large-scale GWAS identifies multiple loci for hand grip strength providing biological insights into muscular fitness

Author: Willems, SM, Wright, DJ, Day, FR, Trajanoska, K, Joshi, PK, Morris, JA, Matteini, AM, Garton, FC, Grarup, N, Oskolkov, N, Thalamuthu, A, Mangino, M, Liu, J, Demirkan, A, Lek, M, Xu, L, Wang, G, Oldmeadow, C, Gaulton, KJ, Lotta, LA, Miyamoto-Mikami, E, Rivas, MA, White, T, Loh, P-R, Aadahl, M, Amin, N, Attia, JR, Austin, K, Benyamin, B, Brage, S, Cheng, Y-C, Cięszczyk, P, Derave, W, Eriksson, K-F, Eynon, N, Linneberg, A, Lucia, A, Massidda, M, Mitchell, BD, Miyachi, M, Murakami, H, Padmanabhan, S, Pandey, A, Papadimitriou, I, Rajpal, DK, Sale, C, Schnurr, TM, Sessa, F, Shrine, N, Tobin, MD, Varley, I, Wain, LV, Wray, NR, Lindgren, CM, MacArthur, DG, Waterworth, DM, McCarthy, MI, Pedersen, O, Khaw, K-T, Kiel, DP, Oei, L, Zheng, H-F, Forgetta, V, Leong, A, Ahmad, OS, Laurin, C, Mokry, LE, Ross, S, Elks, CE, Bowden, J, Warrington, NM, Murray, A, Ruth, KS, Tsilidis, KK, Medina-Gómez, C, Estrada, K, Bis, JC, Chasman, DI, Demissie, S, Enneman, AW, Hsu, Y-H, Ingvarsson, T, Kähönen, M, Kammerer, C, Lacroix, AZ, Li, G, Liu, C-T, Liu, Y, Lorentzon, M, Mägi, R, Mihailov, E, Milani, L, Moayyeri, A, Nielson, CM, Sham, PC, Siggeirsdotir, K, Sigurdsson, G, Stefansson, K, Trompet, S, Thorleifsson, G, Vandenput, L, van der Velde, N, Viikari, J, Xiao, S-M, Zhao, JH, Evans, DS, Cummings, SR, Cauley, J, Duncan, EL, de Groot, LCPGM, Esko, T, Gudnason, V, Harris, TB, Jackson, RD, Jukema, JW, Ikram, AMA, Karasik, D, Kaptoge, S, Kung, AWC, Lehtimäki, T, Lyytikäinen, L-P, Lips, P, Luben, R, Metspalu, A, van Meurs, JBJ, Minster, RL, Orwoll, E, Oei, E, Psaty, BM, Raitakari, OT, Ralston, SW, Ridker, PM, Robbins, JA, Smith, AV, Styrkarsdottir, U, Tranah, GJ, Thorstensdottir, U, Uitterlinden, AG, Zmuda, J, Zillikens, MC, Ntzani, EE, Evangelou, E, Ioannidis, JPA, Evans, DM, Ohlsson, C, Pitsiladis, Y, Fuku, N, Franks, PW, North, KN, van Duijn, CM, Mather, KA, Hansen, T, Hansson, O, Spector, T, Murabito, JM, Richards, JB, Rivadeneira, F, Langenberg, C, Perry, JRB, Wareham, NJ, Scott, RA, Willems, Sara M, Wright, Daniel J, Day, Felix R, Trajanoska, Katerina, Benyamin, Beben, Scott, Robert A, GEFOS Anytype Fracture Consortium, Wright, Daniel [0000-0003-3983-2093], Day, Felix [0000-0003-3789-7651], White, Thomas [0000-0001-8456-0803], Brage, Soren [0000-0002-1265-7355], Khaw, Kay-Tee [0000-0002-8802-2903], Langenberg, Claudia [0000-0002-5017-7344], Perry, John [0000-0001-6483-3771], Wareham, Nicholas [0000-0003-1422-2993], Apollo - University of Cambridge Repository, Epidemiology, and Internal Medicine
Subjects: Male, Genome-wide association study, VARIANTS, Physical strength, DISEASE, Grip strength, 0302 clinical medicine, Neoplasm Proteins/genetics, GENETIC INFLUENCES, European Continental Ancestry Group/genetics, Aetiology, education.field_of_study, Hand Strength, Deporte, 3. Good health, Neoplasm Proteins, muscular fitness, Science & Technology - Other Topics, Medical genetics, medicine.medical_specialty, Science, 1.1 Normal biological development and functioning, European Continental Ancestry Group, ta3111, Article, White People, General Biochemistry, Genetics and Molecular Biology, 03 medical and health sciences, FRACTURES, Genetics, Humans, GENOME-WIDE ASSOCIATION, Genetik, Polymorphism, education, METAANALYSIS, Aged, VLAG, Global Nutrition, Wereldvoeding, Science & Technology, ta1184, Prevention, Hand/physiology, Biology and Life Sciences, INSTRUMENTS, Hand, GEFOS Any-Type of Fracture Consortium, Nuclear Proteins/genetics, Genetics, Population, 030104 developmental biology, Genetic Loci, 030217 neurology & neurosurgery, 0301 basic medicine, Transforming Growth Factor alpha/genetics, General Physics and Astronomy, Bioinformatics, GROWTH-FACTOR-ALPHA, Cohort Studies, Medicine and Health Sciences, 2.1 Biological and endogenous factors, ta315, Multidisciplinary, Nuclear Proteins, Single Nucleotide, Middle Aged, Multidisciplinary Sciences, MENDELIAN RANDOMIZATION, SKELETAL-MUSCLE, Female, Medical Genetics, Adult, Population, Quantitative trait locus, Biology, Polymorphism, Single Nucleotide, Underpinning research, Hand strength, MD Multidisciplinary, Mendelian randomization, medicine, Life Science, Membrane Proteins/genetics, Deportes, Medicinsk genetik, Repressor Proteins/genetics, Whites, Actins/genetics, Membrane Proteins, General Chemistry, Transforming Growth Factor alpha, Genética, Actins, United Kingdom, Repressor Proteins, Good Health and Well Being, Exercise Physiology and nutrition, Musculoskeletal, genome-wide association, Genome-Wide Association Study
Abstract: Hand grip strength is a widely used proxy of muscular fitness, a marker of frailty, and predictor of a range of morbidities and all-cause mortality. To investigate the genetic determinants of variation in grip strength, we perform a large-scale genetic discovery analysis in a combined sample of 195,180 individuals and identify 16 loci associated with grip strength (P, Hand grip strength as a proxy of muscular fitness is a clinical predictor of mortality and morbidity. In a large-scale GWA study, the authors find 16 robustly associated genetic loci that highlight roles in muscle fibre structure and function, neuronal maintenance and nervous system signal transduction.
Published: 2017

31. Very large treatment effects in randomised trials as an empirical marker to indicate whether subsequent trials are necessary: meta-epidemiological assessment

Author: T V Pereira, G Kiew, Peter McCulloch, Myura Nagendran, Ioannidis Jpa., Mahiben Maruthappu, and Douglas G. Altman
Subjects: medicine.medical_specialty, MEDLINE, computer.software_genre, law.invention, 03 medical and health sciences, 0302 clinical medicine, Randomized controlled trial, law, Epidemiology, Forest plot, Medicine, Humans, 030212 general & internal medicine, Randomized Controlled Trials as Topic, business.industry, Binary outcome, Research, Reproducibility of Results, General Medicine, Systematic review, Treatment Outcome, Meta-analysis, Relative risk, Data Interpretation, Statistical, Physical therapy, Data mining, business, computer, 030217 neurology & neurosurgery
Abstract: Objective: Most healthcare interventions provide modest benefits, but occasionally trials report very large improvements over existing treatments or inactive controls. This often leads to speculation that further trials may be unnecessary. We examined whether a very large effect (VLE, relative risk (RR) of ≤0.2 or ≥5) in a randomised trial could be an empirical marker that subsequent trials are unnecessary. Design: Meta-epidemiological assessment of existing published data on randomised trials Data sources: Cochrane Database of Systematic Reviews (CDSR, 2010, issue 7) with data on subsequent large trials updated to CDSR 2015, issue 12. Eligibility criteria for selecting forest plots:: All binary-outcome forest plots which: (i) contained an index randomised trial with a VLE that was nominally statistically significant (P Results: 3,082 reviews yielded 85,002 forest plots, of which only 44 (0.05%) satisfied the inclusion criteria. Index trials were generally small with a median sample of 99 (median 14 events). Few index trials were rated at low risk of bias (9 of 44; 20%). The relative risk was closer to the null in the subsequent large trials in 43 of 44 cases. Subsequent large trial data failed to find a statistically significant (pConclusions: The frequency of very large effects followed by a large trial is vanishingly small, and where they occur they do not appear to be a reliable marker for a benefit that is reproducible and directly actionable. An empirical rule using a very large effect in an RCT as a marker that further trials are unnecessary would be neither practical nor useful. Caution should be taken when interpreting small studies with very large treatment effects.
Published: 2016

32. A comparison of bivariate, multivariate random-effects, and Poisson correlated gamma-frailty models to meta-analyze individual patient data of ordinal scale diagnostic tests

Author: Simoneau, G, Levis, B, Cuijpers, P, Ioannidis, JPA, Patten, SB, Shrier, I, Bombardier, CH, Osorio, FDL, Fann, JR, Gjerdingen, D, Lamers, F, Lotrakul, M, Loewe, B, Shaaban, J, Stafford, L, van Weert, HCPM, Whooley, MA, Wittkampf, KA, Yeung, AS, Thombs, BD, Benedetti, A, Simoneau, G, Levis, B, Cuijpers, P, Ioannidis, JPA, Patten, SB, Shrier, I, Bombardier, CH, Osorio, FDL, Fann, JR, Gjerdingen, D, Lamers, F, Lotrakul, M, Loewe, B, Shaaban, J, Stafford, L, van Weert, HCPM, Whooley, MA, Wittkampf, KA, Yeung, AS, Thombs, BD, and Benedetti, A
Abstract: Individual patient data (IPD) meta-analyses are increasingly common in the literature. In the context of estimating the diagnostic accuracy of ordinal or semi-continuous scale tests, sensitivity and specificity are often reported for a given threshold or a small set of thresholds, and a meta-analysis is conducted via a bivariate approach to account for their correlation. When IPD are available, sensitivity and specificity can be pooled for every possible threshold. Our objective was to compare the bivariate approach, which can be applied separately at every threshold, to two multivariate methods: the ordinal multivariate random-effects model and the Poisson correlated gamma-frailty model. Our comparison was empirical, using IPD from 13 studies that evaluated the diagnostic accuracy of the 9-item Patient Health Questionnaire depression screening tool, and included simulations. The empirical comparison showed that the implementation of the two multivariate methods is more laborious in terms of computational time and sensitivity to user-supplied values compared to the bivariate approach. Simulations showed that ignoring the within-study correlation of sensitivity and specificity across thresholds did not worsen inferences with the bivariate approach compared to the Poisson model. The ordinal approach was not suitable for simulations because the model was highly sensitive to user-supplied starting values. We tentatively recommend the bivariate approach rather than more complex multivariate methods for IPD diagnostic accuracy meta-analyses of ordinal scale tests, although the limited type of diagnostic data considered in the simulation study restricts the generalization of our findings.
Published: 2017

33. Erratum to: Methods for evaluating medical tests and biomarkers.

Author: Gopalakrishna, G, Langendam, M, Scholten, R, Bossuyt, P, Leeflang, M, Noel-Storr, A, Thomas, J, Marshall, I, Wallace, B, Whiting, P, Davenport, C, GopalaKrishna, G, de Salis, I, Mallett, S, Wolff, R, Riley, R, Westwood, M, Kleinen, J, Collins, G, Reitsma, H, Moons, K, Zapf, A, Hoyer, A, Kramer, K, Kuss, O, Ensor, J, Deeks, JJ, Martin, EC, Riley, RD, Rücker, G, Steinhauser, S, Schumacher, M, Snell, K, Willis, B, Debray, T, Deeks, J, di Ruffano, LF, Taylor-Phillips, S, Hyde, C, Taylor, SA, Batnagar, G, STREAMLINE COLON Investigators, STREAMLINE LUNG Investigators, METRIC Investigators, Di Ruffano, LF, Seedat, F, Clarke, A, Byron, S, Nixon, F, Albrow, R, Walker, T, Deakin, C, Zhelev, Z, Hunt, H, Yang, Y, Abel, L, Buchanan, J, Fanshawe, T, Shinkins, B, Wynants, L, Verbakel, J, Van Huffel, S, Timmerman, D, Van Calster, B, Zwinderman, A, Oke, J, O'Sullivan, J, Perera, R, Nicholson, B, Bromley, HL, Roberts, TE, Francis, A, Petrie, D, Mann, GB, Malottki, K, Smith, H, Billingham, L, Sitch, A, Gerke, O, Holm-Vilstrup, M, Segtnan, EA, Halekoh, U, Høilund-Carlsen, PF, Francq, BG, Dinnes, J, Parkes, J, Gregory, W, Hewison, J, Altman, D, Rosenberg, W, Selby, P, Asselineau, J, Perez, P, Paye, A, Bessede, E, Proust-Lima, C, Naaktgeboren, C, de Groot, J, Rutjes, A, Reitsma, J, Ogundimu, E, Cook, J, Le Manach, Y, Vergouwe, Y, Pajouheshnia, R, Groenwold, R, Peelen, L, Nieboer, D, De Cock, B, Pencina, MJ, Steyerberg, EW, Cooper, J, Parsons, N, Stinton, C, Smith, S, Dickens, A, Jordan, R, Enocson, A, Fitzmaurice, D, Adab, P, Boachie, C, Vidmar, G, Freeman, K, Connock, M, Court, R, Moons, C, Harris, J, Mumford, A, Plummer, Z, Lee, K, Reeves, B, Rogers, C, Verheyden, V, Angelini, GD, Murphy, GJ, Huddy, J, Ni, M, Good, K, Cooke, G, Hanna, G, Ma, J, Moons, KGMC, de Groot, JAH, Altman, DG, Reitsma, JB, Collins, GS, Moons, KGM, Kamarudin, AN, Kolamunnage-Dona, R, Cox, T, Borsci, S, Pérez, T, Pardo, MC, Candela-Toha, A, Muriel, A, Zamora, J, Sanghera, S, Mohiuddin, S, Martin, R, Donovan, J, Coast, J, Seo, MK, Cairns, J, Mitchell, E, Smith, A, Wright, J, Hall, P, Messenger, M, Calder, N, Wickramasekera, N, Vinall-Collier, K, Lewington, A, Damen, J, Cairns, D, Hutchinson, M, Sturgeon, C, Mitchel, L, Kift, R, Christakoudi, S, Rungall, M, Mobillo, P, Montero, R, Tsui, T-L, Kon, SP, Tucker, B, Sacks, S, Farmer, C, Strom, T, Chowdhury, P, Rebollo-Mesa, I, Hernandez-Fuentes, M, Damen, JAAG, Debray, TPA, Heus, P, Hooft, L, Scholten, RJPM, Schuit, E, Tzoulaki, I, Lassale, CM, Siontis, GCM, Chiocchia, V, Roberts, C, Schlüssel, MM, Gerry, S, Black, JA, van der Schouw, YT, Peelen, LM, Spence, G, McCartney, D, van den Bruel, A, Lasserson, D, Hayward, G, Vach, W, de Jong, A, Burggraaff, C, Hoekstra, O, Zijlstra, J, de Vet, H, Graziadio, S, Allen, J, Johnston, L, O'Leary, R, Power, M, Johnson, L, Waters, R, Simpson, J, Fanshawe, TR, Phillips, P, Plumb, A, Helbren, E, Halligan, S, Gale, A, Sekula, P, Sauerbrei, W, Forman, JR, Dutton, SJ, Takwoingi, Y, Hensor, EM, Nichols, 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Van Huffel, S, Timmerman, D, Van Calster, B, Zwinderman, A, Oke, J, O'Sullivan, J, Perera, R, Nicholson, B, Bromley, HL, Roberts, TE, Francis, A, Petrie, D, Mann, GB, Malottki, K, Smith, H, Billingham, L, Sitch, A, Gerke, O, Holm-Vilstrup, M, Segtnan, EA, Halekoh, U, Høilund-Carlsen, PF, Francq, BG, Dinnes, J, Parkes, J, Gregory, W, Hewison, J, Altman, D, Rosenberg, W, Selby, P, Asselineau, J, Perez, P, Paye, A, Bessede, E, Proust-Lima, C, Naaktgeboren, C, de Groot, J, Rutjes, A, Reitsma, J, Ogundimu, E, Cook, J, Le Manach, Y, Vergouwe, Y, Pajouheshnia, R, Groenwold, R, Peelen, L, Nieboer, D, De Cock, B, Pencina, MJ, Steyerberg, EW, Cooper, J, Parsons, N, Stinton, C, Smith, S, Dickens, A, Jordan, R, Enocson, A, Fitzmaurice, D, Adab, P, Boachie, C, Vidmar, G, Freeman, K, Connock, M, Court, R, Moons, C, Harris, J, Mumford, A, Plummer, Z, Lee, K, Reeves, B, Rogers, C, Verheyden, V, Angelini, GD, Murphy, GJ, Huddy, J, Ni, M, Good, K, Cooke, G, Hanna, G, Ma, J, Moons, KGMC, de Groot, JAH, Altman, DG, Reitsma, JB, Collins, GS, Moons, KGM, Kamarudin, AN, Kolamunnage-Dona, R, Cox, T, Borsci, S, Pérez, T, Pardo, MC, Candela-Toha, A, Muriel, A, Zamora, J, Sanghera, S, Mohiuddin, S, Martin, R, Donovan, J, Coast, J, Seo, MK, Cairns, J, Mitchell, E, Smith, A, Wright, J, Hall, P, Messenger, M, Calder, N, Wickramasekera, N, Vinall-Collier, K, Lewington, A, Damen, J, Cairns, D, Hutchinson, M, Sturgeon, C, Mitchel, L, Kift, R, Christakoudi, S, Rungall, M, Mobillo, P, Montero, R, Tsui, T-L, Kon, SP, Tucker, B, Sacks, S, Farmer, C, Strom, T, Chowdhury, P, Rebollo-Mesa, I, Hernandez-Fuentes, M, Damen, JAAG, Debray, TPA, Heus, P, Hooft, L, Scholten, RJPM, Schuit, E, Tzoulaki, I, Lassale, CM, Siontis, GCM, Chiocchia, V, Roberts, C, Schlüssel, MM, Gerry, S, Black, JA, van der Schouw, YT, Peelen, LM, Spence, G, McCartney, D, van den Bruel, A, Lasserson, D, Hayward, G, Vach, W, de Jong, A, Burggraaff, C, Hoekstra, O, Zijlstra, J, de Vet, H, Graziadio, S, Allen, J, Johnston, L, O'Leary, R, Power, M, Johnson, L, Waters, R, Simpson, J, Fanshawe, TR, Phillips, P, Plumb, A, Helbren, E, Halligan, S, Gale, A, Sekula, P, Sauerbrei, W, Forman, JR, Dutton, SJ, Takwoingi, Y, Hensor, EM, Nichols, TE, Kempf, E, Porcher, R, de Beyer, J, Hopewell, S, Dennis, J, Shields, B, Jones, A, Henley, W, Pearson, E, Hattersley, A, MASTERMIND consortium, Scheibler, F, Rummer, A, Sturtz, S, Großelfinger, R, Banister, K, Ramsay, C, Azuara-Blanco, A, Burr, J, Kumarasamy, M, Bourne, R, Uchegbu, I, Murphy, J, Carter, A, Marti, J, Eatock, J, Robotham, J, Dudareva, M, Gilchrist, M, Holmes, A, Monaghan, P, Lord, S, StJohn, A, Sandberg, S, Cobbaert, C, Lennartz, L, Verhagen-Kamerbeek, W, Ebert, C, Horvath, A, Test Evaluation Working Group of the European Federation of Clinical Chemistry and Laboratory Medicine, Jenniskens, K, Peters, J, Grigore, B, Ukoumunne, O, Levis, B, Benedetti, A, Levis, AW, Ioannidis, JPA, Shrier, I, Cuijpers, P, Gilbody, S, Kloda, LA, McMillan, D, Patten, SB, Steele, RJ, Ziegelstein, RC, Bombardier, CH, Osório, FDL, Fann, JR, Gjerdingen, D, Lamers, F, Lotrakul, M, Loureiro, SR, Löwe, B, Shaaban, J, Stafford, L, van Weert, HCPM, Whooley, MA, Williams, LS, Wittkampf, KA, Yeung, AS, Thombs, BD, Cooper, C, Nieto, T, Smith, C, Tucker, O, Dretzke, J, Beggs, A, Rai, N, Bayliss, S, Stevens, S, Mallet, S, Sundar, S, Hall, E, Porta, N, Estelles, DL, de Bono, J, and CTC-STOP protocol development group
Abstract: [This corrects the article DOI: 10.1186/s41512-016-0001-y.].
Published: 2017

34. Identification of new susceptibility loci for osteoarthritis (arcOGEN): a genome-wide association study

Author: Shin S-Y., Steffan D. Bos, Margreet Kloppenburg, Andres Metspalu, Ashok Rai, Karen E. Chapman, Tõnu Esko, J M Willkinson, Panagiotis Deloukas, Gudmar Thorleifsson, Nigel K Arden, Ingileif Jonsdottir, Andrew Carr, Kalliope Panoutsopoulou, Fernando Rivadeneira, Michelle Ricketts, F O'Neill, Sarah Metrustry, Massimo Mangino, Ollier Wer., Sarah E. Hunt, N Koller, Sally Doherty, M C Lopes, John Loughlin, Raine Eva., Andrew McCaskie, Bridget Watkins, Lorraine Southam, Gillian A. Wallis, Battley P-K., Vijay K. Yadav, U Stykarsdottir, N Aslam, Evangelos Evangelou, van Meurs Jbj., K Walker, Donald Salter, Mike R. Reed, K. Stefansson, I Carluke, E Arden, K Sherburn, Stuart Ralston, Tim D. Spector, Suzannah Bumpstead, Madhushika Ratnayake, Hannah Blackburn, K S Elliott, H Jonnson, Guangju Zhai, Michael Doherty, M. Wheeler, Thorvaldur Ingvarsson, Ingrid Meulenbelt, J Joseph, Fraser Birrell, D Swift, A.G. Uitterlinden, K Dixon, Ioannidis Jpa., Hanneke J. M. Kerkhof, Aaron G. Day-Williams, E Paling, Simon C. Potter, Ana M. Valdes, P E Slagboom, Richard Keen, Sheryl Mitchell, C Beazley, Eleftheria Zeggini, A Hoffman, Vesna Boraska, Jeanine J. Houwing-Duistermaat, Allan Gordon, and N W Rayner
Subjects: Male, medicine.medical_specialty, Linkage disequilibrium, Chromosome 9, Genome-wide association study, Polymorphism, Single Nucleotide, osteoarthritis, GWAS, FTO gene, Linkage Disequilibrium, Osteoarthritis, Hip, 03 medical and health sciences, 0302 clinical medicine, Internal medicine, Osteoarthritis, medicine, Humans, Genetic Predisposition to Disease, Arthroplasty, Replacement, Chromosome 12, 030304 developmental biology, 030203 arthritis & rheumatology, Genetics, Hip surgery, 0303 health sciences, business.industry, Articles, General Medicine, Odds ratio, Osteoarthritis, Knee, 3. Good health, Chromosome 3, Case-Control Studies, Female, business, Genome-Wide Association Study
Abstract: BACKGROUND: Osteoarthritis is the most common form of arthritis worldwide and is a major cause of pain and disability in elderly people. The health economic burden of osteoarthritis is increasing commensurate with obesity prevalence and longevity. Osteoarthritis has a strong genetic component but the success of previous genetic studies has been restricted due to insufficient sample sizes and phenotype heterogeneity. METHODS: We undertook a large genome-wide association study (GWAS) in 7410 unrelated and retrospectively and prospectively selected patients with severe osteoarthritis in the arcOGEN study, 80% of whom had undergone total joint replacement, and 11,009 unrelated controls from the UK. We replicated the most promising signals in an independent set of up to 7473 cases and 42,938 controls, from studies in Iceland, Estonia, the Netherlands, and the UK. All patients and controls were of European descent. FINDINGS: We identified five genome-wide significant loci (binomial test p≤5·0×10(-8)) for association with osteoarthritis and three loci just below this threshold. The strongest association was on chromosome 3 with rs6976 (odds ratio 1·12 [95% CI 1·08-1·16]; p=7·24×10(-11)), which is in perfect linkage disequilibrium with rs11177. This SNP encodes a missense polymorphism within the nucleostemin-encoding gene GNL3. Levels of nucleostemin were raised in chondrocytes from patients with osteoarthritis in functional studies. Other significant loci were on chromosome 9 close to ASTN2, chromosome 6 between FILIP1 and SENP6, chromosome 12 close to KLHDC5 and PTHLH, and in another region of chromosome 12 close to CHST11. One of the signals close to genome-wide significance was within the FTO gene, which is involved in regulation of bodyweight-a strong risk factor for osteoarthritis. All risk variants were common in frequency and exerted small effects. INTERPRETATION: Our findings provide insight into the genetics of arthritis and identify new pathways that might be amenable to future therapeutic intervention. FUNDING: arcOGEN was funded by a special purpose grant from Arthritis Research UK.
Published: 2012

35. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement (Chinese edition)

Author: D McNamee, Jeremy M. Grimshaw, Tom Lang, Jesse A. Berlin, P C Gøtzsche, N Barrowman, Gerd Antes, E Ernst, Lorenzo Moja, M Egger, Drummond Rennie, K F Schulz, David Tovey, David Moher, Julian P T Higgins, Alessandro Liberati, M Napoli, C Mulrow, Ioannidis Jpa., Kay Dickersin, Douglas G. Altman, Jonathan J Deeks, A Oxman, Roberto D'Amico, Peter Tugwell, Mike Clarke, David C. Atkins, J Clark, Philip J. Devereaux, Virginia Barbour, Margaret Sampson, B Pham, Jos Kleijnen, G Guyatt, Jennifer Tetzlaff, Deborah J. Cook, N Magrini, and P G Shekelle
Subjects: Meta-analysis, PRISMA, Systematic review, Complementary and alternative medicine, Statement (logic), Applied psychology, Preferred reporting items for systematic reviews and meta-analyses, Psychology
Published: 2009

36. Diagnosis of Parkinson's disease on the basis of clinical and genetic classification: A population-based modelling study

Author: Nalls, MA, McLean, CY, Rick, J, Eberly, S, Hutten, SJ, Gwinn, K, Sutherland, M, Martinez, M, Heutink, P, Williams, NM, Hardy, J, Gasser, T, Brice, A, Price, TR, Nicolas, A, Keller, MF, Molony, C, Gibbs, JR, Chen-Plotkin, A, Suh, E, Letson, C, Fiandaca, MS, Mapstone, M, Federoff, HJ, Noyce, AJ, Morris, H, Van Deerlin, VM, Weintraub, D, Zabetian, C, Hernandez, DG, Lesage, S, Mullins, M, Conley, ED, Northover, CAM, Frasier, M, Marek, K, Day-Williams, AG, Stone, DJ, Ioannidis, JPA, and Singleton, AB
Abstract: © 2015 Elsevier Ltd. Background: Accurate diagnosis and early detection of complex diseases, such as Parkinson's disease, has the potential to be of great benefit for researchers and clinical practice. We aimed to create a non-invasive, accurate classification model for the diagnosis of Parkinson's disease, which could serve as a basis for future disease prediction studies in longitudinal cohorts. Methods: We developed a model for disease classification using data from the Parkinson's Progression Marker Initiative (PPMI) study for 367 patients with Parkinson's disease and phenotypically typical imaging data and 165 controls without neurological disease. Olfactory function, genetic risk, family history of Parkinson's disease, age, and gender were algorithmically selected by stepwise logistic regression as significant contributors to our classifying model. We then tested the model with data from 825 patients with Parkinson's disease and 261 controls from five independent cohorts with varying recruitment strategies and designs: the Parkinson's Disease Biomarkers Program (PDBP), the Parkinson's Associated Risk Study (PARS), 23andMe, the Longitudinal and Biomarker Study in PD (LABS-PD), and the Morris K Udall Parkinson's Disease Research Center of Excellence cohort (Penn-Udall). Additionally, we used our model to investigate patients who had imaging scans without evidence of dopaminergic deficit (SWEDD). Findings: In the population from PPMI, our initial model correctly distinguished patients with Parkinson's disease from controls at an area under the curve (AUC) of 0·923 (95% CI 0·900-0·946) with high sensitivity (0·834, 95% CI 0·711-0·883) and specificity (0·903, 95% CI 0·824-0·946) at its optimum AUC threshold (0·655). All Hosmer-Lemeshow simulations suggested that when parsed into random subgroups, the subgroup data matched that of the overall cohort. External validation showed good classification of Parkinson's disease, with AUCs of 0·894 (95% CI 0·867-0·921) in the PDBP cohort, 0·998 (0·992-1·000) in PARS, 0·955 (no 95% CI available) in 23andMe, 0·929 (0·896-0·962) in LABS-PD, and 0·939 (0·891-0·986) in the Penn-Udall cohort. Four of 17 SWEDD participants who our model classified as having Parkinson's disease converted to Parkinson's disease within 1 year, whereas only one of 38 SWEDD participants who were not classified as having Parkinson's disease underwent conversion (test of proportions, p=0·003). Interpretation: Our model provides a potential new approach to distinguish participants with Parkinson's disease from controls. If the model can also identify individuals with prodromal or preclinical Parkinson's disease in prospective cohorts, it could facilitate identification of biomarkers and interventions. Funding: National Institute on Aging, National Institute of Neurological Disorders and Stroke, and the Michael J Fox Foundation.
Published: 2015

37. Sex based subgroup differences in randomized controlled trials: empirical evidence from Cochrane meta-analyses

Author: Wallach, J D, Sullivan, P G, Trepanowski, J F, Steyerberg, Ewout, Ioannidis, JPA, Wallach, J D, Sullivan, P G, Trepanowski, J F, Steyerberg, Ewout, and Ioannidis, JPA
Published: 2016

38. Stefania Boccia

Author: Boccia, S, primary, Rothman, KJ, additional, Panic, N, additional, Flacco, ME, additional, Rosso, A, additional, Pastorino, R, additional, Manzoli, L, additional, La Vecchia, C, additional, Villari, P, additional, Boffetta, P, additional, Ricciardi, W, additional, and Ioannidis, JPA, additional
Published: 2015
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39. Meta-analysis of genome-wide association studies confirms a susceptibility locus for knee osteoarthritis on chromosome 7q22

Author: Evangelou, E, Valdes, AM, Kerkhof, HJM, Styrkarsdottir, U, Zhu, Y, Meulenbelt, I, Lories, RJ, Karassa, FB, Tylzanowski, P, Bos, SD, Rayner, NW, Southam, L, Zhai, G, Elliott, KS, Hunt, SE, Blackburn, H, Potter, SC, Day-Williams, AG, Beazley, C, Akune, T, Arden, NK, Carr, A, Chapman, K, Cupples, LA, Dai, J, Deloukas, P, Doherty, M, Doherty, S, Engstrom, G, Gonzalez, A, Halldorsson, BV, Hammond, CL, Hart, DJ, Helgadottir, H, Hofman, A, Ikegawa, S, Ingvarsson, T, Jiang, Q, Jonsson, H, Kaprio, J, Kawaguchi, H, Kisand, K, Kloppenburg, M, Kujala, UM, Lohmander, LS, Loughlin, J, Luyten, FP, Mabuchi, A, McCaskie, A, Nakajima, M, Nilsson, PM, Nishida, N, Ollier, WER, Panoutsopoulou, K, Van De Putte, T, Ralston, SH, Rivadeneira, F, Saarela, J, Schulte-Merker, S, Shi, D, Slagboom, PE, Sudo, A, Tamm, A, Thorleifsson, G, Thorsteinsdottir, U, Tsezou, A, Wallis, GA, Wilkinson, JM, Yoshimura, N, Zeggini, E, Zhang, F, Jonsdottir, I, Uitterlinden, AG, Felson, DT, Van Meurs, JB, Stefansson, K, Ioannidis, JPA, and Spector, TD
Published: 2011

40. A meta-analysis of genome-wide association studies identifies novel variants associated with osteoarthritis of the hip

Author: Evangelou, E, Kerkhof, HJ, Styrkarsdottir, U, Ntzani, EE, Bos, SD (Steffan Daniel), Esko, T, Evans, DS, Metrustry, S, Panoutsopoulou, K, Ramos, YFM, Thorleifsson, G, Tsilidis, KK, Arden, N, Aslam, N, Bellamy, N, Birrell, F, Blanco, FJ, Carr, A, Chapman, K, Day-Williams, AG, Deloukas, P, Doherty, M, Engstrom, G, Helgadottir, HT, Hofman, Bert, Ingvarsson, T, Jonsson, H, Keis, A, Keurentjes, JC, Kloppenburg, M, Lind, PA, McCaskie, A, Martin, NG, Milani, L, Montgomery, GW, Nelissen, RGHH, Nevitt, MC, Nilsson, PM, Ollier, WER, Parimi, N, Rai, A, Ralston, SH, Reed, MR, Riancho, JA, Rivadeneira, Fernando, Rodriguez-Fontenla, C, Southam, L, Thorsteinsdottir, U, Tsezou, A, AWallis, G, Wilkinson, JM, Gonzalez, A, Lane, NE, Lohmander, LS, Loughlin, J, Metspalu, A, Uitterlinden, André, Jonsdottir, I, Stefansson, K, Slagboom, PE (Eline), Zeggini, E, Meulenbelt, I, Ioannidis, JPA, Spector, TD, van Meurs, Joyce, Valdes, AM, Evangelou, E, Kerkhof, HJ, Styrkarsdottir, U, Ntzani, EE, Bos, SD (Steffan Daniel), Esko, T, Evans, DS, Metrustry, S, Panoutsopoulou, K, Ramos, YFM, Thorleifsson, G, Tsilidis, KK, Arden, N, Aslam, N, Bellamy, N, Birrell, F, Blanco, FJ, Carr, A, Chapman, K, Day-Williams, AG, Deloukas, P, Doherty, M, Engstrom, G, Helgadottir, HT, Hofman, Bert, Ingvarsson, T, Jonsson, H, Keis, A, Keurentjes, JC, Kloppenburg, M, Lind, PA, McCaskie, A, Martin, NG, Milani, L, Montgomery, GW, Nelissen, RGHH, Nevitt, MC, Nilsson, PM, Ollier, WER, Parimi, N, Rai, A, Ralston, SH, Reed, MR, Riancho, JA, Rivadeneira, Fernando, Rodriguez-Fontenla, C, Southam, L, Thorsteinsdottir, U, Tsezou, A, AWallis, G, Wilkinson, JM, Gonzalez, A, Lane, NE, Lohmander, LS, Loughlin, J, Metspalu, A, Uitterlinden, André, Jonsdottir, I, Stefansson, K, Slagboom, PE (Eline), Zeggini, E, Meulenbelt, I, Ioannidis, JPA, Spector, TD, van Meurs, Joyce, and Valdes, AM
Abstract: Objectives Osteoarthritis (OA) is the most common form of arthritis with a clear genetic component. To identify novel loci associated with hip OA we performed a meta-analysis of genome-wide association studies (GWAS) on European subjects. Methods We performed a two-stage meta-analysis on more than 78 000 participants. In stage 1, we synthesised data from eight GWAS whereas data from 10 centres were used for 'in silico' or 'de novo' replication. Besides the main analysis, a stratified by sex analysis was performed to detect possible sex-specific signals. Meta-analysis was performed using inverse-variance fixed effects models. A random effects approach was also used. Results We accumulated 11 277 cases of radiographic and symptomatic hip OA. We prioritised eight single nucleotide polymorphism (SNPs) for follow-up in the discovery stage (4349 OA cases); five from the combined analysis, two male specific and one female specific. One locus, at 20q13, represented by rs6094710 (minor allele frequency (MAF) 4%) near the NCOA3 (nuclear receptor coactivator 3) gene, reached genome-wide significance level with p=7.9x10(-9) and OR=1.28 (95% CI 1.18 to 1.39) in the combined analysis of discovery (p= 5.6x10(-8)) and follow-up studies (p=7.3x10(-4)). We showed that this gene is expressed in articular cartilage and its expression was significantly reduced in OA-affected cartilage. Moreover, two loci remained suggestive associated; rs5009270 at 7q31 (MAF 30%, p=9.9x10(-7), OR=1.10) and rs3757837 at 7p13 (MAF 6%, p=2.2x10(-6), OR=1.27 in male specific analysis). Conclusions Novel genetic loci for hip OA were found in this meta-analysis of GWAS.
Published: 2014

41. Assessment of Osteoarthritis Candidate Genes in a Meta-Analysis of Nine Genome-Wide Association Studies

Author: Rodriguez-Fontenla, C, Calaza, M, Evangelou, E, Valdes, AM, Arden, N, Blanco, FJ, Carr, A, Chapman, K, Deloukas, P, Doherty, M, Esko, T, Aleta, CMG, Carnota, JJGR, Helgadottir, H, Hofman, Bert, Jonsdottir, I, Kerkhof, HJM, Kloppenburg, M, McCaskie, A, Ntzani, EE, Ollier, WER, Oreiro, N, Panoutsopoulou, K, Ralston, SH, Ramos, YF, Riancho, JA, Rivadeneira, Fernando, Slagboom, PE (Eline), Styrkarsdottir, U, Thorsteinsdottir, U, Thorleifsson, G, Tsezou, A, Uitterlinden, André, Wallis, GA, Wilkinson, JM, Zhai, GJ, Zhu, YY, Felson, DT, Ioannidis, JPA, Loughlin, J, Metspalu, A, Meulenbelt, I, Stefansson, K, van Meurs, Joyce, Zeggini, E, Spector, TD, Gonzalez, A, Rodriguez-Fontenla, C, Calaza, M, Evangelou, E, Valdes, AM, Arden, N, Blanco, FJ, Carr, A, Chapman, K, Deloukas, P, Doherty, M, Esko, T, Aleta, CMG, Carnota, JJGR, Helgadottir, H, Hofman, Bert, Jonsdottir, I, Kerkhof, HJM, Kloppenburg, M, McCaskie, A, Ntzani, EE, Ollier, WER, Oreiro, N, Panoutsopoulou, K, Ralston, SH, Ramos, YF, Riancho, JA, Rivadeneira, Fernando, Slagboom, PE (Eline), Styrkarsdottir, U, Thorsteinsdottir, U, Thorleifsson, G, Tsezou, A, Uitterlinden, André, Wallis, GA, Wilkinson, JM, Zhai, GJ, Zhu, YY, Felson, DT, Ioannidis, JPA, Loughlin, J, Metspalu, A, Meulenbelt, I, Stefansson, K, van Meurs, Joyce, Zeggini, E, Spector, TD, and Gonzalez, A
Abstract: Objective. To assess candidate genes for association with osteoarthritis (OA) and identify promising genetic factors and, secondarily, to assess the candidate gene approach in OA. Methods. A total of 199 candidate genes for association with OA were identified using Human Genome Epidemiology (HuGE) Navigator. All of their single-nucleotide polymorphisms (SNPs) with an allele frequency of > 5% were assessed by fixed-effects meta-analysis of 9 genome-wide association studies (GWAS) that included 5,636 patients with knee OA and 16,972 control subjects and 4,349 patients with hip OA and 17,836 control subjects of European ancestry. An additional 5,921 individuals were genotyped for significantly associated SNPs in the meta-analysis. After correction for the number of independent tests, P values less than 1.58 x 10(-5) were considered significant. Results. SNPs at only 2 of the 199 candidate genes (COL11A1 and VEGF) were associated with OA in the meta-analysis. Two SNPs in COL11A1 showed association with hip OA in the combined analysis: rs4907986 (P = 1.29 x 10(-5), odds ratio [OR] 1.12, 95% confidence interval [95% CI] 1.06 - 1.17) and rs1241164 (P = 1.47 x 10(-5), OR 0.82, 95% CI 0.74 - 0.89). The sex-stratified analysis also showed association of COL11A1 SNP rs4908291 in women (P = 1.29 x 10(-5), OR 0.87, 95% CI 0.82 - 0.92); this SNP showed linkage disequilibrium with rs4907986. A single SNP of VEGF, rs833058, showed association with hip OA in men (P = 1.35 x 10(-5), OR 0.85, 95% CI 0.79 - 0.91). After additional samples were genotyped, association at one of the COL11A1 signals was reinforced, whereas association at VEGF was slightly weakened. Conclusion. Two candidate genes, COL11A1 and VEGF, were significantly associated with OA in this focused meta-analysis. The remaining candidate genes were not associated.
Published: 2014

42. Immunogenicity and adverse events of avian influenza A (H5N1) vaccine in healthy adults: a multiple-treatment meta-analysis

Author: Manzoli, L, Salanti, G, DE VITO, Corrado, Boccia, Antonio, Ioannidis, Jpa, and Villari, Paolo
Published: 2009

43. Vitamin D receptor gene BsmI and TaqI polymorphisms and fracture risk: A meta-analysis

Author: Fang, Y (Yue), Rivadeneira, Fernando, van Meurs, Joyce, Pols, Huib, Ioannidis, JPA, Uitterlinden, André, and Internal Medicine
Published: 2006

44. Large-scale evidence for the effect of the COLIA1 Sp1 polymorphism on osteoporosis outcomes: The GENOMOS study (vol 3, pg 223, 2006)

Author: Ralston, SH, Uitterlinden, André, Brandi, ML, Balcells, S, Langdahl, BL, Lips, P, Lorenc, R, Obermayer-Pietsch, B, Scollen, S, Bustamante, M, Husted, LB, Carey, AH, Diez-Perez, A, Dunning, AM, Falchetti, A, Karczmarewicz, E, Kruk, M, van Leeuwen, Hans, van Meurs, Joyce, Mangion, J, McGuigan, FEA, Mellibovsky, L, del Monte, F, Pols, Huib, Reeve, J, Reid, DM, Renner, W, Rivadeneira, Fernando, van Schoor, NM, Sherlock, RE, Ioannidis, JPA, and Internal Medicine
Published: 2006

45. Accuracy of anti-ribosomal P protein antibody testing for the diagnosis of neuropsychiatric systemic lupus erythematosus - An international meta-analysis

Author: Karassa, FB Afeltra, A Ambrozic, A Chang, DM De Keyser, F Doria, A Galeazzi, M Hirohata, S Hoffman, IEA and Inanc, M Massardo, L Mathieu, A Mok, CC Morozzi, G and Sanna, G Spindler, AJ Tzioufas, AG Yoshio, T Ioannidis, JPA
Abstract: Objective. To quantitatively evaluate the diagnostic accuracy of antibodies to ribosomal P proteins (anti-P) for neuropsychiatric systemic lupus erythematosus (NPSLE) in general, for psychosis, mood disorder, or both, and for other diffuse manifestations. Methods. This international meta-analysis combined standardized data from 1,537 lupus patients contributed by 14 research teams. Weighted estimation of sensitivity and specificity with fixed-effects and random-effects models, as well as summary receiver operating characteristic (SROC) curve analysis, was used to summarize test performance. The robustness of the overall estimates was examined in sensitivity analyses that included additional studies published up to November 1, 2004 in the Medline, EMBase, and Cochrane databases. Results. Combining the data from the 14 teams, the weighted sensitivity and specificity estimates for the diagnosis of NPSLE were 26% (95% confidence interval [95% CI] 15-42%) and 80% (95% CI 74-85%), respectively. For psychosis, mood disorder, or both, the sensitivity and specificity were 27% (95% CI 14-47%) and 80% (95% CI 74-85%), respectively. For other diffuse manifestations, the sensitivity was 24% (95% CI 12-42%), and the specificity was 80% (95% CI 73-85%). The proportion of patients with anti-P antibodies did not vary markedly across different presentations of NPSLE. Between-study heterogeneity was substantial, but the SROC curves were consistent with the weighted estimates. In further analyses that included another 24 published studies, only the sensitivity for psychosis and/or mood disorder was slightly improved, but it was still suboptimal (42% [95% Cl 30-53%]); the specificity remained essentially the same (81% [95% Cl 76-85%]). Conclusion. Anti-P antibody testing has limited diagnostic value for NPSLE, and it is not helpful in differentiating among various disease phenotypes.
Published: 2006

46. Mortality in systemic sclerosis: an international meta-analysis of individual patient data

Author: Ioannidis, JPA Vlachoyiannopoulos, PG Haidich, AB Medsger, TA Lucas, M Michet, CJ Kuwana, M Yasuoka, H van den Hoogen, F Boome, LT van Laar, JM Verbeet, NL and Matucci-Cerinic, M Georgountzos, A Moutsopoulos, HM
Abstract: Purpose: Studies on mortality associated with systemic sclerosis have been limited by small sample sizes. We aimed to obtain large-scale evidence on survival outcomes and predictors for this disease. Methods: We performed a meta-analysis of individual patient data from cohorts recruited from seven medical centers in the United States, Europe, and Japan, using standardized definitions for disease subtype and organ system involvement. ne primary outcome was all- cause mortality. Standardized mortality ratios and predictors of mortality were estimated. The main analysis was based only on patients enrolled at each center within 6 months of diagnosis (incident cases). Results: Among 1645 incident cases, 578 deaths occurred over 11,521 person-years of follow-up. mortality ratios varied by cohort.(1.5 to 7.2). In multivariate analyses that adjusted for age and Standardized m sex, renal (hazard ratio [HR] = 1.9; 95% confidence interval [CI]: 1.4 to 2.5), cardiac (HR = 2.8; 95% CI: 2.1 to 3.8), and pulmonary (HR = 1.6; 95% CI: 1.3 to 2.2) involvement, and anti-topoisomerase I antibodies (HR = 1.3; 95% CI: 1.0 to 1.6), increased mortality risk. Renal, cardiac, and pulmonary involvement tended to occur together (P < 0.001). For patients without adverse predictors for 3 years after enrollment, the subsequent risk of death was not significantly different from that for the general population in three cohorts, but was significantly increased in three cohorts that comprised mostly referred patients. Analysis that included all cases in each center (n = 3311; total follow-up: 19,990 person-years) yielded larsel similar results. Conclusion: Systemic sclerosis confers a high mortality risk. but there is considerable heterogeneity across settings. Internal organ involvement and anti-topoisomerase I antibodies are important determinants of mortality. 0 2005 Elsevier Inc. All rights reserved.
Published: 2005

47. Clinicopathologic predictors of death and ESRD in patients with pauci-immune necrotizing glomerulonephritis

Author: Kapitsinou, PP Ioannidis, JPA Boletis, JN Sotsiou, F and Nakopoulou, L Daphnis, E Moutsopoulos, HM
Subjects: urologic and male genital diseases
Abstract: Background: Pauci-immune necrotizing glomerulonephritis (PING) occurs in various settings and has a very variable prognosis. We investigated whether clinicopathologic findings at the time of renal biopsy may predict major disease outcomes. Methods: We evaluated 72 consecutive patients with biopsy-documented PING. Kaplan-Meier curves and Cox models assessed event rates and risk factors for death, end-stage renal disease (ESRD), and death or new ESRD (after the renal biopsy). Results: During a follow-up of 305 person-years, 11 patients died, 13 patients developed ESRD, and 16 patients died or developed new ESRD. Among patients first seen within 3 months of renal biopsy (incident cases), the 5-year mortality rate was 20%, whereas the death or new ESRD rate was 34%. In univariate analyses, older age, lower creatinine clearance, erythrocyte sedimentation rate, and percentages of abnormal glomeruli, glomeruli with fibrous crescents, and glomeruli with global sclerosis were significant predictors of mortality, whereas antineutrophil cytoplasmic autoantibodies with cytoplasmic staining conferred borderline protection. For ESRD, significant predictors included a greater creatinine level, lower hematocrit, interstitial fibrosis, tubular necrosis, greater C-reactive protein level, and percentages of abnormal glomeruli, glomeruli with extracapillary proliferation, cellular crescents, and global glomerulosclerosis. For death or new ESRD, predictors were fairly similar. Adjusting for baseline creatinine level, the risk for ESRD increased 1.78-fold (95% confidence interval [CI], 1.23 to 2.58) per each 10% increase in global sclerosis and 1.47-fold (95% CI, 1.05 to 2.07) per each 10% increase in glomeruli with cellular crescents. Conclusion: Global glomerulosclerosis and crescents in a renal biopsy are strong predictors of the long-term outcome of PING.
Published: 2003

48. Evaluation of the association of autoantibodies with mortality in the very elderly: a cohort study

Author: Ioannidis, JPA Katsifis, GE Stavropoulos, ED Manoussakis, MN and Moutsopoulos, HM
Abstract: Objective. To evaluate whether autoantibodies in the absence of rheumatic diseases increase the risk of mortality among very elderly subjects who are otherwise in good functional condition. Methods. Autoantibodies were measured in 1987 in 156 elderly nursing home residents (median age 84 yr) who were followed subsequently over 14.6 yr. Results. Eleven subjects had anticardiolipin antibodies, 30 had rheumatoid factor and 19 had antibodies to single-stranded DNA (ssDNA). Other autoantibodies were more rare. During follow-up, 144 subjects died. Adjusting for age as a time-dependent covariate, the hazard ratio for death was 0.71 [95% confidence interval (0) 0.38-1.32] for anticardiolipin antibodies, 0.93 (95% CI 0.60-1.41) for rheumatoid factor, 1.08 (95% CI 0.65-1.79) for antibodies to ssDNA, and 0.99 (95% CI, 0.70-1.41) for any autoantibody. Hazard ratios were similar when adjusted also for sex and clinical conditions. Conclusion. Our results exclude the possibility that the autoantibodies evaluated increase substantially the risk of death among very elderly subjects in good functional condition.
Published: 2003

49. Atherosclerosis in premenopausal women with antiphospholipid syndrome and systemic lupus erythematosus: a controlled study

Author: Vlachoyiannopoulos, PG Kanellopoulos, PG Ioannidis, JPA and Tektonidou, MG Mastorakou, I Moutsopoulos, HM
Abstract: Objective. To evaluate whether premenopausal women with antiphospholipid syndrome (APS) or systemic lupus erythematosus (SLE) have increased prevalence of atherosclerosis after adjustment has been made for known cardiovascular risk factors. Methods. We evaluated premenopausal women with APS in comparison with age-matched groups of patients with SLE [positive or negative for anticardiolipin (aCL) antibodies] or rheumatoid arthritis (RA), and healthy subjects. Thirty-three subjects in each group were assessed for cardiovascular risk factors, including a detailed lipid profile. Ultrasonography of carotid and femoral arteries assessed the intima-media thickness (IMT) and the presence of atherosclerotic plaque. Results. Atherosclerotic plaques were detected in 5, 2, 4, 1 and 1 subject in the five groups respectively. APS patients had significantly more affected vessels than RA patients and healthy controls (P=0.042 and P=0.016, respectively), but not compared with SLE patients. No consistent differences in IMT, traditional cardiovascular risk factors or lipid parameters were detected among the five groups. The odds for atherosclerosis independently increased 1.19-fold per year of increasing age [95% confidence interval (CI) 1.08-1.31; P=0.001), 1.019-fold per 1 mg/dl increase in low-density lipoprotein (LDL) (95% CI 1.003-1.036; P=0.020), 1.035-fold per additional 1 g of methylprednisolone equivalent cumulative corticosteroid dose (95% CI, 0.996-1.074; P=0.074), and 4.35-fold in the presence of APS or SLE (95% CI 0.75-25.2; P=0.10). Neither aCL nor anti-beta(2)GPI antibodies were associated with atherosclerosis. Conclusion. Premenopausal APS and SLE women have an increased prevalence of carotid and femoral plaque that is not accounted for by other predictors of atherosclerosis, including age, lipid parameters and cumulative steroid dose.
Published: 2003

50. Evaluation of Excess Significance Bias in Animal Studies of Neurological Diseases

Author: Bero, L, Tsilidis, KK, Panagiotou, OA, Sena, ES, Aretouli, E, Evangelou, E, Howells, DW, Salman, RA-S, Macleod, MR, Ioannidis, JPA, Bero, L, Tsilidis, KK, Panagiotou, OA, Sena, ES, Aretouli, E, Evangelou, E, Howells, DW, Salman, RA-S, Macleod, MR, and Ioannidis, JPA
Abstract: Animal studies generate valuable hypotheses that lead to the conduct of preventive or therapeutic clinical trials. We assessed whether there is evidence for excess statistical significance in results of animal studies on neurological disorders, suggesting biases. We used data from meta-analyses of interventions deposited in Collaborative Approach to Meta-Analysis and Review of Animal Data in Experimental Studies (CAMARADES). The number of observed studies with statistically significant results (O) was compared with the expected number (E), based on the statistical power of each study under different assumptions for the plausible effect size. We assessed 4,445 datasets synthesized in 160 meta-analyses on Alzheimer disease (n = 2), experimental autoimmune encephalomyelitis (n = 34), focal ischemia (n = 16), intracerebral hemorrhage (n = 61), Parkinson disease (n = 45), and spinal cord injury (n = 2). 112 meta-analyses (70%) found nominally (p≤0.05) statistically significant summary fixed effects. Assuming the effect size in the most precise study to be a plausible effect, 919 out of 4,445 nominally significant results were expected versus 1,719 observed (p<10⁻⁹). Excess significance was present across all neurological disorders, in all subgroups defined by methodological characteristics, and also according to alternative plausible effects. Asymmetry tests also showed evidence of small-study effects in 74 (46%) meta-analyses. Significantly effective interventions with more than 500 animals, and no hints of bias were seen in eight (5%) meta-analyses. Overall, there are too many animal studies with statistically significant results in the literature of neurological disorders. This observation suggests strong biases, with selective analysis and outcome reporting biases being plausible explanations, and provides novel evidence on how these biases might influence the whole research domain of neurological animal literature.
Published: 2013

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