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3,932 results on '"Cole, Stephen"'

2. Investigating symptom duration using current status data: a case study of post-acute COVID-19 syndrome

Author

Wolock, Charles J., Jacob, Susan, Bennett, Julia C., Elias-Warren, Anna, O'Hanlon, Jessica, Kenny, Avi, Jewell, Nicholas P., Rotnitzky, Andrea, Cole, Stephen R., Weil, Ana A., Chu, Helen Y., and Carone, Marco

Subjects
Statistics - Applications, Statistics - Methodology
Abstract

For infectious diseases, characterizing symptom duration is of clinical and public health importance. Symptom duration may be assessed by surveying infected individuals and querying symptom status at the time of survey response. For example, in a SARS-CoV-2 testing program at the University of Washington, participants were surveyed at least 28 days after testing positive and asked to report current symptom status. This study design yielded current status data: outcome measurements for each respondent consisted only of the time of survey response and a binary indicator of whether symptoms had resolved by that time. Such study design benefits from limited risk of recall bias, but analyzing the resulting data necessitates tailored statistical tools. Here, we review methods for current status data and describe a novel application of modern nonparametric techniques to this setting. The proposed approach is valid under weaker assumptions compared to existing methods, allows use of flexible machine learning tools, and handles potential survey nonresponse. From the university study, we estimate that 19% of participants experienced ongoing symptoms 30 days after testing positive, decreasing to 7% at 90 days. Female sex, history of seasonal allergies, fatigue during acute infection, and higher viral load were associated with slower symptom resolution., Comment: The first two authors contributed equally to this work. Main text: 20 pages, 1 figure, 4 tables. Supplement: 14 pages, 8 figures, 0 tables

Published
2024

3. Double Robust Variance Estimation

Author

Shook-Sa, Bonnie E., Zivich, Paul N., Lee, Chanhwa, Xue, Keyi, Ross, Rachael K., Edwards, Jessie K., Stringer, Jeffrey S. A., and Cole, Stephen R.

Subjects
Statistics - Methodology, Statistics - Applications
Abstract

Doubly robust estimators have gained popularity in the field of causal inference due to their ability to provide consistent point estimates when either an outcome or exposure model is correctly specified. However, the influence function based variance estimator frequently used with doubly robust estimators is only consistent when both the outcome and exposure models are correctly specified. Here, use of M-estimation and the empirical sandwich variance estimator for doubly robust point and variance estimation is demonstrated. Simulation studies illustrate the properties of the influence function based and empirical sandwich variance estimators. Estimators are applied to data from the Improving Pregnancy Outcomes with Progesterone (IPOP) trial to estimate the effect of maternal anemia on birth weight among women with HIV. In the example, birth weights if all women had anemia were estimated to be lower than birth weights if no women had anemia, though estimates were imprecise. Variance estimates were more stable under varying model specifications for the empirical sandwich variance estimator than the influence function based variance estimator., Comment: 19 pages, 5 figures, 6 tables

Published
2024

4. Finite sample performance of optimal treatment rule estimators with right-censored outcomes

Author

Jetsupphasuk, Michael, Hudgens, Michael G., Edwards, Jessie K., and Cole, Stephen R.

Subjects
Statistics - Methodology
Abstract

Patient care may be improved by recommending treatments based on patient characteristics when there is treatment effect heterogeneity. Recently, there has been a great deal of attention focused on the estimation of optimal treatment rules that maximize expected outcomes. However, there has been comparatively less attention given to settings where the outcome is right-censored, especially with regard to the practical use of estimators. In this study, simulations were undertaken to assess the finite-sample performance of estimators for optimal treatment rules and estimators for the expected outcome under treatment rules. The simulations were motivated by the common setting in biomedical and public health research where the data is observational, survival times may be right-censored, and there is interest in estimating baseline treatment decisions to maximize survival probability. A variety of outcome regression and direct search estimation methods were compared for optimal treatment rule estimation across a range of simulation scenarios. Methods that flexibly model the outcome performed comparatively well, including in settings where the treatment rule was non-linear. R code to reproduce this study's results are available on Github.

Published
2024

5. Synthesis estimators for positivity violations with a continuous covariate

Author

Zivich, Paul N, Edwards, Jessie K, Shook-Sa, Bonnie E, Lofgren, Eric T, Lessler, Justin, and Cole, Stephen R

Subjects
Statistics - Methodology
Abstract

Studies intended to estimate the effect of a treatment, like randomized trials, may not be sampled from the desired target population. To correct for this discrepancy, estimates can be transported to the target population. Methods for transporting between populations are often premised on a positivity assumption, such that all relevant covariate patterns in one population are also present in the other. However, eligibility criteria, particularly in the case of trials, can result in violations of positivity when transporting to external populations. To address nonpositivity, a synthesis of statistical and mathematical models can be considered. This approach integrates multiple data sources (e.g. trials, observational, pharmacokinetic studies) to estimate treatment effects, leveraging mathematical models to handle positivity violations. This approach was previously demonstrated for positivity violations by a single binary covariate. Here, we extend the synthesis approach for positivity violations with a continuous covariate. For estimation, two novel augmented inverse probability weighting estimators are proposed. Both estimators are contrasted with other common approaches for addressing nonpositivity. Empirical performance is compared via Monte Carlo simulation. Finally, the competing approaches are illustrated with an example in the context of two-drug versus one-drug antiretroviral therapy on CD4 T cell counts among women with HIV.

Published
2023

8. The Metropolis algorithm: A useful tool for epidemiologists

Author

Keil, Alexander P, Edwards, Jessie K, Naimi, Ashley I, and Cole, Stephen R

Subjects
Statistics - Computation, I.6.3, I.6.38
Abstract

The Metropolis algorithm is a Markov chain Monte Carlo (MCMC) algorithm used to simulate from parameter distributions of interest, such as generalized linear model parameters. The "Metropolis step" is a keystone concept that underlies classical and modern MCMC methods and facilitates simple analysis of complex statistical models. Beyond Bayesian analysis, MCMC is useful for generating uncertainty intervals, even under the common scenario in causal inference in which the target parameter is not directly estimated by a single, fitted statistical model. We demonstrate, with a worked example, pseudo-code, and R code, the basic mechanics of the Metropolis algorithm. We use the Metropolis algorithm to estimate the odds ratio and risk difference contrasting the risk of childhood leukemia among those exposed to high versus low level magnetic fields. This approach can be used for inference from Bayesian and frequentist paradigms and, in small samples, offers advantages over large-sample methods like the bootstrap., Comment: 26 pages, 3 figures

Published
2023

9. Empirical sandwich variance estimator for iterated conditional expectation g-computation

Author

Zivich, Paul N, Ross, Rachael K, Shook-Sa, Bonnie E, Cole, Stephen R, and Edwards, Jessie K

Subjects
Statistics - Methodology, Statistics - Computation
Abstract

Iterated conditional expectation (ICE) g-computation is an estimation approach for addressing time-varying confounding for both longitudinal and time-to-event data. Unlike other g-computation implementations, ICE avoids the need to specify models for each time-varying covariate. For variance estimation, previous work has suggested the bootstrap. However, bootstrapping can be computationally intense. Here, we present ICE g-computation as a set of stacked estimating equations. Therefore, the variance for the ICE g-computation estimator can be consistently estimated using the empirical sandwich variance estimator. Performance of the variance estimator was evaluated empirically with a simulation study. The proposed approach is also demonstrated with an illustrative example on the effect of cigarette smoking on the prevalence of hypertension. In the simulation study, the empirical sandwich variance estimator appropriately estimated the variance. When comparing runtimes between the sandwich variance estimator and the bootstrap for the applied example, the sandwich estimator was substantially faster, even when bootstraps were run in parallel. The empirical sandwich variance estimator is a viable option for variance estimation with ICE g-computation., Comment: 18 pages, 1 figure, 6 tables

Published
2023

10. A Causal Inference Framework for Leveraging External Controls in Hybrid Trials

Author

Valancius, Michael, Pang, Herb, Zhu, Jiawen, Cole, Stephen R, Funk, Michele Jonsson, and Kosorok, Michael R

Subjects
Statistics - Methodology, Statistics - Machine Learning
Abstract

We consider the challenges associated with causal inference in settings where data from a randomized trial is augmented with control data from an external source to improve efficiency in estimating the average treatment effect (ATE). Through the development of a formal causal inference framework, we outline sufficient causal assumptions about the exchangeability between the internal and external controls to identify the ATE and establish the connection to a novel graphical criteria. We propose estimators, review efficiency bounds, develop an approach for efficient doubly-robust estimation even when unknown nuisance models are estimated with flexible machine learning methods, and demonstrate finite-sample performance through a simulation study. To illustrate the ideas and methods, we apply the framework to a trial investigating the effect of risdisplam on motor function in patients with spinal muscular atrophy for which there exists an external set of control patients from a previous trial.

Published
2023

11. Fusing Trial Data for Treatment Comparisons: Single versus Multi-Span Bridging

Author

Shook-Sa, Bonnie E., Zivich, Paul N., Rosin, Samuel P., Edwards, Jessie K., Adimora, Adaora A., Hudgens, Michael G., and Cole, Stephen R.

Subjects
Statistics - Applications, Statistics - Methodology
Abstract

While randomized controlled trials (RCTs) are critical for establishing the efficacy of new therapies, there are limitations regarding what comparisons can be made directly from trial data. RCTs are limited to a small number of comparator arms and often compare a new therapeutic to a standard of care which has already proven efficacious. It is sometimes of interest to estimate the efficacy of the new therapy relative to a treatment that was not evaluated in the same trial, such as a placebo or an alternative therapy that was evaluated in a different trial. Such multi-study comparisons are challenging because of potential differences between trial populations that can affect the outcome. In this paper, two bridging estimators are considered that allow for comparisons of treatments evaluated in different trials using data fusion methods to account for measured differences in trial populations. A "multi-span'' estimator leverages a shared arm between two trials, while a "single-span'' estimator does not require a shared arm. A diagnostic statistic that compares the outcome in the standardized shared arms is provided. The two estimators are compared in simulations, where both estimators demonstrate minimal empirical bias and nominal confidence interval coverage when the identification assumptions are met. The estimators are applied to data from the AIDS Clinical Trials Group 320 and 388 to compare the efficacy of two-drug versus four-drug antiretroviral therapy on CD4 cell counts among persons with advanced HIV. The single-span approach requires fewer identification assumptions and was more efficient in simulations and the application.

Published
2023

12. Prevalence of antibiotic use for dogs and cats in United States veterinary teaching hospitals, August 2020.

Author

Beaudoin, Amanda L, Bollig, Emma R, Burgess, Brandy A, Cohn, Leah A, Cole, Stephen D, Dear, Jonathan D, Fellman, Claire L, Frey, Erin, Goggs, Robert, Johnston, Andrea, Kreuder, Amanda J, KuKanich, Kate S, LeCuyer, Tessa E, Menard, Julie, Reagan, Krystle L, Sykes, Jane E, Veir, Julia K, Viviano, Katrina, Wayne, Annie, and Granick, Jennifer L

Subjects
antibiotic indication, antibiotic measurement, antibiotic prophylaxis, antibiotic resistance, antibiotic stewardship, cats, dogs, Infectious Diseases, Infection, Veterinary Sciences
Abstract

BackgroundAwareness of prescribing practices helps identify opportunities to improve antibiotic use (AU).ObjectivesTo estimate AU prevalence in dogs and cats in U.S. veterinary teaching hospitals (VTHs) and identify antibiotic drugs commonly prescribed, indications for use, and evidence of bacterial infection.AnimalsMedical record data were collected from dogs and cats examined at 14 VTHs.MethodsData were collected from VTH medical records of dogs and cats examined by primary care, urgent care, emergency and critical care, internal medicine, and surgery services on a single day during August 13-September 3, 2020. Data included signalment; clinical service; inpatient or outpatient status; clinical conditions; diagnostic tests; evidence of bacterial infection; intended reason for AU; name and route of antibiotics prescribed.ResultsOf 883 dogs and cats, 322 (36.5%) were prescribed at least 1 antibiotic. Among 285 antibiotics administered systemically intended for treatment of infection, 10.9% were prescribed without evidence of infection. The most common class of antibiotics presribed for systemic administration was potentiated penicillin for dogs (115/346, 33.3%) and cats (27/80, 33.8%). For dogs and cats, first-generation cephalosporins (93/346, 26.9% and 11/80, 13.8%, respectively) and fluoroquinolones (51/346, 14.7% and 19/80, 23.8%, respectively) was second or third most-prescribed. Common AU indications included skin, respiratory, and urinary conditions, and perioperative use.Conclusions and clinical importanceCollaborative data collection provides a sustainable methodology to generate national AU prevalence estimates and bring attention to areas requiring additional research and detailed data collection. These efforts can also identify practice improvement opportunities in settings where future veterinarians are trained.

Published
2023

13. Transportability without positivity: a synthesis of statistical and simulation modeling

Author

Zivich, Paul N, Edwards, Jessie K, Lofgren, Eric T, Cole, Stephen R, Shook-Sa, Bonnie E, and Lessler, Justin

Subjects
Statistics - Methodology
Abstract

When estimating an effect of an action with a randomized or observational study, that study is often not a random sample of the desired target population. Instead, estimates from that study can be transported to the target population. However, transportability methods generally rely on a positivity assumption, such that all relevant covariate patterns in the target population are also observed in the study sample. Strict eligibility criteria, particularly in the context of randomized trials, may lead to violations of this assumption. Two common approaches to address positivity violations are restricting the target population and restricting the relevant covariate set. As neither of these restrictions are ideal, we instead propose a synthesis of statistical and simulation models to address positivity violations. We propose corresponding g-computation and inverse probability weighting estimators. The restriction and synthesis approaches to addressing positivity violations are contrasted with a simulation experiment and an illustrative example in the context of sexually transmitted infection testing uptake. In both cases, the proposed synthesis approach accurately addressed the original research question when paired with a thoughtfully selected simulation model. Neither of the restriction approaches were able to accurately address the motivating question. As public health decisions must often be made with imperfect target population information, model synthesis is a viable approach given a combination of empirical data and external information based on the best available knowledge.

Published
2023
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14. Higher-order evidence

Author

Cole, Stephen R., Shook-Sa, Bonnie E., Zivich, Paul N., Edwards, Jessie K., Richardson, David B., and Hudgens, Michael G.

Published
2024
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15. Positivity: Identifiability and Estimability

Author

Zivich, Paul N, Cole, Stephen R, and Westreich, Daniel

Subjects
Statistics - Methodology
Abstract

Positivity, the assumption that every unique combination of confounding variables that occurs in a population has a non-zero probability of an action, can be further delineated as deterministic positivity and stochastic positivity. Here, we revisit this distinction, examine its relation to nonparametric identifiability and estimability, and discuss how to address violations of positivity assumptions. Finally, we relate positivity to recent interest in machine learning, as well as the limitations of data-adaptive algorithms for causal inference. Positivity may often be overlooked, but it remains important for inference., Comment: 6 pages

Published
2022

16. Bridged treatment comparisons: an illustrative application in HIV treatment

Author

Zivich, Paul N, Cole, Stephen R, Edwards, Jessie K, Shook-Sa, Bonnie E, Breskin, Alexander, and Hudgens, Michael G

Subjects
Statistics - Methodology
Abstract

Comparisons of treatments, interventions, or exposures are of central interest in epidemiology, but direct comparisons are not always possible due to practical or ethical reasons. Here, we detail a fusion approach to compare treatments across studies. The motivating example entails comparing the risk of the composite outcome of death, AIDS, or greater than a 50% CD4 cell count decline in people with HIV when assigned triple versus mono antiretroviral therapy, using data from the AIDS Clinical Trial Group (ACTG) 175 (mono versus dual therapy) and ACTG 320 (dual versus triple therapy). We review a set of identification assumptions and estimate the risk difference using an inverse probability weighting estimator that leverages the shared trial arms (dual therapy). A fusion diagnostic based on comparing the shared arms is proposed that may indicate violation of the identification assumptions. Application of the data fusion estimator and diagnostic to the ACTG trials indicates triple therapy results in a reduction in risk compared to monotherapy in individuals with baseline CD4 counts between 50 and 300 cells/mm$^3$. Bridged treatment comparisons address questions that none of the constituent data sources could address alone, but valid fusion-based inference requires careful consideration of the underlying assumptions., Comment: 21 pages, 3 figures, 5 tables

Published
2022

17. Delicatessen: M-Estimation in Python

Author

Zivich, Paul N, Klose, Mark, Cole, Stephen R, Edwards, Jessie K, and Shook-Sa, Bonnie E

Subjects
Statistics - Methodology, Statistics - Computation
Abstract

M-estimation is a general statistical framework that simplifies estimation. Here, we introduce delicatessen, a Python library that automates the tedious calculations of M-estimation, and supports both built-in user-specified estimating equations. To highlight the utility of delicatessen for quantitative data analysis, we provide several illustrations common to life science research: linear regression robust to outliers, estimation of a dose-response curve, and standardization of results., Comment: 1 figure

Published
2022

18. Exposure Effects on Count Outcomes with Observational Data, with Application to Incarcerated Women

Author

Shook-Sa, Bonnie E., Hudgens, Michael G., Knittel, Andrea K., Edmonds, Andrew, Ramirez, Catalina, Cole, Stephen R., Cohen, Mardge, Adedimeji, Adebola, Taylor, Tonya, Michel, Katherine G., Kovacs, Andrea, Cohen, Jennifer, Donohue, Jessica, Foster, Antonina, Fischl, Margaret A., Long, Dustin, and Adimora, Adaora A.

Subjects
Statistics - Methodology, Statistics - Applications
Abstract

Causal inference methods can be applied to estimate the effect of a point exposure or treatment on an outcome of interest using data from observational studies. For example, in the Women's Interagency HIV Study, it is of interest to understand the effects of incarceration on the number of sexual partners and the number of cigarettes smoked after incarceration. In settings like this where the outcome is a count, the estimand is often the causal mean ratio, i.e., the ratio of the counterfactual mean count under exposure to the counterfactual mean count under no exposure. This paper considers estimators of the causal mean ratio based on inverse probability of treatment weights, the parametric g-formula, and doubly robust estimation, each of which can account for overdispersion, zero-inflation, and heaping in the measured outcome. Methods are compared in simulations and are applied to data from the Women's Interagency HIV Study.

Published
2022

19. Stratified analyses refine association between TLR7 rare variants and severe COVID-19

Author

Rimoldi, Valeria, Paraboschi, Elvezia M., Bandera, Alessandra, Peyvandi, Flora, Grasselli, Giacomo, Blasi, Francesco, Malvestiti, Francesco, Pelusi, Serena, Bianco, Cristiana, Miano, Lorenzo, Lombardi, Angela, Invernizzi, Pietro, Gerussi, Alessio, Citerio, Giuseppe, Biondi, Andrea, Valsecchi, Maria Grazia, Cazzaniga, Marina Elena, Foti, Giuseppe, Beretta, Ilaria, D'Angiò, Mariella, Bettini, Laura Rachele, Farré, Xavier, Iraola-Guzmán, Susana, Kogevinas, Manolis, Castaño-Vinyals, Gemma, Garcia-Etxebarria, Koldo, Nafria, Beatriz, D'Amato, Mauro, Palom, Adriana, Begg, Colin, Clohisey, Sara, Hinds, Charles, Horby, Peter, Knight, Julian, Ling, Lowell, Maslove, David, McAuley, Danny, Millar, Johnny, Montgomery, Hugh, Nichol, Alistair, Openshaw, Peter J.M., Pereira, Alexandre C., Ponting, Chris P., Rowan, Kathy, Semple, Malcolm G., Shankar-Hari, Manu, Summers, Charlotte, Walsh, Timothy, Baillie, J. Kenneth, Aravindan, Latha, Armstrong, Ruth, Biggs, Heather, Boz, Ceilia, Brown, Adam, Clark, Richard, Coutts, Audrey, Coyle, Judy, Cullum, Louise, Das, Sukamal, Day, Nicky, Donnelly, Lorna, Duncan, Esther, Fawkes, Angie, Fineran, Paul, Fourman, Max Head, Furlong, Anita, Furniss, James, Gallagher, Bernadette, Gilchrist, Tammy, Golightly, Ailsa, Griffiths, Fiona, Hafezi, Katarzyna, Hamilton, Debbie, Hendry, Ross, Law, Andy, Law, Dawn, Law, Rachel, Law, Sarah, Lidstone-Scott, Rebecca, Macgillivray, Louise, Maclean, Alan, Mal, Hanning, McCafferty, Sarah, Mcmaster, Ellie, Meikle, Jen, Moore, Shona C., Morrice, Kirstie, Murphy, Lee, Murphy, Sheena, Hellen, Mybaya, Oosthuyzen, Wilna, Zheng, Chenqing, Chen, Jiantao, Parkinson, Nick, Paterson, Trevor, Schon, Katherine, Stenhouse, Andrew, Das, Mihaela, Swets, Maaike, Szoor-McElhinney, Helen, Taneski, Filip, Turtle, Lance, Wackett, Tony, Ward, Mairi, Weaver, Jane, Wrobel, Nicola, Zechner, Marie, Arbane, Gill, Bociek, Aneta, Campos, Sara, Grau, Neus, Jones, Tim Owen, Lim, Rosario, Marotti, Martina, Ostermann, Marlies, Whitton, Christopher, Alldis, Zoe, Astin-Chamberlain, Raine, Bibi, Fatima, Biddle, Jack, Blow, Sarah, Bolton, Matthew, Borra, Catherine, Bowles, Ruth, Burton, Maudrian, Choudhury, Yasmin, Collier, David, Cox, Amber, Easthope, Amy, Ebano, Patrizia, Fotiadis, Stavros, Gurasashvili, Jana, Halls, Rosslyn, Hartridge, Pippa, Kallon, Delordson, Kassam, Jamila, Lancoma-Malcolm, Ivone, Matharu, Maninderpal, May, Peter, Mitchelmore, Oliver, Newman, Tabitha, Patel, Mital, Pheby, Jane, Pinzuti, Irene, Prime, Zoe, Prysyazhna, Oleksandra, Shiel, Julian, Taylor, Melanie, Tierney, Carey, Wood, Suzanne, Zak, Anne, Zongo, Olivier, Bonner, Stephen, Hugill, Keith, Jones, Jessica, Liggett, Steven, Headlam, Evie, Bandla, Nageswar, Gellamucho, Minnie, Davies, Michelle, Thompson, Christopher, Abdelrazik, Marwa, Bakthavatsalam, Dhanalakshmi, Elhassan, Munzir, Ganesan, Arunkumar, Haldeos, Anne, Moreno-Cuesta, Jeronimo, Purohit, Dharam, Vincent, Rachel, Xavier, Kugan, Rohit, Kumar, Alasdair, Frater, Saleem, Malik, David, Carter, Samuel, Jenkins, Lamond, Zoe, Alanna, Wall, Fernandez-Roman, Jaime, Hamilton, David O., Johnson, Emily, Johnston, Brian, Martinez, Maria Lopez, Mulla, Suleman, Shaw, David, Waite, Alicia A.C., Waugh, Victoria, Welters, Ingeborg D., Williams, Karen, Cavazza, Anna, Cockrell, Maeve, Corcoran, Eleanor, Depante, Maria, Finney, Clare, Jerome, Ellen, McPhail, Mark, Nayak, Monalisa, Noble, Harriet, O'Reilly, Kevin, Pappa, Evita, Saha, Rohit, Saha, Sian, Smith, John, Knighton, Abigail, Antcliffe, David, Banach, Dorota, Brett, Stephen, Coghlan, Phoebe, Fernandez, Ziortza, Gordon, Anthony, Rojo, Roceld, Arias, Sonia Sousa, Templeton, Maie, Meredith, Megan, Morris, Lucy, Ryan, Lucy, Clark, Amy, Sampson, Julia, Peters, Cecilia, Dent, Martin, Langley, Margaret, Ashraf, Saima, Wei, Shuying, Andrew, Angela, Bashyal, Archana, Davidson, Neil, Hutton, Paula, McKechnie, Stuart, Wilson, Jean, Baptista, David, Crowe, Rebecca, Fernandes, Rita, Herdman-Grant, Rosaleen, Joseph, Anna, O'Connor, Denise, Allen, Meryem, Loveridge, Adam, McKenley, India, Morino, Eriko, Naranjo, Andres, Simms, Richard, Sollesta, Kathryn, Swain, Andrew, Venkatesh, Harish, Khera, Jacyntha, Fox, Jonathan, Andrew, Gillian, Barclay, Lucy, Callaghan, Marie, Campbell, Rachael, Clark, Sarah, Hope, Dave, Marshall, Lucy, McCulloch, Corrienne, Briton, Kate, Singleton, Jo, Birch, Sohphie, Brimfield, Lutece, Daly, Zoe, Pogson, David, Rose, Steve, Battle, Ceri, Brinkworth, Elaine, Harford, Rachel, Murphy, Carl, Newey, Luke, Rees, Tabitha, Williams, Marie, Arnold, Sophie, Polgarova, Petra, Stroud, Katerina, Meaney, Eoghan, Jones, Megan, Ng, Anthony, Agrawal, Shruti, Pathan, Nazima, White, Deborah, Daubney, Esther, Elston, Kay, Grauslyte, Lina, Hussain, Musarat, Phull, Mandeep, Pogreban, Tatiana, Rosaroso, Lace, Salciute, Erika, Franke, George, Wong, Joanna, George, Aparna, Ortiz-Ruiz de Gordoa, Laura, Peasgood, Emily, Phillips, Claire, Bates, Michelle, Dasgin, Jo, Gill, Jaspret, Nilsson, Annette, Scriven, James, Delgado, Carlos Castro, Dawson, Deborah, Ding, Lijun, Durrant, Georgia, Ezeobu, Obiageri, Farnell-Ward, Sarah, Harrison, Abiola, Kanu, Rebecca, Leaver, Susannah, Maccacari, Elena, Manna, Soumendu, Saluzzio, Romina Pepermans, Queiroz, Joana, Samakomva, Tinashe, Sicat, Christine, Texeira, Joana, Da Gloria, Edna Fernandes, Lisboa, Ana, Rawlins, John, Mathew, Jisha, Kinch, Ashley, Hurt, William James, Shah, Nirav, Clark, Victoria, Thanasi, Maria, Yun, Nikki, Patel, Kamal, Bennett, Sara, Goodwin, Emma, Jackson, Matthew, Kent, Alissa, Tibke, Clare, Woodyatt, Wiesia, Zaki, Ahmed, Abraheem, Azmerelda, Bamford, Peter, Cawley, Kathryn, Dunmore, Charlie, Faulkner, Maria, Girach, Rumanah, Jeffrey, Helen, Jones, Rhianna, London, Emily, Nagra, Imrun, Nasir, Farah, Sainsbury, Hannah, Smedley, Clare, Patel, Tahera, Smith, Matthew, Chukkambotla, Srikanth, Kazi, Aayesha, Hartley, Janice, Dykes, Joseph, Hijazi, Muhammad, Keith, Sarah, Khan, Meherunnisa, Ryan-Smith, Janet, Springle, Philippa, Thomas, Jacqueline, Truman, Nick, Saad, Samuel, Coleman, Dabheoc, Fine, Christopher, Matt, Roseanna, Gay, Bethan, Dalziel, Jack, Ali, Syamlan, Goodchild, Drew, Harling, Rhiannan, Bhatterjee, Ravi, Goddard, Wendy, Davison, Chloe, Duberly, Stephen, Hargreaves, Jeanette, Bolton, Rachel, Davey, Miriam, Golden, David, Seaman, Rebecca, Cherian, Shiney, Cutler, Sean, Heron, Anne Emma, Roynon-Reed, Anna, Szakmany, Tamas, Williams, Gemma, Richards, Owen, Cheema, Yusuf, Brooke, Hollie, Buckley, Sarah, Suarez, Jose Cebrian, Charlesworth, Ruth, Hansson, Karen, Norris, John, Poole, Alice, Rose, Alastair, Sandhu, Rajdeep, Sloan, Brendan, Smithson, Elizabeth, Thirumaran, Muthu, Wagstaff, Veronica, Metcalfe, Alexandra, Brunton, Mark, Caterson, Jess, Coles, Holly, Frise, Matthew, Rai, Sabi Gurung, Jacques, Nicola, Keating, Liza, Tilney, Emma, Bartley, Shauna, Bhuie, Parminder, Gibson, Sian, Lyle, Amanda, McNeela, Fiona, Radhakrishnan, Jayachandran, Hughes, Alistair, Yates, Bryan, Reynolds, Jessica, Campbell, Helen, Thompsom, Maria, Dodds, Steve, Duffy, Stacey, Greer, Sandra, Shuker, Karen, Tridente, Ascanio, Khade, Reena, Sundar, Ashok, Tsinaslanidis, George, Birkinshaw, Isobel, Carter, Joseph, Howard, Kate, Ingham, Joanne, Joy, Rosie, Pearson, Harriet, Roche, Samantha, Scott, Zoe, Bancroft, Hollie, Bellamy, Mary, Carmody, Margaret, Daglish, Jacqueline, Moore, Faye, Rhodes, Joanne, Sangombe, Mirriam, Kadiri, Salma, Croft, Maria, White, Ian, Frost, Victoria, Aquino, Maia, Jha, Rajeev, Krishnamurthy, Vinodh, Lim, Lai, Lim, Li, Combes, Edward, Joefield, Teishel, Monnery, Sonja, Beech, Valerie, Trotman, Sallyanne, Almaden-Boyle, Christine, Austin, Pauline, Cabrelli, Louise, Cole, Stephen, Casey, Matt, Chapman, Susan, Whyte, Clare, Baird, Yolanda, Butler, Aaron, Chadbourn, Indra, Folkes, Linda, Fox, Heather, Gardner, Amy, Gomez, Raquel, Hobden, Gillian, Hodgson, Luke, King, Kirsten, Margarson, Michael, Martindale, Tim, Meadows, Emma, Raynard, Dana, Thirlwall, Yvette, Helm, David, Margalef, Jordi, Criste, Kristine, Cusack, Rebecca, Golder, Kim, Golding, Hannah, Jones, Oliver, Leggett, Samantha, Male, Michelle, Marani, Martyna, Prager, Kirsty, Williams, Toran, Roberts, Belinda, Salmon, Karen, Anderson, Peter, Archer, Katie, Austin, Karen, Davis, Caroline, Durie, Alison, Kelsall, Olivia, Thrush, Jessica, Vigurs, Charlie, Wild, Laura, Wood, Hannah-Louise, Tranter, Helen, Harrison, Alison, Cowley, Nicholas, McAlindon, Michael, Burtenshaw, Andrew, Digby, Stephen, Low, Emma, Morgan, Aled, Cother, Naiara, Rankin, Tobias, Clayton, Sarah, McCurdy, Alex, Ahmed, Cecilia, Baines, Balvinder, Clamp, Sarah, Colley, Julie, Haq, Risna, Hayes, Anne, Hulme, Jonathan, Hussain, Samia, Joseph, Sibet, Kumar, Rita, Maqsood, Zahira, Purewal, Manjit, Benham, Leonie, Bradshaw, Zena, Brown, Joanna, Caswell, Melanie, Cupitt, Jason, Melling, Sarah, Preston, Stephen, Slawson, Nicola, Stoddard, Emma, Warden, Scott, Deacon, Bethan, Lynch, Ceri, Pothecary, Carla, Roche, Lisa, Howe, Gwenllian Sera, Singh, Jayaprakash, Turner, Keri, Ellis, Hannah, Stroud, Natalie, Hunt, Jodie, Dearden, Joy, Dobson, Emma, Drummond, Andy, Mulcahy, Michelle, Munt, Sheila, O'Connor, Grainne, Philbin, Jennifer, Rishton, Chloe, Tully, Redmond, Winnard, Sarah, Cathcart, Susanne, Duffy, Katharine, Puxty, Alex, Puxty, Kathryn, Turner, Lynne, Ireland, Jane, Semple, Gary, Long, Kate, Whiteley, Simon, Wilby, Elizabeth, Ogg, Bethan, Cowton, Amanda, Kay, Andrea, Kent, Melanie, Potts, Kathryn, Wilkinson, Ami, Campbell, Suzanne, Brown, Ellen, Melville, Julie, Naisbitt, Jay, Joseph, Rosane, Lazo, Maria, Walton, Olivia, Neal, Alan, Alexander, Peter, Allen, Schvearn, Bradley-Potts, Joanne, Brantwood, Craig, Egan, Jasmine, Felton, Timothy, Padden, Grace, Ward, Luke, Moss, Stuart, Glasgow, Susannah, Abel, Lynn, Brett, Michael, Digby, Brian, Gemmell, Lisa, Hornsby, James, MacGoey, Patrick, O'Neil, Pauline, Price, Richard, Rodden, Natalie, Rooney, Kevin, Sundaram, Radha, Thomson, Nicola, Hopkins, Bridget, Thrasyvoulou, Laura, Willis, Heather, Clark, Martyn, Coulding, Martina, Jude, Edward, McCormick, Jacqueline, Mercer, Oliver, Potla, Darsh, Rehman, Hafiz, Savill, Heather, Turner, Victoria, Downes, Charlotte, Holding, Kathleen, Riches, Katie, Hilton, Mary, Hayman, Mel, Subramanian, Deepak, Daniel, Priya, Adanini, Oluronke, Bhatia, Nikhil, Msiska, Maines, Collins, Rebecca, Clement, Ian, Patel, Bijal, Gulati, A., Hays, Carole, Webster, K., Hudson, Anne, Webster, Andrea, Stephenson, Elaine, McCormack, Louise, Slater, Victoria, Nixon, Rachel, Hanson, Helen, Fearby, Maggie, Kelly, Sinead, Bridgett, Victoria, Robinson, Philip, Camsooksai, Julie, Humphrey, Charlotte, Jenkins, Sarah, Reschreiter, Henrik, Wadams, Beverley, Death, Yasmin, Bastion, Victoria, Clarke, Daphene, David, Beena, Kent, Harriet, Lorusso, Rachel, Lubimbi, Gamu, Murdoch, Sophie, Penacerrada, Melchizedek, Thomas, Alastair, Valentine, Jennifer, Vochin, Ana, Wulandari, Retno, Djeugam, Brice, Bell, Gillian, English, Katy, Katary, Amro, Wilcox, Louise, Bruce, Michelle, Connolly, Karen, Duncan, Tracy, T-Michael, Helen, Lindergard, Gabriella, Hey, Samuel, Fox, Claire, Alfonso, Jordan, Durrans, Laura Jayne, Guerin, Jacinta, Blackledge, Bethan, Harris, Jade, Hruska, Martin, Eltayeb, Ayaa, Lamb, Thomas, Hodgkiss, Tracey, Cooper, Lisa, Rothwell, Joanne, Allan, Angela, Anderson, Felicity, Kaye, Callum, Liew, Jade, Medhora, Jasmine, Scott, Teresa, Trumper, Erin, Botello, Adriana, Lankester, Liana, Nikitas, Nikitas, Wells, Colin, Stowe, Bethan, Spencer, Kayleigh, Brandwood, Craig, Smith, Lara, Birchall, Katie, Kolakaluri, Laurel, Baines, Deborah, Sukumaran, Anila, Apetri, Elena, Basikolo, Cathrine, Catlow, Laura, Charles, Bethan, Dark, Paul, Doonan, Reece, Harvey, Alice, Horner, Daniel, Knowles, Karen, Lee, Stephanie, Lomas, Diane, Lyons, Chloe, Marsden, Tracy, McLaughlan, Danielle, McMorrow, Liam, Pendlebury, Jessica, Perez, Jane, Poulaka, Maria, Proudfoot, Nicola, Slaughter, Melanie, Slevin, Kathryn, Thomas, Vicky, Walker, Danielle, Michael, Angiy, Collis, Matthew, Cosier, Tracey, Millen, Gemma, Richardson, Neil, Schumacher, Natasha, Weston, Heather, Rand, James, Baxter, Nicola, Henderson, Steven, Kennedy-Hay, Sophie, McParland, Christopher, Rooney, Laura, Sim, Malcolm, McCreath, Gordan, Akeroyd, Louise, Bano, Shereen, Bromley, Matt, Gurr, Lucy, Lawton, Tom, Morgan, James, Sellick, Kirsten, Warren, Deborah, Wilkinson, Brian, McGowan, Janet, Ledgard, Camilla, Stacey, Amelia, Pye, Kate, Bellwood, Ruth, Bentley, Michael, Bewley, Jeremy, Garland, Zoe, Grimmer, Lisa, Gumbrill, Bethany, Johnson, Rebekah, Sweet, Katie, Webster, Denise, Efford, Georgia, Convery, Karen, Fottrell-Gould, Deirdre, Hudig, Lisa, Keshet-Price, Jocelyn, Randell, Georgina, Stammers, Katie, Bokhari, Maria, Linnett, Vanessa, Lucas, Rachael, McCormick, Wendy, Ritzema, Jenny, Sanderson, Amanda, Wild, Helen, Rostron, Anthony, Roy, Alistair, Woods, Lindsey, Cornell, Sarah, Wakinshaw, Fiona, Rogerson, Kimberley, Jarmain, Jordan, Parker, Robert, Reddy, Amie, Turner-Bone, Ian, Wilding, Laura, Harding, Peter, Abernathy, Caroline, Foster, Louise, Gratrix, Andrew, Martinson, Vicky, Parkinson, Priyai, Stones, Elizabeth, Carbral-Ortega, Llucia, Bercades, Georgia, Brealey, David, Hass, Ingrid, MacCallum, Niall, Martir, Gladys, Raith, Eamon, Reyes, Anna, Smyth, Deborah, Zitter, Letizia, Benyon, Sarah, Marriott, Suzie, Park, Linda, Keenan, Samantha, Gordon, Elizabeth, Quinn, Helen, Baines, Kizzy, Cagova, Lenka, Fofano, Adama, Garner, Lucie, Holcombe, Helen, Mepham, Sue, Mitchell, Alice Michael, Mwaura, Lucy, Praman, Krithivasan, Vuylsteke, Alain, Zamikula, Julie, Purewal, Bally, Rivers, Vanessa, Bell, Stephanie, Blakemore, Hayley, Borislavova, Borislava, Faulkner, Beverley, Gendall, Emma, Goff, Elizabeth, Hayes, Kati, Thomas, Matt, Worner, Ruth, Smith, Kerry, Stephens, Deanna, Mew, Louise, Mwaura, Esther, Stewart, Richard, Williams, Felicity, Wren, Lynn, Sutherland, Sara-Beth, Bevan, Emily, Martin, Jane, Trodd, Dawn, Watson, Geoff, Brown, Caroline Wrey, Collins, Amy, Khaliq, Waqas, Gude, Estefania Treus, Akinkugbe, Olugbenga, Bamford, Alasdair, Beech, Emily, Belfield, Holly, Bell, Michael, Davies, Charlene, Jones, Gareth A.L., McHugh, Tara, Meghari, Hamza, O'Neill, Lauran, Peters, Mark J., Ray, Samiran, Tomas, Ana Luisa, Burn, Iona, Hambrook, Geraldine, Manso, Katarina, Penn, Ruth, Shanmugasundaram, Pradeep, Tebbutt, Julie, Thornton, Danielle, Cole, Jade, Davies, Rhys, Duffin, Donna, Hill, Helen, Player, Ben, Thomas, Emma, Williams, Angharad, Griffin, Denise, Muchenje, Nycola, Mupudzi, Mcdonald, Partridge, Richard, Conyngham, Jo-Anna, Thomas, Rachel, Wright, Mary, Corral, Maria Alvarez, Jacob, Reni, Jones, Cathy, Denmade, Craig, Beavis, Sarah, Dale, Katie, Gascoyne, Rachel, Hawes, Joanne, Pritchard, Kelly, Stevenson, Lesley, Whileman, Amanda, Doble, Patricia, Hutter, Joanne, Pawley, Corinne, Shovelton, Charmaine, Vaida, Marius, Butcher, Deborah, O'Sullivan, Susie, Butterworth-Cowin, Nicola, Ahmad, Norfaizan, Barker, Joann, Bauchmuller, Kris, Bird, Sarah, Cawthron, Kay, Harrington, Kate, Jackson, Yvonne, Kibutu, Faith, Lenagh, Becky, Masuko, Shamiso, Mills, Gary H., Raithatha, Ajay, Wiles, Matthew, Willson, Jayne, Newell, Helen, Lye, Alison, Nwafor, Lorenza, Jarman, Claire, Rowland-Jones, Sarah, Foote, David, Cole, Joby, Thompson, Roger, Watson, James, Hesseldon, Lisa, Macharia, Irene, Chetam, Luke, Smith, Jacqui, Ford, Amber, Anderson, Samantha, Birchall, Kathryn, Housley, Kay, Walker, Sara, Milner, Leanne, Hanratty, Helena, Trower, Helen, Phillips, Patrick, Oxspring, Simon, Donne, Ben, Jardine, Catherine, Williams, Dewi, Hay, Alasdair, Flanagan, Rebecca, Hughes, Gareth, Latham, Scott, McKenna, Emma, Anderson, Jennifer, Hull, Robert, Rhead, Kat, Cruz, Carina, Pattison, Natalie, Charnock, Rob, McFarland, Denise, Cosgrove, Denise, Ahmed, Ashar, Morris, Anna, Jakkula, Srinivas, Ali, Asifa, Brady, Megan, Dale, Sam, Dance, Annalisa, Gledhill, Lisa, Greig, Jill, Hanson, Kathryn, Holdroyd, Kelly, Home, Marie, Kelly, Diane, Kitson, Ross, Matapure, Lear, Melia, Deborah, Mellor, Samantha, Nortcliffe, Tonicha, Pinnell, Jez, Robinson, Matthew, Shaw, Lisa, Shaw, Ryan, Thomis, Lesley, Wilson, Alison, Wood, Tracy, Bayo, Lee-Ann, Merwaha, Ekta, Ishaq, Tahira, Hanley, Sarah, Hibbert, Meg, Tetla, Dariusz, Woodford, Chrsitopher, Durga, Latha, Kennard-Holden, Gareth, Branney, Debbie, Frankham, Jordan, Pitts, Sally, White, Nigel, Laha, Shondipon, Verlander, Mark, Williams, Alexandra, Altabaibeh, Abdelhakim, Alvaro, Ana, Gilbert, Kayleigh, Ma, Louise, Mostoles, Loreta, Parmar, Chetan, Simpson, Kathryn, Jetha, Champa, Booker, Lauren, Pratley, Anezka, Adams, Colene, Agasou, Anita, Arden, Tracie, Bowes, Amy, Boyle, Pauline, Beekes, Mandy, Button, Heather, Capps, Nigel, Carnahan, Mandy, Carter, Anne, Childs, Danielle, Donaldson, Denise, Hard, Kelly, Hurford, Fran, Hussain, Yasmin, Javaid, Ayesha, Jones, James, Jose, Sanal, Leigh, Michael, Martin, Terry, Millward, Helen, Motherwell, Nichola, Rikunenko, Rachel, Stickley, Jo, Summers, Julie, Ting, Louise, Tivenan, Helen, Tonks, Louise, Wilcox, Rebecca, Holland, Maureen, Keenan, Natalie, Lyons, Marc, Wassall, Helen, Marsh, Chris, Mahenthran, Mervin, Carter, Emma, Kong, Thomas, Blackman, Helen, Creagh-Brown, Ben, Donlon, Sinead, Michalak-Glinska, Natalia, Mtuwa, Sheila, Pristopan, Veronika, Salberg, Armorel, Smith, Eleanor, Stone, Sarah, Piercy, Charles, Verula, Jerik, Burda, Dorota, Montaser, Rugia, Harden, Lesley, Mayangao, Irving, Marriott, Cheryl, Bradley, Paul, Harris, Celia, Anderson, Susan, Andrews, Eleanor, Birch, Janine, Collins, Emma, Hammerton, Kate, O'Leary, Ryan, Clark, Michele, Purvis, Sarah, Barber, Russell, Hewitt, Claire, Hilldrith, Annette, Jackson-Lawrence, Karen, Shepardson, Sarah, Wills, Maryanne, Butler, Susan, Tavares, Silvia, Cunningham, Amy, Hindale, Julia, Arif, Sarwat, Bean, Sarah, Burt, Karen, Spivey, Michael, Demetriou, Carrie, Eckbad, Charlotte, Hierons, Sarah, Howie, Lucy, Mitchard, Sarah, Ramos, Lidia, Serrano-Ruiz, Alfredo, White, Katie, Kelly, Fiona, Cristiano, Daniele, Dormand, Natalie, Farzad, Zohreh, Gummadi, Mahitha, Liyanage, Kamal, Patel, Brijesh, Salmi, Sara, Sloane, Geraldine, Thwaites, Vicky, Varghese, Mathew, Zborowski, Anelise C., Allan, John, Geary, Tim, Houston, Gordon, Meikle, Alistair, O'Brien, Peter, Forsey, Miranda, Kaliappan, Agilan, Nicholson, Anne, Riches, Joanne, Vertue, Mark, Allan, Elizabeth, Darlington, Kate, Davies, Ffyon, Easton, Jack, Kumar, Sumit, Lean, Richard, Menzies, Daniel, Pugh, Richard, Qiu, Xinyi, Davies, Llinos, Williams, Hannah, Scanlon, Jeremy, Davies, Gwyneth, Mackay, Callum, Lewis, Joannne, Rees, Stephanie, Oblak, Metod, Popescu, Monica, Thankachen, Mini, Higham, Andrew, Simpson, Kerry, Craig, Jayne, Baruah, Rosie, Morris, Sheila, Ferguson, Susie, Shepherd, Amy, Prockter Moore, Luke Stephen, Vizcaychipi, Marcela Paola, Gomes de Almeida Martins, Laura, Carungcong, Jaime, Mohamed Ali, Inthakab Ali, Beaumont, Karen, Blunt, Mark, Coton, Zoe, Curgenven, Hollie, Elsaadany, Mohamed, Fernandes, Kay, Ally, Sameena Mohamed, Rangarajan, Harini, Sarathy, Varun, Selvanayagam, Sivarupan, Vedage, Dave, White, Matthew, Gill, Mandy, Paul, Paul, Ratnam, Valli, Shelton, Sarah, Wynter, Inez, Carmody, Siobhain, Page, Valerie Joan, Beith, Claire Marie, Black, Karen, Clements, Suzanne, Morrison, Alan, Strachan, Dominic, Taylor, Margaret, Clarkson, Michelle, D'Sylva, Stuart, Norman, Kathryn, Auld, Fiona, Donnachie, Joanne, Edmond, Ian, Prentice, Lynn, Runciman, Nikole, Salutous, Dario, Symon, Lesley, Todd, Anne, Turner, Patricia, Short, Abigail, Sweeney, Laura, Murdoch, Euan, Senaratne, Dhaneesha, Hill, Michaela, Kannan, Thogulava, Laura, Wild, Crawley, Rikki, Crew, Abigail, Cunningham, Mishell, Daniels, Allison, Harrison, Laura, Hope, Susan, Inweregbu, Ken, Jones, Sian, Lancaster, Nicola, Matthews, Jamie, Nicholson, Alice, Wray, Gemma, Langton, Helen, Prout, Rachel, Watters, Malcolm, Novis, Catherine, Barron, Anthony, Collins, Ciara, Kaul, Sundeep, Passmore, Heather, Prendergast, Claire, Reed, Anna, Rogers, Paula, Shokkar, Rajvinder, Woodruff, Meriel, Middleton, Hayley, Polgar, Oliver, Nolan, Claire, Mahay, Kanta, Collier, Dawn, Hormis, Anil, Maynard, Victoria, Graham, Cheryl, Walker, Rachel, Knights, Ellen, Price, Alicia, Thomas, Alice, Thorpe, Chris, Behan, Teresa, Burnett, Caroline, Hatton, Jonathan, Heeney, Elaine, Mitra, Atideb, Newton, Maria, Pollard, Rachel, Stead, Rachael, Amin, Vishal, Anastasescu, Elena, Anumakonda, Vikram, Karthik, Komala, Kausar, Rizwana, Reid, Karen, Smith, Jacqueline, Imeson-Wood, Janet, Skinner, Denise, Gaylard, Jane, Mullan, Dee, Newman, Julie, Brown, Alison, Crickmore, Vikki, Debreceni, Gabor, Wilkins, Joy, Nicol, Liz, Reece-Anthony, Rosie, Birt, Mark, Ghosh, Alison, Williams, Emma, Allen, Louise, Beranova, Eva, Crisp, Nikki, Deery, Joanne, Hazelton, Tracy, Knight, Alicia, Price, Carly, Tilbey, Sorrell, Turki, Salah, Turney, Sharon, Cooper, Joshua, Finch, Cheryl, Liderth, Sarah, Quinn, Alison, Waddington, Natalia, Coventry, Tina, Fowler, Susan, MacMahon, Michael, McGregor, Amanda, Cowley, Anne, Highgate, Judith, Gregory, Jane, O'Connell, Susan, Smith, Tim, Barberis, Luigi, Gopal, Shameer, Harris, Nichola, Lake, Victoria, Metherell, Stella, Radford, Elizabeth, Daniel, Amelia, Finn, Joanne, Saha, Rajnish, White, Nikki, Donnison, Phil, Trim, Fiona, Eapen, Beena, Birch, Jenny, Bough, Laura, Goodsell, Josie, Tutton, Rebecca, Williams, Patricia, Williams, Sarah, Winter-Goodwin, Barbara, Nichol, Ailstair, Brickell, Kathy, Smyth, Michelle, Murphy, Lorna, Coetzee, Samantha, Gales, Alistair, Otahal, Igor, Raj, Meena, Sell, Craig, Hilltout, Paula, Evitts, Jayne, Tyler, Amanda, Waldron, Joanne, Beesley, Kate, Board, Sarah, Kubisz-Pudelko, Agnieszka, Lewis, Alison, Perry, Jess, Pippard, Lucy, Wood, Di, Buckley, Clare, Barry, Peter, Flint, Neil, Rekha, Patel, Hales, Dawn, Bunni, Lara, Jennings, Claire, Latif, Monica, Marshall, Rebecca, Subramanian, Gayathri, McGuigan, Peter J., Wasson, Christopher, Finn, Stephanie, Green, Jackie, Collins, Erin, King, Bernadette, Campbell, Andy, Smuts, Sara, Duffield, Joseph, Smith, Oliver, Mallon, Lewis, Claire, Watkins, Botfield, Liam, Butler, Joanna, Dexter, Catherine, Fletcher, Jo, Garg, Atul, Kuravi, Aditya, Ranga, Poonam, Virgilio, Emma, Belagodu, Zakaula, Fuller, Bridget, Gherman, Anca, Olufuwa, Olumide, Paramsothy, Remi, Stuart, Carmel, Oakley, Naomi, Kamundi, Charlotte, Tyl, David, Collins, Katy, Silva, Pedro, Taylor, June, King, Laura, Coates, Charlotte, Crowley, Maria, Wakefield, Phillipa, Beadle, Jane, Johnson, Laura, Sargeant, Janet, Anderson, Madeleine, Brady, Ailbhe, Chan, Rebekah, Little, Jeff, McIvor, Shane, Prady, Helena, Whittle, Helen, Mathew, Bijoy, Attwood, Ben, Parsons, Penny, Ward, Geraldine, Bremmer, Pamela, Joe, West, Tracy, Baird, Jim, Ruddy, Davies, Ellie, Sathe, Sonia, Dennis, Catherine, McGregor, Alastair, Parris, Victoria, Srikaran, Sinduya, Sukha, Anisha, Clarke, Noreen, Whiteside, Jonathan, Mascarenhas, Mairi, Donaldson, Avril, Matheson, Joanna, Barrett, Fiona, O'Hara, Marianne, Okeefe, Laura, Bradley, Clare, Eastgate-Jackson, Christine, Filipe, Helder, Martin, Daniel, Maharajh, Amitaa, Garcia, Sara Mingo, Pakou, Glykeria, De Neef, Mark, Dent, Kathy, Horsley, Elizabeth, Akhtar, Muhmmad Nauman, Pearson, Sandra, Potoczna, Dorota, Spencer, Sue, Clapham, Melanie, Harper, Rosemary, Poultney, Una, Rice, Polly, Mutch, Rachel, Armstrong, Lisa, Bates, Hayley, Dooks, Emma, Farquhar, Fiona, Hairsine, Brigid, McParland, Chantal, Packham, Sophie, Bi, Rehana, Scholefield, Barney, Ashton, Lydia, George, Linsha, Twiss, Sophie, Wright, David, Chablani, Manish, Kirkby, Amy, Netherton, Kimberley, Davies, Kim, O'Brien, Linda, Omar, Zohra, Perkins, Emma, Lewis, Tracy, Sutherland, Isobel, Burns, Karen, Ben Chandler, Dr, Elliott, Kerry, Mallinson, Janine, Turnbull, Alison, Gondo, Prisca, Hadebe, Bernard, Kayani, Abdul, Masunda, Bridgett, Anderson, Taya, Hawcutt, Dan, O'Malley, Laura, Rad, Laura, Rogers, Naomi, Saunderson, Paula, Allison, Kathryn Sian, Afolabi, Deborah, Whitbread, Jennifer, Jones, Dawn, Dore, Rachael, Halkes, Matthew, Mercer, Pauline, Thornton, Lorraine, Dawson, Joy, Garrioch, Sweyn, Tolson, Melanie, Aldridge, Jonathan, Kapoor, Ritoo, Loader, David, Castle, Karen, Humphreys, Sally, Tampsett, Ruth, Mackintosh, Katherine, Ayers, Amanda, Harrison, Wendy, North, Julie, Allibone, Suzanne, Genetu, Roman, Kasipandian, Vidya, Patel, Amit, Mac, Ainhi, Murphy, Anthony, Mahjoob, Parisa, Nazari, Roonak, Worsley, Lucy, Fagan, Andrew, Bemand, Thomas, Black, Ethel, Dela Rosa, Arnold, Howle, Ryan, Jhanji, Shaman, Baikady, Ravishankar Rao, Tatham, Kate Colette, Thomas, Benjamin, Bell, Dina, Boyle, Rosalind, Douglas, Katie, Glass, Lynn, Lee, Emma, Lennon, Liz, Rattray, Austin, Taylor, Abigail, Hughes, Rachel Anne, Thomas, Helen, Rees, Alun, Duskova, Michaela, Phipps, Janet, Brooks, Suzanne, Edwards, Michelle, Quaid, Sheena, Watson, Ekaterina, Brayne, Adam, Fisher, Emma, Hunt, Jane, Jackson, Peter, Kaye, Duncan, Love, Nicholas, Parkin, Juliet, Tuckey, Victoria, Van Koutrik, Lynne, Carter, Sasha, Andrew, Benedict, Findlay, Louise, Adams, Katie, Service, Jen, Williams, Alison, Cheyne, Claire, Saunderson, Anne, Moultrie, Sam, Odam, Miranda, Hall, Kathryn, Mapfunde, Isheunesu, Willis, Charlotte, Lyon, Alex, Sri-Chandana, Chunda, Scherewode, Joslan, Stephenson, Lorraine, Marsh, Sarah, Hardy, John, Houlden, Henry, Moncur, Eleanor, Tariq, Ambreen, Tucci, Arianna, Hobrok, Maria, Loosley, Ronda, McGuinness, Heather, Tench, Helen, Wolf-Roberts, Rebecca, Irvine, Val, Shelley, Benjamin, Gorman, Claire, Gupta, Abhinav, Timlick, Elizabeth, Brady, Rebecca, Milligan, Barry, Bellini, Arianna, Bryant, Jade, Mayer, Anton, Pickard, Amy, Roe, Nicholas, Sowter, Jason, Howlett, Alex, Fidler, Katy, Tagliavini, Emma, Donnelly, Kevin, Boos, Jannik, van der Made, Caspar I., Ramakrishnan, Gayatri, Coughlan, Eamon, Asselta, Rosanna, Löscher, Britt-Sabina, Valenti, Luca V.C., de Cid, Rafael, Bujanda, Luis, Julià, Antonio, Pairo-Castineira, Erola, May, Sandra, Zametica, Berina, Heggemann, Julia, Albillos, Agustín, Banales, Jesus M., Barretina, Jordi, Blay, Natalia, Bonfanti, Paolo, Buti, Maria, Fernandez, Javier, Marsal, Sara, Prati, Daniele, Ronzoni, Luisa, Sacchi, Nicoletta, Schultze, Joachim L., Riess, Olaf, Franke, Andre, Rawlik, Konrad, Ellinghaus, David, Hoischen, Alexander, Schmidt, Axel, and Ludwig, Kerstin U.

Published
2024
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21. Estimating SARS-CoV-2 Seroprevalence

Author

Rosin, Samuel P., Shook-Sa, Bonnie E., Cole, Stephen R., and Hudgens, Michael G.

Subjects
Statistics - Applications
Abstract

Governments and public health authorities use seroprevalence studies to guide responses to the COVID-19 pandemic. Seroprevalence surveys estimate the proportion of individuals who have detectable SARS-CoV-2 antibodies. However, serologic assays are prone to misclassification error, and non-probability sampling may induce selection bias. In this paper, nonparametric and parametric seroprevalence estimators are considered that address both challenges by leveraging validation data and assuming equal probabilities of sample inclusion within covariate-defined strata. Both estimators are shown to be consistent and asymptotically normal, and consistent variance estimators are derived. Simulation studies are presented comparing the estimators over a range of scenarios. The methods are used to estimate SARS-CoV-2 seroprevalence in New York City, Belgium, and North Carolina., Comment: Main text: 23 pages, 5 figures, 3 tables. Appendix: 24 pages, 18 figures. Preprint

Published
2021

26. Anal cancer incidence in men with HIV who have sex with men: are black men at higher risk?

Author

McNeil, Candice J, Lee, Jennifer S, Cole, Stephen R, Patel, Shivani A, Martin, Jeffrey, Mathews, William C, Moore, Richard D, Mayer, Kenneth H, Eron, Joseph J, Saag, Michael S, Kitahata, Mari M, and Achenbach, Chad J

Subjects
Biomedical and Clinical Sciences, Public Health, Health Sciences, Clinical Sciences, Prevention, Cancer, Behavioral and Social Science, Minority Health, Infectious Diseases, Sexually Transmitted Infections, HIV/AIDS, Digestive Diseases, Health Disparities, Hepatitis, Liver Disease, Sexual and Gender Minorities (SGM/LGBT*), Clinical Research, Hepatitis - B, Aetiology, 2.2 Factors relating to the physical environment, Infection, Good Health and Well Being, Anus Neoplasms, Cohort Studies, Coinfection, HIV Infections, Homosexuality, Male, Humans, Incidence, Male, Risk Factors, Sexual and Gender Minorities, AIDS, anal cancer, HIV, men who have sex with men, racial disparities, on behalf of the Centers for AIDS Research (CFAR) Network of Integrated Clinical Systems, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Virology, Biomedical and clinical sciences, Health sciences
Abstract

ObjectiveTo assess differences in anal cancer incidence between racial/ethnic groups among a clinical cohort of men with HIV who have sex with men.DesignClinical cohort study.MethodsWe studied men who have sex with men (MSM) in the Centers for AIDS Research Network of Integrated Clinical Systems (CNICS) who initiated antiretroviral therapy (ART) under HIV care in CNICS. We compared anal cancer incidence between Black and non-Black men and calculated hazard ratios controlling for demographic characteristics (age, CNICS site, year of ART initiation), HIV disease indicators (nadir CD4+, peak HIV RNA), and co-infection/behavioral factors including hepatitis B virus (HBV), hepatitis C virus (HCV), tobacco smoking and alcohol abuse.ResultsWe studied 7473 MSM with HIV who contributed 41 810 person-years of follow-up after initiating ART between 1996 and 2014 in CNICS. Forty-one individuals had an incident diagnosis of anal cancer under observation. Crude rates of anal cancer were 204 versus 61 per 100 000 person-years among Black versus non-Black MSM. The weighted hazard ratio for anal cancer in Black MSM (adjusting for demographics, HIV disease factors, and co-infection/behavioral factors) was 2.37 (95% confidence interval: 1.17, 4.82) compared to non-Black MSM.ConclusionsIn this large multicenter cohort, Black MSM were at significantly increased risk for anal cancer compared to non-Black MSM. Further detailed studies evaluating factors impacting anal cancer incidence and outcomes in Black men with HIV are necessary. Inclusion of more diverse study cohorts may elucidate modifiable factors associated with increased anal cancer risk experienced by Black MSM.

Published
2022

27. Odds Ratios are far from 'portable': A call to use realistic models for effect variation in meta-analysis

Author

Xiao, Mengli, Chu, Haitao, Cole, Stephen, Chen, Yong, MacLehose, Richard, Richardson, David, and Greenland, Sander

Subjects
Statistics - Applications
Abstract

Objective: Recently Doi et al. argued that risk ratios should be replaced with odds ratios in clinical research. We disagreed, and empirically documented the lack of portability of odds ratios, while Doi et al. defended their position. In this response we highlight important errors in their position. Study Design and Setting: We counter Doi et al.'s arguments by further examining the correlations of odds ratios, and risk ratios, with baseline risks in 20,198 meta-analyses from the Cochrane Database of Systematic Reviews. Results: Doi et al.'s claim that odds ratios are portable is invalid because 1) their reasoning is circular: they assume a model under which the odds ratio is constant and show that under such a model the odds ratio is portable; 2) the method they advocate to convert odds ratios to risk ratios is biased; 3) their empirical example is readily-refuted by counter-examples of meta-analyses in which the risk ratio is portable but the odds ratio isn't; and 4) they fail to consider the causal determinants of meta-analytic inclusion criteria: Doi et al. mistakenly claim that variation in odds ratios with different baseline risks in meta-analyses is due to collider bias. Empirical comparison between the correlations of odds ratios, and risk ratios, with baseline risks show that the portability of odds ratios and risk ratios varies across settings. Conclusion: The suggestion to replace risk ratios with odds ratios is based on circular reasoning and a confusion of mathematical and empirical results. It is especially misleading for meta-analyses and clinical guidance. Neither the odds ratio nor the risk ratio is universally portable. To address this lack of portability, we reinforce our suggestion to report variation in effect measures conditioning on modifying factors such as baseline risk; understanding such variation is essential to patient-centered practice., Comment: 16 pages, 2 figures, 2 tables

Published
2021

28. Generalizing trial evidence to target populations in non-nested designs: Applications to AIDS clinical trials

Author

Li, Fan, Buchanan, Ashley L., and Cole, Stephen R.

Subjects
Statistics - Methodology, Statistics - Applications
Abstract

Comparative effectiveness evidence from randomized trials may not be directly generalizable to a target population of substantive interest when, as in most cases, trial participants are not randomly sampled from the target population. Motivated by the need to generalize evidence from two trials conducted in the AIDS Clinical Trials Group (ACTG), we consider weighting, regression and doubly robust estimators to estimate the causal effects of HIV interventions in a specified population of people living with HIV in the USA. We focus on a non-nested trial design and discuss strategies for both point and variance estimation of the target population average treatment effect. Specifically in the generalizability context, we demonstrate both analytically and empirically that estimating the known propensity score in trials does not increase the variance for each of the weighting, regression and doubly robust estimators. We apply these methods to generalize the average treatment effects from two ACTG trials to specified target populations and operationalize key practical considerations. Finally, we report on a simulation study that investigates the finite-sample operating characteristics of the generalizability estimators and their sandwich variance estimators., Comment: 44 pages, 3 tables and 3 figures. Journal of the Royal Statistical Society: Series C (2022)

Published
2021
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29. Tutorial: Introduction to computational causal inference using reproducible Stata, R and Python code

Author

Smith, Matthew J., Maringe, Camille, Rachet, Bernard, Mansournia, Mohammad A., Zivich, Paul N., Cole, Stephen R., and Luque-Fernandez, Miguel Angel

Subjects
Statistics - Methodology, Statistics - Computation
Abstract

The purpose of many health studies is to estimate the effect of an exposure on an outcome. It is not always ethical to assign an exposure to individuals in randomised controlled trials, instead observational data and appropriate study design must be used. There are major challenges with observational studies, one of which is confounding that can lead to biased estimates of the causal effects. Controlling for confounding is commonly performed by simple adjustment for measured confounders; although, often this is not enough. Recent advances in the field of causal inference have dealt with confounding by building on classical standardisation methods. However, these recent advances have progressed quickly with a relative paucity of computational-oriented applied tutorials contributing to some confusion in the use of these methods among applied researchers. In this tutorial, we show the computational implementation of different causal inference estimators from a historical perspective where different estimators were developed to overcome the limitations of the previous one. Furthermore, we also briefly introduce the potential outcomes framework, illustrate the use of different methods using an illustration from the health care setting, and most importantly, we provide reproducible and commented code in Stata, R and Python for researchers to apply in their own observational study. The code can be accessed at https://github.com/migariane/TutorialCausalInferenceEstimators

Published
2020

30. Sensitivity analyses for effect modifiers not observed in the target population when generalizing treatment effects from a randomized controlled trial: Assumptions, models, effect scales, data scenarios, and implementation details

Author

Nguyen, Trang Quynh, Ackerman, Benjamin, Schmid, Ian, Cole, Stephen R., and Stuart, Elizabeth A.

Subjects
Statistics - Methodology
Abstract

Background: Randomized controlled trials are often used to inform policy and practice for broad populations. The average treatment effect (ATE) for a target population, however, may be different from the ATE observed in a trial if there are effect modifiers whose distribution in the target population is different that from that in the trial. Methods exist to use trial data to estimate the target population ATE, provided the distributions of treatment effect modifiers are observed in both the trial and target population -- an assumption that may not hold in practice. Methods: The proposed sensitivity analyses address the situation where a treatment effect modifier is observed in the trial but not the target population. These methods are based on an outcome model or the combination of such a model and weighting adjustment for observed differences between the trial sample and target population. They accommodate several types of outcome models: linear models (including single time outcome and pre- and post-treatment outcomes) for additive effects, and models with log or logit link for multiplicative effects. We clarify the methods' assumptions and provide detailed implementation instructions. Illustration: We illustrate the methods using an example generalizing the effects of an HIV treatment regimen from a randomized trial to a relevant target population. Conclusion: These methods allow researchers and decision-makers to have more appropriate confidence when drawing conclusions about target population effects.

Published
2020
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31. Transportability From Randomized Trials to Clinical Care: On Initial HIV Treatment With Efavirenz and Suicidal Thoughts or Behaviors

Author

Mollan, Katie R, Pence, Brian W, Xu, Steven, Edwards, Jessie K, Mathews, W Christopher, O’Cleirigh, Conall, Crane, Heidi M, Eaton, Ellen F, Collier, Ann C, Weideman, Ann Marie K, Westreich, Daniel, Cole, Stephen R, Tierney, Camlin, Bengtson, Angela M, and Group, for the CFAR Network of Integrated Clinical Systems and the AIDS Clinical Trials

Subjects
Epidemiology, Health Sciences, Sexually Transmitted Infections, Clinical Trials and Supportive Activities, Infectious Diseases, HIV/AIDS, Clinical Research, Women's Health, Infection, Adult, Alkynes, Anti-HIV Agents, Antidepressive Agents, Benzoxazines, Cyclopropanes, Depression, Drug Prescriptions, Female, HIV, HIV Infections, Humans, Incidence, Male, Observational Studies as Topic, Proportional Hazards Models, Randomized Controlled Trials as Topic, Suicidal Ideation, Translational Research, Biomedical, United States, benzoxazines, efavirenz, inverse odds weights, multiple imputation, new user design, suicidal ideation, transportability, CFAR Network of Integrated Clinical Systems and the AIDS Clinical Trials Group, Mathematical Sciences, Medical and Health Sciences
Abstract

In an analysis of randomized trials, use of efavirenz for treatment of human immunodeficiency virus (HIV) infection was associated with increased suicidal thoughts/behaviors. However, analyses of observational data have found no evidence of increased risk. To assess whether population differences might explain this divergence, we transported the effect of efavirenz use from these trials to a specific target population. Using inverse odds weights and multiple imputation, we transported the effect of efavirenz on suicidal thoughts/behaviors in these randomized trials (participants were enrolled in 2001-2007) to a trials-eligible cohort of US adults initiating antiretroviral therapy while receiving HIV clinical care at medical centers between 1999 and 2015. Overall, 8,291 cohort participants and 3,949 trial participants were eligible. Prescription of antidepressants (19% vs. 13%) and injection drug history (16% vs. 10%) were more frequent in the cohort than in the trial participants. Compared with the effect in trials, the estimated hazard ratio for efavirenz on suicidal thoughts/behaviors was attenuated in our target population (trials: hazard ratio (HR) = 2.3 (95% confidence interval (CI): 1.2, 4.4); transported: HR = 1.8 (95% CI: 0.9, 4.4)), whereas the incidence rate difference was similar (trials: HR = 5.1 (95% CI: 1.6, 8.7); transported: HR = 5.4 (95% CI: -0.4, 11.4)). In our target population, there was greater than 20% attenuation of the hazard ratio estimate as compared with the trials-only estimate. Transporting results from trials to a target population is informative for addressing external validity.

Published
2021

32. Mortality Among Persons Entering HIV Care Compared With the General U.S. Population : An Observational Study.

Author

Edwards, Jessie K, Cole, Stephen R, Breger, Tiffany L, Rudolph, Jacqueline E, Filiatreau, Lindsey M, Buchacz, Kate, Humes, Elizabeth, Rebeiro, Peter F, D'Souza, Gypsyamber, Gill, M John, Silverberg, Michael J, Mathews, W Christopher, Horberg, Michael A, Thorne, Jennifer, Hall, H Irene, Justice, Amy, Marconi, Vincent C, Lima, Viviane D, Bosch, Ronald J, Sterling, Timothy R, Althoff, Keri N, Moore, Richard D, Saag, Michael, and Eron, Joseph J

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Behavioral and Social Science, Infectious Diseases, Sexually Transmitted Infections, HIV/AIDS, Aetiology, 2.4 Surveillance and distribution, Infection, Good Health and Well Being, Adult, Cause of Death, Cohort Studies, Female, HIV Infections, Humans, Male, Middle Aged, Population Surveillance, Risk Factors, United States, Public Health and Health Services
Abstract

BackgroundUnderstanding advances in the care and treatment of adults with HIV as well as remaining gaps requires comparing differences in mortality between persons entering care for HIV and the general population.ObjectiveTo assess the extent to which mortality among persons entering HIV care in the United States is elevated over mortality among matched persons in the general U.S. population and trends in this difference over time.DesignObservational cohort study.SettingThirteen sites from the U.S. North American AIDS Cohort Collaboration on Research and Design.Participants82 766 adults entering HIV clinical care between 1999 and 2017 and a subset of the U.S. population matched on calendar time, age, sex, race/ethnicity, and county using U.S. mortality and population data compiled by the National Center for Health Statistics.MeasurementsFive-year all-cause mortality, estimated using the Kaplan-Meier estimator of the survival function.ResultsOverall 5-year mortality among persons entering HIV care was 10.6%, and mortality among the matched U.S. population was 2.9%, for a difference of 7.7 (95% CI, 7.4 to 7.9) percentage points. This difference decreased over time, from 11.1 percentage points among those entering care between 1999 and 2004 to 2.7 percentage points among those entering care between 2011 and 2017.LimitationMatching on available covariates may have failed to account for differences in mortality that were due to sociodemographic factors rather than consequences of HIV infection and other modifiable factors.ConclusionMortality among persons entering HIV care decreased dramatically between 1999 and 2017, although those entering care remained at modestly higher risk for death in the years after starting care than comparable persons in the general U.S. population.Primary funding sourceNational Institutes of Health.

Published
2021

33. Current and Past Immunodeficiency Are Associated With Higher Hospitalization Rates Among Persons on Virologically Suppressive Antiretroviral Therapy for up to 11 Years

Author

Davy-Mendez, Thibaut, Napravnik, Sonia, Eron, Joseph J, Cole, Stephen R, van Duin, David, Wohl, David A, Hogan, Brenna C, Althoff, Keri N, Gebo, Kelly A, Moore, Richard D, Silverberg, Michael J, Horberg, Michael A, Gill, M John, Mathews, W Christopher, Klein, Marina B, Colasanti, Jonathan A, Sterling, Timothy R, Mayor, Angel M, Rebeiro, Peter F, Buchacz, Kate, Li, Jun, Nanditha, Ni Gusti Ayu, Thorne, Jennifer E, Nijhawan, Ank, Berry, Stephen A, Benson, Constance A, Bosch, Ronald J, Kirk, Gregory D, Mayer, Kenneth H, Grasso, Chris, Hogg, Robert S, Montaner, Julio SG, Salters, Kate, Lima, Viviane D, Sereda, Paul, Trigg, Jason, Rodriguez, Benigno, Brown, Todd, Tien, Phyllis, D’Souza, Gypsyamber, Rabkin, Charles, Kroch, Abigail, Burchell, Ann, Betts, Adrian, Lindsay, oanne, Hunter-Mellado, Robert F, Martin, Jeffrey N, Brooks, John T, Saag, Michael S, Mugavero, Michael J, Willig, James, Mathews, William C, Kitahata, Mari M, Crane, Heidi M, Haas, David, Rebeiro, Peter, Turner, Megan, Tate, Janet, Dubrow, Robert, Fiellin, David, Gange, Stephen J, McKaig, Rosemary G, Freeman, Aimee M, Van Rompaey, Stephen E, Morton, Liz, McReynolds, Justin, Lober, William B, Lee, Jennifer S, You, Bin, Hogan, Brenna, Zhang, Jinbing, and Jing, Jerry

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Infectious Diseases, Sexually Transmitted Infections, Clinical Research, Anti-HIV Agents, CD4 Lymphocyte Count, Canada, Cohort Studies, Female, HIV Infections, Hospitalization, Humans, Male, Viral Load, HIV/AIDS, 6.1 Pharmaceuticals, HIV, hospitalization, CD4 lymphocyte count, sustained virologic response, cohort studies, North American AIDS Cohort Collaboration on Research and Design (NA-ACCORD) of IeDEA, Biological Sciences, Medical and Health Sciences, Microbiology, Biological sciences, Biomedical and clinical sciences, Health sciences
Abstract

BackgroundPersons with HIV (PWH) with persistently low CD4 counts despite efficacious antiretroviral therapy could have higher hospitalization risk.MethodsIn six US and Canadian clinical cohorts, PWH with virologic suppression for ≥1 year in 2005-2015 were followed until virologic failure, loss to follow-up, death, or study end. Stratified by early (Years 2-5) and long-term (Years 6-11) suppression and lowest pre-suppression CD4 count 500 cells/μL had an aIRR of 1.44 during early suppression (95% CI 1.01-2.06), and 1.67 (1.03-2.72) during long-term suppression. Among patients with lowest pre-suppression CD4 ≥200 (56%), patients with current CD4 351-500 versus >500 cells/μL had an aIRR of 1.22 (0.93-1.60) during early suppression and 2.09 (1.18-3.70) during long-term suppression.ConclusionsVirologically suppressed patients with lower CD4 counts experienced higher hospitalization rates, and could potentially benefit from targeted clinical management strategies.

Published
2021

34. Effect of adequacy of empirical antibiotic therapy for hospital-acquired bloodstream infections on intensive care unit patient prognosis: a causal inference approach using data from the Eurobact2 study

Author

Tabah, Alexis, Lipman, Jeffrey, Pollock, Hamish, Ben Margetts, Udy, Andrew, Young, Meredith, Bhadange, Neeraj, Tyler, Steven, Ledtischke, Anne, Finnis, Mackenzie, Dwivedi, Jyotsna, Saxena, Manoj, Biradar, Vishwanath, Soar, Natalie, Sarode, Vineet, Brewster, David, Regli, Adrian, Weeda, Elizabeth, Ahmed, Samiul, Fourie, Cheryl, Laupland, Kevin, Ramanan, Mahesh, Walsham, James, Meyer, Jason, Litton, Edward, Maria Palermo, Anna, Yap, Timothy, Eroglu, Ege, George Attokaran, Antony, Jaramillo, C'havala, Nafees, Khalid Mk, Nafees, Khalid Mahmood Khan, Aqilah Haji Abd Rashid, Nurhikmahtul, Adi Muhamad Ibnu Walid, Haji, Mon, Tomas, Dhakshina Moorthi, P., Sudhirchandra, Shah, Sridharan, Dhadappa Damodar, Haibo, Qiu, Xie, Jianfeng, Jianfeng, Xie, Wei-Hua, Lu, Zhen, Wang, Qian, Chuanyun, Luo, Jili, Chen, Xiaomei, Wang, Hao, Zhao, Peng, Zhao, Juan, Wusi, Qiu, Mingmin, Chen, Xu, Lei, Yin, Chengfen, Wang, Ruilan, Wang, Jinfeng, Yin, Yongjie, Zhang, Min, Ye, Jilu, Hu, Chungfang, Zhou, Suming, Huang, Min, Yan, Jing, Wang, Yan, Qin, Bingyu, Ye, Ling, Weifeng, Xie, Peije, Li, Geng, Nan, Ling, Lowell, Hayashi, Yoshiro, Karumai, Toshiyuki, Yamasaki, Masaki, Hashimoto, Satoru, Hosokawa, Koji, Makino, Jun, Matsuyoshi, Takeo, Kuriyama, Akira, Shigemitsu, Hidenobu, Mishima, Yuka, Nagashima, Michio, Yoshida, Hideki, Fujitani, Shigeki, Omori, Koichiro, Rinka, Hiroshi, Saito, Hiroki, Atobe, Kaori, Kato, Hideaki, Takaki, Shunsuke, Sulaiman, Helmi, Shahnaz Hasan, M., Fadhil Hadi Jamaluddin, Muhamad, Pheng, Lee See, Visvalingam, Sheshendrasurian, Thing Liew, Mun, Ling Danny Wong, Siong, Khang Fong, Kean, Bt Abdul Rahman, Hamizah, Md Noor, Zuraini, Lee, Kok Tong, Hamid Azman, Abd., Zulfakar Mazlan, Mohd, Ali, Saedah, Hernandez, Aaron Mark, Abello, Anton, Jeon, Kyeongman, Lee, Sang-Min, Park, Sunghoon, Park, Seung Yong, Yoon Lim, Sung, Kwa, Andrea Lay Hoon, Yuan Goh, Qing, Ng, Shin Yi, An Lie, Sui, Junyang Goh, Ken, Yunkai Li, Andrew, Ong, Caroline Yu Ming, Yan Lim, Jia, Lishan Quah, Jessica, Ng, Kangqi, Xiang Long Ng, Louis, Yeh, Tony Yu-Chang, Chang Yeh, Yu, Chou, Nai-Kuan, Cia, Cong-Tat, Hu, Ting-Yu, Kuo, Li-Kuo, Ku, Shih-Chi, Wongsurakiat, Phunsup, Apichatbutr, Yutthana, Chiewroongroj, Supattra, Alsisi, Adel, Nadeem, Rashid, El Houfi, Ashraf, Elhadidy, Amr, Barsoum, Mina, Osman, Nermin, Mostafa, Tarek, Elbahnasawy, Mohamed, Saber, Ahmed, Aldhalia, Amer, Elmandouh, Omar, Elsayed, Ahmed, Elbadawy, Merihan A., Awad, Ahmed K., Hemead, Hanan M., Zand, Farid, Ouhadian, Maryam, Hamid Borsi, Seyed, Mehraban, Zahra, Kashipazha, Davood, Ahmadi, Fatemeh, Savaie, Mohsen, Soltani, Farhad, Rashidi, Mahboobeh, Baghbanian, Reza, Javaherforoosh, Fatemeh, Amiri, Fereshteh, Kiani, Arash, Amin Zargar, Mohammad, Mahmoodpoor, Ata, Aalinezhad, Fatemeh, Dabiri, Gholamreza, Sabetian, Golnar, Sarshad, Hakimeh, Masjedi, Mansoor, Tajvidi, Ramin, Nasirodin (S.M.N.) Tabatabaei, Dr Seyed Mohammad, Ahmed, Abdullah Khudhur, Singer, Pierre, Kagan, Ilya, Rigler, Merav, Belman, Daniel, Levin, Phillip, Harara, Belal, Diab, Adei, Abillama, Fayez, Abilama, Fayez, Ibrahim, Rebecca, Fares, Aya, Elhadi, Muhammed, Buimsaedah, Ahmad, Gamra, Marwa, Aqeelah, Ahmed, Ali Mohammed Ali, Almajdoub, Gaber Sadik Homaidan, Ahmed, Almiqlash, Bushray, Bilkhayr, Hala, Bouhuwaish, Ahmad, Sa Taher, Ahmed, Abdulwahed, Eman, Abousnina, Fathi A., Khaled Hdada, Aisha, Jobran, Rania, Ben Hasan, Hayat, Shaban Ben Hasan, Rabab, Khalid Abidi, Serghini, Issam, Seddiki, Rachid, Boukatta, Brahim, Kanjaa, Nabil, Mouhssine, Doumiri, Ahmed Wajdi, Maazouzi, Dendane, Tarek, Ali Zeggwagh, Amine, Housni, Brahim, Younes, Oujidi, Hachimi, Abdelhamid, Ghannam, A., Belkhadir, Z., Abu Jayyab, Mustafa, Aithssain, Ali, Lance, Marcus, Nissar, Shaikh, Sallam, Hend, Elrabi, Omar, Almekhlafi, Ghaleb A., Awad, Maher, Aljabbary, Ahmed, Karam Chaaban, Mohammad, Abu-Sayf, Natalia, Al-Jadaan, Mohammad, Bakr, Lubna, Mounir Bouaziz, Bouaziz, Mounir, Turki, Olfa, Sellami, Walid, Vidal, Gabriela, Centeno, Pablo, Morvillo, Natalia, Oscar Acevedo, José, Mabel Lopez, Patricia, Fernández, Rubén, Segura, Matías, Aparicio, Marta, Alonzo, Irene, Nuccetelli, Yanina, Montefiore, Pablo, Arias, Mario, Felipe Reyes, Luis, Ñamendys-Silva, Silvio A., Romero-Gonzalez, Juan P., Hermosillo, Mariana, Alejandro Castillo, Roberto, Nicolás Pantoja Leal, Jesús, Garcia Aguilar, Candy, Ocotlan Gonzalez Herrera, Mara, Vladimir Espinoza Villafuerte, Missael, Lomeli-Teran, Manuel, Dominguez-Cherit, Jose G., Davalos-Alvarez, Adrian, Sánchez-Hurtado, Luis, Tejeda-Huezo, Brigitte, Perez-Nieto, Orlando R., Deloya Tomas, Ernesto, De Bus, Liesbet, De Waele, Jan, Francois, Guy, Hollevoet, Isabelle, Denys, Wouter, Bourgeois, Marc, Vanderhaeghen, Sofie F.M., Mesland, Jean-Baptiste, Henin, Pierre, Haentjens, Lionel, Biston, Patrick, Noel, Cindérella, Layos, Nathalie, Misset, Benoît, De Schryver, Nicolas, Serck, Nicolas, Wittebole, Xavier, De Waele, Elisabeth, Opdenacker, Godelive, Kovacevic, Pedja, Zlojutro, Biljana, Ina, Filipovic-Grcic, Custovic, Aida, Filipovic-Grcic, Ina, Radonic, Radovan, Vujaklija Brajkovic, Ana, Persec, Jasminka, Sakan, Sanja, Nikolic, Mario, Lasic, Hrvoje, Leone, Marc, Timsit, Jean-François, Ruppe, Etienne, Ruckly, Stephane, Montravers, Philippe, Arbelot, Charlotte, Patrier, Juliette, Zappela, N., Montravers, P., Dulac, Thierry, Castanera, Jérémy, Auchabie, Johann, Le Meur, Anthony, Marchalot, A., Beuzelin, M., Massri, Alexandre, Guesdon, Charlotte, Escudier, Etienne, Mateu, Philippe, Rosman, Jérémy, Leroy, Olivier, Alfandari, Serge, Nica, Alexandru, Souweine, Bertrand, Coupez, Elisabeth, Duburcq, Thibault, Kipnis, Eric, Bortolotti, Perrine, Le Souhaitier, Mathieu, Mira, Jean-Paul, Garcon, Pierre, Duprey, Matthieu, Thyrault, Martial, Paulet, Rémi, Philippart, François, Tran, Marc, Bruel, Cédric, Weiss, Emmanuel, Janny, Sylvie, Foucrier, Arnaud, Perrigault, Pierre-François, Djanikian, Flora, Barbier, François, Gainnier, Marc, Bourenne, Jérémy, Louis, Guillaume, Smonig, Roland, Argaud, Laurent, Baudry, Thomas, Mekonted Dessap, Armand, Razazi, Keyvan, Kalfon, Pierre, Badre, Gaëtan, Larcher, Romaric, Lefrant, Jean-Yves, Roger, Claire, Sarton, Benjamine, Silva, Stein, Demeret, Sophie, Le Guennec, Loïc, Siami, Shidasp, Aparicio, Christelle, Voiriot, Guillaume, Fartoukh, Muriel, Dahyot-Fizelier, Claire, Imzi, Nadia, Klouche, Kada, Bracht, Hendrik, Hoheisen, Sandra, Bloos, Frank, Thomas-Rueddel, Daniel, Petros, Sirak, Pasieka, Bastian, Dubler, Simon, Schmidt, Karsten, Gottschalk, Antje, Wempe, Carola, Lepper, Philippe, Metz, Carlos, Viderman, Dmitriy, Umbetzhanov, Yerlan, Mugazov, Miras, Bazhykayeva, Yelena, Kaligozhin, Zhannur, Babashev, Baurzhan, Merenkov, Yevgeniy, Temirov, Talgat, Arvaniti, Kostoula, Smyrniotis, Dimitrios, Psallida, Vasiliki, Fildisis, Georgios, Soulountsi, Vasiliki, Kaimakamis, Evangelos, Iasonidou, Cristina, Papoti, Sofia, Renta, Foteini, Vasileiou, Maria, Romanou, Vasiliki, Koutsoukou, Vasiliki, Kristina Matei, Mariana, Moldovan, Leora, Karaiskos, Ilias, Paskalis, Harry, Marmanidou, Kyriaki, Papanikolaou, M., Kampolis, C., Oikonomou, Marina, Kogkopoulos, Evangelos, Nikolaou, Charikleia, Sakkalis, Anastasios, Chatzis, Marinos, Georgopoulou, Maria, Efthymiou, Anna, Chantziara, Vasiliki, Sakagianni, Aikaterini, Athanasa (Athanassa), Zoi (Zoe), Papageorgiou, Eirini, Ali, Fadi, Dimopoulos, Georges, Panagiota Almiroudi, Mariota, Malliotakis, Polychronis, Marouli, Diamantina, Theodorou, Vasiliki, Retselas, Ioannis, Kouroulas, Vasilios, Papathanakos, Georgios, Bassetti, Matteo, Giacobbe, Daniele, Montrucchio, Giorgia, Sales, Gabriele, De Pascale, Gennaro, Maria Montini, Luca, Carelli, Simone, Vargas, Joel, Di Gravio, Valentina, Roberto Giacobbe, Daniele, Gratarola, Angelo, Porcile, Elisa, Mirabella, Michele, Daroui, Ivan, Lodi, Giovanni, Zuccaro, Francesco, Grazia Schlevenin, Maria, Pelosi, Paolo, Battaglini, Denise, Cortegiani, Andrea, Ippolito, Mariachiara, Bellina, Davide, Di Guardo, Andrea, Pelagalli, Lorella, Covotta, Marco, Rocco, Monica, Fiorelli, Silvia, Cotoia, Antonella, Chiara Rizzo, Anna, Adam Mikstacki, Mikstacki, Adam, Tamowicz, Barbara, Kaptur Komorowska, Irmina, Szczesniak, Anna, Bojko, Jozef, Kotkowska, Anna, Walczak-Wieteska, Paulina, Wasowska, Dominika, Nowakowski, Tomasz, Broda, Hanna, Mariusz Peichota, Assoc, Pietraszek-Grzywaczewska, Iwona, Martin-Loeches, Ignacio, Bisanti, Alessandra, Paiva, José Artur, Póvoa, Pedro, Cartoze, Nuno, Pereira, Tiago, Guimarães, Nádia, Alves, Madalena, Josefina Pinheiro Marques, Ana, Rios Pinto, Ana, Krystopchuk, Andriy, Teresa, Ana, Manuel Pereira de Figueiredo, António, Botelho, Isabel, Duarte, Tiago, Costa, Vasco, Pedro Cunha, Rui, Molinos, Elena, Tito da Costa, Ledo, Sara, Queiró, Joana, Pascoalinho, Dulce, Nunes, Cristina, Pedro Moura, José, Pereira, Énio, Carvalho Mendes, António, Valeanu, Liana, Bubenek-Turconi, Serban, Marina Grintescu, Ioana, Cobilinschi, Cristian, Carmen Filipescu, Daniela, Elena Predoi, Cornelia, Tomescu, Dana, Popescu, Mihai, Marcu, Alexandra, Grigoras, Ioana, Lungu, Olguta, Gritsan, Alexey, Anderzhanova, Anastasia, Meleshkina, Yulia, Magomedov, Marat, Zubareva, Nadezhda, Tribulev, Maksim, Gaigolnik, Denis, Eremenko, Aleksandr, Vistovskaya, Natala, Chukina, Maria, Belskiy, Vladislav, Furman, Mikhail, Ferrer Rocca, Ricard, Martinez, Maria, Casares, Vanessa, Mellado Artigas, Ricard, Vera, Paula, Flores, Matias, Amador Amerigo, Joaquin, Gracia Arnillas, Maria Pilar, Munoz Bermudez, Rosana, Armestar, Fernando, Catalan, Beatriz, Roig, Regina, Raguer, Laura, Dolores Quesada, María, Diaz Santos, Emilio, Gomà, Gemma, Ubeda, Alejandro, Salgado, Maria, Forcelledo Espina, Lorena, Garcia Prieto, Emilio, Asensio, Mj, Rodriguez, M., Maseda, Dr Emilio, Suarez De La Rica, Alejandro, Ignacio Ayestaran, J., Novo, Mariana, Blasco-Navalpotro, Miguel Angel, Orejas Gallego, Alberto, Sjovall, Fredrik, Sjövall, Fredrik, Spahic, Dzana, Johan Svensson, Carl, Haney, Michael, Edin, Alicia, Åkerlund, Joyce, De Geer, Lina, Prazak, Josef, Buetti, Niccolò, Jakob, Stephan, Pagani, Jl, Abed-Maillard, S., Akova, Murat, Tarık Aslan, Abdullah, Tarik Aslan, Abdullah, Timuroglu, Arif, Kocagoz, Sesin, Kusoglu, Hulya, Mehtap, Selcuk, Ceyhun, Solakoğlu, Altintas, Dr. Neriman Defne, Talan, Leyla, Kayaaslan, Bircan, Kaya Kalem, Ayşe, Kurt, Dr. Ibrahim, Telli, Murat, Ozturk, Barcin, Erol, Çiğdem, Dindar Demiray, Emine Kubra, Çolak, Sait, Akbas, Türkay, Dr. Kursat Gundogan, Sari, Ali, Agalar, Canan, Çolak, Onur, Baykam, Nurcan (N), Akdogan, Ozlem (O), Yilmaz, Mesut, Tunay, Burcu, Cakmak, Rumeysa, Saltoglu, Nese, Karaali, Ridvan, Iftihar Koksal, Firdevs Aksoy, Eroglu, Ahmet, Tolga Saracoglu, Kemal, Bilir, Yeliz, Guzeldag, Seda, Ersoz, Gulden, Evik, Guliz, Sungurtekin, Hulya, Ozgen, Cansu, Erdoğan, Cem, Gürbüz, Yunus, Altin, Nilgün, Bayindir, Yasar, Ersoy, Yasemin, Goksu, Senay, Akyol, Ahmet, Dr, Kartal, Batirel, Ayse, Cagan Aktas, Sabahat, Morris, Andrew Conway, Conway Morris, Andrew, Routledge, Matthew, Ercole, Ari, Antcliffe, David, Rojo, Roceld, Tizard, Kate, Faulkner, Maria, Cowton, Amanda, Kent, Melanie, Raj, Ashok, Zormpa, Artemis, Tinaslanidis, George, Khade, Reena, Torlinski, Tomasz, Mulhi, Randeep, Goyal, Shraddha, Bajaj, Manan, Soltan, Marina, Yonan, Aimee, Dolan, Rachael, Johnson, Aimee, Macfie, Caroline, Lennard, James, Templeton, Maie, Sousa Arias, Sonia, Franke, Uwe, Hugill, Keith, Angell, Hollie, Benjamin J Parcell, Cobb, Katherine, Cole, Stephen, Smith, Tim, Graham, Clive, Cerman, Jaroslav, Keegan, Allison, Ritzema, Jenny, Sanderson, Amanda, Roshdy, Ashraf, Szakmany, Tamas, Baumer, Tom, Longbottom, Rebecca, Hall, Daniel, Tatham, Kate, Loftus, S., Husain, A., Black, E., Jhanji, S., Rao Baikady, R., Mcguigan, Peter, Mckee, Rachel, Kannan, Santhana, Antrolikar, Supriya, Marsden, Nicholas, Della Torre, Valentina, Banach, Dorota, Zaki, Ahmed, Jackson, Matthew, Chikungwa, Moses, Attwood, Ben, Patel, Jamie, Rebecca E Tilley, Humphreys, Sally K., Jean Renaud, Paul, Sokhan, Anton, Burma, Yaroslava, Sligl, Wendy, Baig, Nadia, McCoshen, Lorena, Kutsogiannis, Demetrios J., Thompson, Patricia, Hewer, Tayne, Rabbani, Raihan, Huq, Shihan Mahmud Redwanul, Hasan, Rajib, Motiul Islam, Mohammad, Gurjar, Mohan, Baronia, Arvind, Kothari, Nikhil, Sharma, Ankur, Karmakar, Saurabh, Sharma, Priya, Nimbolkar, Janardan, Samdani, Pratit, Vaidyanathan, R., Ahmedi Rubina, Noor, Jain, Nikhilesh, Pahuja, Madhumati, Singh, Ritu, Shekhar, Saurav, Syed, Nabeel Muzaffar, Ozair, Ahmad, Sarwar Siddiqui, Suhail, Bose, Payel, Datta, Avijatri, Rathod, Darshana, Patel, Mayur, MK, Renuka, Sailaja, K Baby, Dsilva, Carol, Chandran, Jagadish, Ghosh, Pralay, Mukherjee, Sudipta, Sheshala, Kaladhar, Chandra Misra, Krushna, Adekola, Oyebola O., Yusuf Yakubu, Saidu, Mgbosoro Ugwu, Euphemia, Olatosi, John (O), Desalu, Ibironke, Asiyanbi, Gabriel, Oladimeji, Motunrayo, Idowu, Olusola, Adeola, Fowotade, Mer, Mervyn, Mc Cree, Melanie, El Sanousi, Dr. Bashir, Adil Ali Karar, Ali, Saidahmed, Elfayadh, Hamid, Hytham K.S., Loiodice, Ambre, Bailly, Sébastien, Ruckly, Stéphane, and Staiquly, Quentin

Published
2024
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37. Super learning in the SAS system

Author

Keil, Alexander P., Westreich, Daniel, Edwards, Jessie K, and Cole, Stephen R

Subjects
Statistics - Machine Learning, Computer Science - Machine Learning
Abstract

Background and objective: Stacking is an ensemble machine learning method that averages predictions from multiple other algorithms, such as generalized linear models and regression trees. An implementation of stacking, called super learning, has been developed as a general approach to supervised learning and has seen frequent usage, in part due to the availability of an R package. We develop super learning in the SAS software system using a new macro, and demonstrate its performance relative to the R package. Methods: Following previous work using the R SuperLearner package we assess the performance of super learning in a number of domains. We compare the R package with the new SAS macro in a small set of simulations assessing curve fitting in a predictive model as well in a set of 14 publicly available datasets to assess cross-validated accuracy. Results: Across the simulated data and the publicly available data, the SAS macro performed similarly to the R package, despite a different set of potential algorithms available natively in R and SAS. Conclusions: Our super learner macro performs as well as the R package at a number of tasks. Further, by extending the macro to include the use of R packages, the macro can leverage both the robust, enterprise oriented procedures in SAS and the nimble, cutting edge packages in R. In the spirit of ensemble learning, this macro extends the potential library of algorithms beyond a single software system and provides a simple avenue into machine learning in SAS., Comment: 7 pages, 1 table, 3 figures

Published
2018

38. Compound Retention in Care and All-Cause Mortality among People Living with HIV

Author

Kay, Emma Sophia, Batey, D Scott, Westfall, Andrew O, Christopoulos, Katerina, Cole, Stephen R, Geng, Elvin H, Mathews, W Christopher, Moore, Richard D, and Mugavero, Michael J

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Clinical Research, Infectious Diseases, Prevention, HIV/AIDS, Infection, Good Health and Well Being, hazard ratio, mortality hazards, retention in care, Clinical sciences, Medical microbiology
Abstract

BackgroundTo obtain optimal health outcomes, persons living with human immunodeficiency virus (HIV) must be retained in clinical care. We examined the relationships between 4 possible combinations of 2 separate retention measures (missed visits and the Institute of Medicine [IOM] indicator) and all-cause mortality.MethodsThe sample included 4162 antiretroviral therapy (ART)-naive patients who started ART between January 2000 and July 2010 at any of 5 US sites of the Center for AIDS Research Network of Integrated Clinical Systems. The independent variable of interest was retention, captured over the 12-month period after the initiation of ART. The study outcome, all-cause mortality 1 year after ART initiation, was determined by querying the Social Security Death Index or the National Death Index. We evaluated the associations of the 4 categories of retention with all-cause mortality, using the Cox proportional hazards models.ResultsTen percent of patients did not meet retention standards for either measure (hazard ratio [HR], 2.26; 95% confidence interval [CI], 1.59-3.21). Patients retained by the IOM but not the missed-visits measure (42%) had a higher HR for mortality (1.72; 95% CI, 1.33-2.21) than patients retained by both measures (41%). Patients retained by the missed-visits but not the IOM measure (6%) had the same mortality hazards as patients retained by both measures (HR, 1.01; 95% CI, .54-1.87).ConclusionsMissed visits within the first 12 months of ART initiation are a major risk factor for subsequent death. Incorporating missed visits in clinical and public health retention and viral suppression programming is advised.

Published
2019

41. Bacterial Prostatitis Secondary to Salmonella enterica serovar Enteritidis in an Immunocompetent Dog.

Author

Harvey, Samantha, McEntee, Elisa, and Cole, Stephen

Abstract

Salmonella is a rod-shaped gram-negative bacterium of the family Enterobacteriaceae, commonly present in the gastrointestinal tract in humans and animals. Salmonella-associated bacteriuria and prostatitis are rare but have been reported in humans, predominantly older patients with underlying diseases, including urinary tract obstructions, diabetes mellitus, and compromised immunity. In dogs, Salmonella bacteriuria and prostatitis have only been described in patients on immunosuppressive medications. This study reports the case of a 7 yr old male Pit bull terrier mix with Salmonella prostatitis. The patient had a 3 day history of lethargy and anorexia. He was fed a commercial diet and had no previous medical or medication history. On physical examination, he had caudal abdominal pain and a firm, enlarged, painful prostate. Ultrasound revealed marked prostatomegaly with multifocal echogenic fluid-filled cavitations and regional peritonitis. Urine and prostatic fluid culture grew Salmonella (>100,000 colony-forming units/mL) using standard culture methods. Treatment with enrofloxacin was initiated for 8 wk. Repeat urine and prostatic cultures after cessation of antibiotics were negative, and serial fecal cultures were Salmonella negative. This case report is, to the best of our knowledge, the first to describe Salmonella prostatitis and bacteriuria in an immunocompetent dog who was not fed a raw diet. [ABSTRACT FROM AUTHOR]

Published
2024
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42. Diagnostic Accuracy of an Integrated AI Tool to Estimate Gestational Age From Blind Ultrasound Sweeps.

Author

Stringer, Jeffrey S. A., Pokaprakarn, Teeranan, Prieto, Juan C., Vwalika, Bellington, Chari, Srihari V., Sindano, Ntazana, Freeman, Bethany L., Sikapande, Bridget, Davis, Nicole M., Sebastião, Yuri V., Mandona, Nelly M., Stringer, Elizabeth M., Benabdelkader, Chiraz, Mungole, Mutinta, Kapilya, Filson M., Almnini, Nariman, Diaz, Arieska N., Fecteau, Brittany A., Kosorok, Michael R., and Cole, Stephen R.

Subjects
PREGNANT women, IMAGE analysis, RESOURCE-limited settings, GESTATIONAL age, BODY mass index
Abstract

Key Points: Question: Can novice clinicians accurately estimate gestational age using a low-cost, battery-powered ultrasonography probe with integrated artificial intelligence (AI) image interpretation? Findings: This prospective study enrolled 400 pregnant individuals with due dates confirmed by first-trimester ultrasonography. At follow-up visits randomly assigned throughout gestation, novice clinicians using an AI-enabled device estimated gestational age as accurately as credentialed sonographers using traditional ultrasonography devices (difference, 0.2 days). Meaning: Obstetrical care in low-resource settings may benefit from reliable gestational age assessment using AI integration with point-of-care ultrasonography. Importance: Accurate assessment of gestational age (GA) is essential to good pregnancy care but often requires ultrasonography, which may not be available in low-resource settings. This study developed a deep learning artificial intelligence (AI) model to estimate GA from blind ultrasonography sweeps and incorporated it into the software of a low-cost, battery-powered device. Objective: To evaluate GA estimation accuracy of an AI-enabled ultrasonography tool when used by novice users with no prior training in sonography. Design, Setting, and Participants: This prospective diagnostic accuracy study enrolled 400 individuals with viable, single, nonanomalous, first-trimester pregnancies in Lusaka, Zambia, and Chapel Hill, North Carolina. Credentialed sonographers established the "ground truth" GA via transvaginal crown-rump length measurement. At random follow-up visits throughout gestation, including a primary evaluation window from 14 0/7 weeks' to 27 6/7 weeks' gestation, novice users obtained blind sweeps of the maternal abdomen using the AI-enabled device (index test) and credentialed sonographers performed fetal biometry with a high-specification machine (study standard). Main Outcomes and Measures: The primary outcome was the mean absolute error (MAE) of the index test and study standard, which was calculated by comparing each method's estimate to the previously established GA and considered equivalent if the difference fell within a prespecified margin of ±2 days. Results: In the primary evaluation window, the AI-enabled device met criteria for equivalence to the study standard, with an MAE (SE) of 3.2 (0.1) days vs 3.0 (0.1) days (difference, 0.2 days [95% CI, −0.1 to 0.5]). Additionally, the percentage of assessments within 7 days of the ground truth GA was comparable (90.7% for the index test vs 92.5% for the study standard). Performance was consistent in prespecified subgroups, including the Zambia and North Carolina cohorts and those with high body mass index. Conclusions and Relevance: Between 14 and 27 weeks' gestation, novice users with no prior training in ultrasonography estimated GA as accurately with the low-cost, point-of-care AI tool as credentialed sonographers performing standard biometry on high-specification machines. These findings have immediate implications for obstetrical care in low-resource settings, advancing the World Health Organization goal of ultrasonography estimation of GA for all pregnant people. Trial Registration: ClinicalTrials.gov Identifier: NCT05433519 This prospective diagnostic study examines the accuracy of an AI-enabled ultrasonography tool in estimating gestational age when used by those with no prior training in sonography. [ABSTRACT FROM AUTHOR]

Published
2024
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44. Sensitivity Analyses for Misclassification of Cause of Death in the Parametric G-Formula.

Author

Edwards, Jessie K, Cole, Stephen R, Moore, Richard D, Mathews, W Christopher, Kitahata, Mari, and Eron, Joseph J

Subjects
Epidemiology, Health Sciences, Prevention, Infection, Good Health and Well Being, Acquired Immunodeficiency Syndrome, Antiretroviral Therapy, Highly Active, Cause of Death, Epidemiologic Methods, Female, HIV Infections, Humans, Male, Models, Statistical, Risk Assessment, Risk Factors, Sensitivity and Specificity, United States, cause of death, HIV, outcome measurement errors, Mathematical Sciences, Medical and Health Sciences
Abstract

Cause-specific mortality is an important outcome in studies of interventions to improve survival, yet causes of death can be misclassified. Here, we present an approach to performing sensitivity analyses for misclassification of cause of death in the parametric g-formula. The g-formula is a useful method to estimate effects of interventions in epidemiologic research because it appropriately accounts for time-varying confounding affected by prior treatment and can estimate risk under dynamic treatment plans. We illustrate our approach using an example comparing acquired immune deficiency syndrome (AIDS)-related mortality under immediate and delayed treatment strategies in a cohort of therapy-naive adults entering care for human immunodeficiency virus infection in the United States. In the standard g-formula approach, 10-year risk of AIDS-related mortality under delayed treatment was 1.73 (95% CI: 1.17, 2.54) times the risk under immediate treatment. In a sensitivity analysis assuming that AIDS-related death was measured with sensitivity of 95% and specificity of 90%, the 10-year risk ratio comparing AIDS-related mortality between treatment plans was 1.89 (95% CI: 1.13, 3.14). When sensitivity and specificity are unknown, this approach can be used to estimate the effects of dynamic treatment plans under a range of plausible values of sensitivity and specificity of the recorded event type.

Published
2018

45. At-Risk Alcohol Use Among HIV-Positive Patients and the Completion of Patient-Reported Outcomes

Author

Rudolph, Jacqueline E, Cole, Stephen R, Edwards, Jessie K, Moore, Richard, O’Cleirigh, Conall, Mathews, Wm Christopher, Christopoulos, Katerina, and For the Center for AIDS Research Network of Integrated Clinical Systems

Subjects
Public Health, Health Sciences, Substance Misuse, HIV/AIDS, Clinical Research, Alcoholism, Alcohol Use and Health, Prevention, Cardiovascular, Oral and gastrointestinal, Good Health and Well Being, Adult, Alcohol Drinking, Alcohol-Related Disorders, Female, HIV Infections, Humans, Male, Middle Aged, Patient Reported Outcome Measures, Quality of Health Care, Patient-reported outcomes, PROs, HIV, Alcohol consumption, Selection bias, Center for AIDS Research Network of Integrated Clinical Systems, Public Health and Health Services, Social Work, Public health
Abstract

Heavy drinking is prevalent among people living with HIV. Studies use tools like patient-reported outcomes (PROs) to quantify alcohol use in a detailed, timely manner. However, if alcohol misuse influences PRO completion, selection bias may result. Our study included 14,145 adult HIV patients (133,036 visits) from CNICS who were eligible to complete PROs at an HIV primary care visit. We compared PRO completion proportions between patients with and without a clinical diagnosis of at-risk alcohol use in the prior year. We accounted for confounding by baseline and visit-specific covariates. PROs were completed at 20.8% of assessed visits. The adjusted difference in PRO completion proportions was -3.2% (95% CI -5.6 to -0.8%). The small association between receipt of an at-risk alcohol use diagnosis and decreased PRO completion suggests there could be modest selection bias in studies using the PRO alcohol measure.

Published
2018

46. Chronic hepatitis C virus infection and subsequent HIV viral load among women with HIV initiating antiretroviral therapy

Author

Willis, Sarah J, Cole, Stephen R, Westreich, Daniel, Edmonds, Andrew, Hurt, Christopher B, Albrecht, Svenja, Anastos, Kathryn, Augenbraun, Michael, Fischl, Margaret, French, Audrey L, Kalapila, Aley G, Karim, Roksana, Peters, Marion G, Plankey, Michael, Seaberg, Eric C, Tien, Phyllis C, and Adimora, Adaora A

Subjects
Medical Microbiology, Biomedical and Clinical Sciences, Public Health, Health Sciences, Digestive Diseases, Infectious Diseases, Chronic Liver Disease and Cirrhosis, Substance Misuse, Clinical Research, Liver Disease, Hepatitis, HIV/AIDS, Hepatitis - C, Emerging Infectious Diseases, Drug Abuse (NIDA only), Infection, Good Health and Well Being, Adult, Anti-Retroviral Agents, Female, Follow-Up Studies, HIV, HIV Infections, Hepatitis C, Chronic, Humans, Longitudinal Studies, Middle Aged, Prospective Studies, RNA, Viral, Treatment Outcome, Viral Load, antiretroviral therapy, hepatitis C virus coinfection, hepatitis C virus, HIV infection, viral load, women, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Virology, Biomedical and clinical sciences, Health sciences
Abstract

ObjectivesOne in four persons living with HIV is coinfected with hepatitis C virus (HCV). Biological and behavioral mechanisms may increase HIV viral load among coinfected persons. Therefore, we estimated the longitudinal effect of chronic HCV on HIV suppression after ART initiation among women with HIV (WWH).DesignHIV RNA was measured every 6 months among 441 WWH in the Women's Interagency HIV Study who initiated ART from 2000 to 2015.MethodsLog-binomial regression models were used to compare the proportion of study visits with detectable HIV RNA between women with and without chronic HCV. Robust sandwich variance estimators accounted for within-person correlation induced by repeated HIV RNA measurements during follow-up. We controlled for confounding and selection bias (because of loss to follow-up and death) using inverse probability-of-exposure-and-censoring weights.ResultsOne hundred and fourteen women (25%) had chronic HCV before ART initiation. Overall, the proportion of visits with detectable HIV RNA was similar among women with and without chronic HCV [relative risk (RR) 1.19 (95% CI 0.72, 1.95)]. Six months after ART initiation, the proportion of visits with detectable HIV RNA among women with chronic HCV was 1.88 (95% CI 1.41-2.51) times that among women without HCV, at 2 years, the ratio was 1.60 (95% CI 1.17-2.19), and by 6 years there was no difference (1.03; 95% CI 0.60-1.79).ConclusionChronic HCV may negatively impact early HIV viral response to ART. These findings reaffirm the need to test persons with HIV for HCV infection, and increase engagement in HIV care and access to HCV treatment among persons with HIV/HCV coinfection.

Published
2018

47. Evaluating the Population Impact on Racial/Ethnic Disparities in HIV in Adulthood of Intervening on Specific Targets: A Conceptual and Methodological Framework

Author

Howe, Chanelle J, Dulin-Keita, Akilah, Cole, Stephen R, Hogan, Joseph W, Lau, Bryan, Moore, Richard D, Mathews, W Christopher, Crane, Heidi M, Drozd, Daniel R, Geng, Elvin, Boswell, Stephen L, Napravnik, Sonia, Eron, Joseph J, Mugavero, Michael J, and Systems, for the CFAR Network of Integrated Clinical

Subjects
Epidemiology, Health Sciences, Sexually Transmitted Infections, Clinical Research, Behavioral and Social Science, Prevention, Minority Health, Infectious Diseases, HIV/AIDS, Good Health and Well Being, Adult, Anti-HIV Agents, Ethnicity, Female, HIV, HIV Infections, Health Status Disparities, Healthcare Disparities, Humans, Male, Observational Studies as Topic, Racial Groups, United States, health status disparities, CFAR Network of Integrated Clinical Systems, Mathematical Sciences, Medical and Health Sciences
Abstract

Reducing racial/ethnic disparities in human immunodeficiency virus (HIV) disease is a high priority. Reductions in HIV racial/ethnic disparities can potentially be achieved by intervening on important intermediate factors. The potential population impact of intervening on intermediates can be evaluated using observational data when certain conditions are met. However, using standard stratification-based approaches commonly employed in the observational HIV literature to estimate the potential population impact in this setting may yield results that do not accurately estimate quantities of interest. Here we describe a useful conceptual and methodological framework for using observational data to appropriately evaluate the impact on HIV racial/ethnic disparities of interventions. This framework reframes relevant scientific questions in terms of a controlled direct effect and estimates a corresponding proportion eliminated. We review methods and conditions sufficient for accurate estimation within the proposed framework. We use the framework to analyze data on 2,329 participants in the CFAR [Centers for AIDS Research] Network of Integrated Clinical Systems (2008-2014) to evaluate the potential impact of universal prescription of and ≥95% adherence to antiretroviral therapy on racial disparities in HIV virological suppression. We encourage the use of the described framework to appropriately evaluate the potential impact of targeted interventions in addressing HIV racial/ethnic disparities using observational data.

Published
2018

48. Virologic suppression and CD4+ cell count recovery after initiation of raltegravir or efavirenz-containing HIV treatment regimens

Author

Edwards, Jessie K, Cole, Stephen R, Hall, H Irene, Mathews, W Christopher, Moore, Richard D, Mugavero, Michael J, and Eron, Joseph J

Subjects
Medical Microbiology, Biomedical and Clinical Sciences, Clinical Trials and Supportive Activities, HIV/AIDS, Clinical Research, Infectious Diseases, 6.1 Pharmaceuticals, Evaluation of treatments and therapeutic interventions, Management of diseases and conditions, 7.1 Individual care needs, Infection, Adolescent, Adult, Alkynes, Anti-HIV Agents, Antiretroviral Therapy, Highly Active, Benzoxazines, CD4 Lymphocyte Count, Cohort Studies, Cyclopropanes, Female, HIV Infections, Humans, Male, Middle Aged, Raltegravir Potassium, Survival Analysis, Time Factors, Treatment Outcome, United States, Viral Load, Young Adult, efavirenz, HIV, HIV integrase inhibitors, sustained virologic response, viral load, CNICS investigators, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Virology, Biomedical and clinical sciences, Health sciences
Abstract

ObjectiveTo explore the effectiveness of raltegravir-based antiretroviral therapy (ART) on treatment response among ART-naive patients seeking routine clinical care.DesignCohort study of adults enrolled in HIV care in the United States.MethodsWe compared virologic suppression and CD4 cell count recovery over a 2.5 year period after initiation of an ART regimen containing raltegravir or efavirenz using observational data from a US clinical cohort, generalized to the US population of people with diagnosed HIV. We accounted for nonrandom treatment assignment, informative censoring, and nonrandom selection from the US target population using inverse probability weights.ResultsOf the 2843 patients included in the study, 2476 initiated the efavirenz-containing regimen and 367 initiated the raltegravir-containing regimen. In the weighted intent-to-treat analysis, patients spent an average of 74 (95% confidence interval: 41, 106) additional days alive with a suppressed viral load on the raltegravir regimen than on the efavirenz regimen over the 2.5-year study period. CD4 cell count recovery was also superior under the raltegravir regimen.ConclusionPatients receiving raltegravir spent more time alive and suppressed than patients receiving efavirenz, but the probability of viral suppression by 2.5 years after treatment was similar between groups. Optimizing the amount of time spent in a state of viral suppression is important to improve survival among people living with HIV and to reduce onward transmission.

Published
2018

49. Estimating multiple time‐fixed treatment effects using a semi‐Bayes semiparametric marginal structural Cox proportional hazards regression model

Author

Cole, Stephen R, Edwards, Jessie K, Westreich, Daniel, Lesko, Catherine R, Lau, Bryan, Mugavero, Michael J, Mathews, W Christopher, Eron, Joseph J, Greenland, Sander, and Investigators, for the CNICS

Subjects
Mathematical Sciences, Statistics, Anti-HIV Agents, Bayes Theorem, Biometry, HIV Infections, Humans, Models, Statistical, Proportional Hazards Models, Regression Analysis, bias, causal inference, cohort study, semi-Bayes, semiparametric, survival analysis, CNICS Investigators, Statistics & Probability
Abstract

Marginal structural models for time-fixed treatments fit using inverse-probability weighted estimating equations are increasingly popular. Nonetheless, the resulting effect estimates are subject to finite-sample bias when data are sparse, as is typical for large-sample procedures. Here we propose a semi-Bayes estimation approach which penalizes or shrinks the estimated model parameters to improve finite-sample performance. This approach uses simple symmetric data-augmentation priors. Limited simulation experiments indicate that the proposed approach reduces finite-sample bias and improves confidence-interval coverage when the true values lie within the central "hill" of the prior distribution. We illustrate the approach with data from a nonexperimental study of HIV treatments.

Published
2018

50. Cancer risk in HIV patients with incomplete viral suppression after initiation of antiretroviral therapy

Author

Lee, Jennifer S, Cole, Stephen R, Achenbach, Chad J, Dittmer, Dirk P, Richardson, David B, Miller, William C, Mathews, Christopher, Althoff, Keri N, Moore, Richard D, and Eron, Joseph J

Subjects
Biomedical and Clinical Sciences, Public Health, Health Sciences, Clinical Research, Sexually Transmitted Infections, Cancer, Infectious Diseases, HIV/AIDS, Prevention, Infection, Good Health and Well Being, Adult, Anti-HIV Agents, Antiretroviral Therapy, Highly Active, Defective Viruses, Female, HIV Infections, HIV-1, Humans, Middle Aged, Neoplasms, RNA, Viral, Risk Factors, Viral Load, Center for AIDS Research (CFAR) Network of Integrated Clinical Systems, General Science & Technology
Abstract

BackgroundCancer causes significant morbidity and mortality among HIV patients in the US due to extended life expectancy with access to effective antiretroviral therapy. Low, detectable HIV RNA has been studied as a risk factor for adverse health outcomes, but its clinical impact on cancer risk remains unclear. The objective of this study was to determine whether HIV RNA 999 copies/mL. We calculated estimates of the cumulative incidence of cancer diagnosis, accounting for death as a competing event. Inverse probability of exposure and censoring weights were used to control for confounding and differential loss to follow up, respectively.ResultsCrude 10-year first cancer risk in the study sample was 7.03% (95% CI: 6.08%, 7.98%), with the highest risk observed among patients with viral loads between 200 and 999 copies/mL six months after ART initiation (10.7%). After controlling for baseline confounders, 10-year first cancer risk was 6.90% (95% CI: 5.69%, 8.12%), and was similar across viral load categories.ConclusionOverall risk of first cancer was not associated with incomplete viral suppression; however, cancer remains a significant threat to HIV patients after treatment initiation. As more HIV patients gain access to treatment in the current "treat all" era, occurrences of incomplete viral suppression will be observed more frequently in clinical practice, which supports continued study of the role of low-level HIV RNA on cancer development.

Published
2018

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