Search

Much research effort is invested in attempting to determine causal influences on disease onset and progression to inform prevention and treatment efforts. However, this is often dependent on observational data that are prone to well-known limitations, particularly residual confounding and reverse causality. Several statistical methods have been developed to support stronger causal inference. However, a complementary approach is to use design-based methods for causal inference, which acknowledge sources of bias and attempt to mitigate these through the design of the study rather than solely through statistical adjustment. Genetically informed methods provide a novel and potentially powerful extension to this approach, accounting by design for unobserved genetic and environmental confounding. No single approach will be absent from bias. Instead, we should seek and combine evidence from multiple methodologies that each bring different (and ideally uncorrelated) sources of bias. If the results of these different methodologies align-or triangulate-then we can be more confident in our causal inference. To be truly effective, this should ideally be done prospectively, with the sources of evidence specified in advance, to protect against one final source of bias-our own cognitions, expectations, and fondly held beliefs.

Background Previous research has suggested that statistical power is suboptimal in many biomedical disciplines, but it is unclear whether power is better in trials for particular interventions, disorders, or outcome types. We therefore performed a detailed examination of power in trials of psychotherapy, pharmacotherapy, and complementary and alternative medicine (CAM) for mood, anxiety, and psychotic disorders. Methods We extracted data from the Cochrane Database of Systematic Reviews (Mental Health). We focused on continuous efficacy outcomes and estimated power to detect predetermined effect sizes (standardized mean difference [SMD] = 0.20–0.80, primary SMD = 0.40) and meta-analytic effect sizes (ESMA). We performed meta-regression to estimate the influence of including underpowered studies in meta-analyses. Results We included 256 reviews with 10 686 meta-analyses and 47 384 studies. Statistical power for continuous efficacy outcomes was very low across intervention and disorder types (overall median [IQR] power for SMD = 0.40: 0.32 [0.19–0.54]; for ESMA: 0.23 [0.09–0.58]), only reaching conventionally acceptable levels (80%) for SMD = 0.80. Median power to detect the ESMA was higher in treatment-as-usual (TAU)/waitlist-controlled (0.49–0.63) or placebo-controlled (0.12–0.38) trials than in trials comparing active treatments (0.07–0.13). Adequately-powered studies produced smaller effect sizes than underpowered studies (B = −0.06, p ⩽ 0.001). Conclusions Power to detect both predetermined and meta-analytic effect sizes in psychiatric trials was low across all interventions and disorders examined. Consistent with the presence of reporting bias, underpowered studies produced larger effect sizes than adequately-powered studies. These results emphasize the need to increase sample sizes and to reduce reporting bias against studies reporting null results to improve the reliability of the published literature.

BackgroundFixed-effect meta-analysis has been used to summarize genetic effects on a phenotype across multiple Genome-Wide Association Studies (GWAS) assuming a common underlying genetic effect. Genetic effects may vary with age, therefore meta-analysing GWAS of age-diverse samples could be misleading. Meta-regression allows adjustment for study specific characteristics and models heterogeneity between studies. The aim of this study was to explore the use of meta-analysis and meta-regression for estimating age-varying genetic effects on phenotypes.MethodsWith simulations we compared the performance of meta-regression to fixed-effect and random -effects meta-analyses in estimating (i) main genetic effects and (ii) age-varying genetic effects (SNP by age interactions) from multiple GWAS studies under a range of scenarios. We applied meta-regression on publicly available summary data to estimate the main and age-varying genetic effects of theFTOSNP rs9939609 on Body Mass Index (BMI).ResultsFixed-effect and random-effects meta-analyses accurately estimated genetic effects when these did not change with age. Meta-regression accurately estimated both the main genetic effects and the age-varying genetic effects. When the number of studies or the age-diversity between studies was low, meta-regression had limited power. In the applied example, each additional minor allele (A) of rs9939609 was inversely associated with BMI at ages 0 to 3, and positively associated at ages 5.5 to 13. This is similar to the association that has been previously reported by a study that used individual participant data.ConclusionsGWAS using summary statistics from age-diverse samples should consider using meta-regression to explore age-varying genetic effects.KEY MESSAGESMeta-analysis has been used to summarize genetic effects on a phenotype across multiple Genome-Wide Association Studies (GWAS) assuming a common underlying genetic effect for all studies. However, genetic effects may vary with age, therefore meta-analysing GWAS of age-diverse samples could produce misleading results.Meta-regression could be used to relate observed between-study heterogeneity to study characteristics such as age. Therefore, meta-regression could be used to combine summary level GWAS data to provide evidence for any age-varying genetic effects.This simulation study shows that when genetic effects vary with age, meta-regression provides unbiased estimates of main and age-varying genetic effects. The precision of the estimates depends on the number of studies included, and the diversity in age between them.The applied example using publicly available summary data, supported the simulation study.By applying meta-regression, we observed a previously reported age-varying association between each additional minor allele (A) of rs9939609 and BMI; an inverse at ages 0 to 3 and a positive association at ages 5.5 to 13.Similar association has been previously reported by a study that used individual participant data.

Background Paediatric chronic fatigue syndrome or myalgic encephalomyelitis (CFS/ME) is a common illness with a major impact on quality of life. Recovery is poorly understood. Our aim was to describe definitions of recovery in paediatric CFS/ME, the rate of recovery and the time to recovery. Methods This systematic review included a detailed search of MEDLINE, EMBASE, PsycInfo and Cochrane Library between 1994 and July 2018. Inclusion criteria were (1) clinical trials and observational studies, (2) participants aged, Significant differences in definitions of recovery, result in highly varied reported recovery rates for chronic fatigue syndrome. Consensus definitions of recovery are needed, the authors recommend that recovery may be best defined from the child's own perspective.

Despite a major increase in the range and number of software offerings now available to help researchers produce evidence syntheses, there is currently no generic tool for producing figures to display and explore the risk-of-bias assessments that routinely take place as part of systematic review. However, tools such as the R programming environment and Shiny (an R package for building interactive web apps) have made it straightforward to produce new tools to help in producing evidence syntheses. We present a new tool, robvis (Risk-Of-Bias VISualization), available as an R package and web app, which facilitates rapid production of publication-quality risk-of-bias assessment figures. We present a timeline of the tool's development and its key functionality.

Background The use of Mendelian randomization (MR) in epidemiology has increased considerably in recent years, with a subsequent increase in systematic reviews of MR studies. We conducted a systematic review of tools designed for assessing risk of bias and/or quality of evidence in MR studies and a review of systematic reviews of MR studies. Methods We systematically searched MEDLINE, Embase, the Web of Science, preprints servers and Google Scholar for articles containing tools for assessing, conducting and/or reporting MR studies. We also searched for systematic reviews and protocols of systematic reviews of MR studies. From eligible articles we collected data on tool characteristics and content, as well as details of narrative description of bias assessment. Results Our searches retrieved 2464 records to screen, from which 14 tools, 35 systematic reviews and 38 protocols were included in our review. Seven tools were designed for assessing risk of bias/quality of evidence in MR studies and evaluation of their content revealed that all seven tools addressed the three core assumptions of instrumental variable analysis, violation of which can potentially introduce bias in MR analysis estimates. Conclusion We present an overview of tools and methods to assess risk of bias/quality of evidence in MR analysis. Issues commonly addressed relate to the three standard assumptions of instrumental variables analyses, the choice of genetic instrument(s) and features of the population(s) from which the data are collected (particularly in two-sample MR), in addition to more traditional non-MR-specific epidemiological biases. The identified tools should be tested and validated for general use before recommendations can be made on their widespread use. Our findings should raise awareness about the importance of bias related to MR analysis and provide information that is useful for assessment of MR studies in the context of systematic reviews.

BackgroundEvidence on the impact of the pandemic on healthcare presentations for self-harm has accumulated rapidly. However, existing reviews do not include studies published beyond 2020.AimsTo systematically review evidence on presentations to health services following self-harm during the COVID-19 pandemic.MethodA comprehensive search of databases (WHO COVID-19 database; Medline; medRxiv; Scopus; PsyRxiv; SocArXiv; bioRxiv; COVID-19 Open Research Dataset, PubMed) was conducted. Studies published from 1 January 2020 to 7 September 2021 were included. Study quality was assessed with a critical appraisal tool.ResultsFifty-one studies were included: 57% (29/51) were rated as ‘low’ quality, 31% (16/51) as ‘moderate’ and 12% (6/51) as ‘high-moderate’. Most evidence (84%, 43/51) was from high-income countries. A total of 47% (24/51) of studies reported reductions in presentation frequency, including all six rated as high-moderate quality, which reported reductions of 17–56%. Settings treating higher lethality self-harm were overrepresented among studies reporting increased demand. Two of the three higher-quality studies including study observation months from 2021 reported reductions in self-harm presentations. Evidence from 2021 suggests increased numbers of presentations among adolescents, particularly girls.ConclusionsSustained reductions in numbers of self-harm presentations were seen into the first half of 2021, although this evidence is based on a relatively small number of higher-quality studies. Evidence from low- and middle-income countries is lacking. Increased numbers of presentations among adolescents, particularly girls, into 2021 is concerning. Findings may reflect changes in thresholds for help-seeking, use of alternative sources of support and variable effects of the pandemic across groups.

2AbstractBackgroundThere is some evidence that circulating blood lipids play a role in the development of Alzheimer’s disease (AD) and dementia. These modifiable risk factors could be targeted by existing lipid-regulating agents, including statins, for dementia prevention. Here, we test the association between lipid-regulating agents and incidence of dementia and related conditions in the Clinical Practice Research Datalink (CPRD), an United Kingdom-based electronic health record database.MethodsA retrospective cohort study was performed using routinely collected CPRD data (January 1995 and March 2016). Multivariable Cox proportional hazard models, allowing for a time-varying treatment indicator, were used to estimate the association between seven lipid-regulating drug classes (vs. no drug) and five dementia outcomes (all-cause, vascular and other dementias, and probable and possible Alzheimer’s disease).ResultsWe analyzed 1,684,564 participants with a total follow-up of 10,835,685 patient-years (median: 5.9 years (IQR:2.7-9.7)). We found little evidence that lipid-regulating agents were associated with incidence of Alzheimer’s disease (probable HR:0.98, 95%CI:0.94-1.01; possible HR:0.97, 95%CI:0.93-1.01), but there was evidence of an increased risk of all-cause (HR:1.17, 95%CI:1.14-1.19), vascular (HR:1.81, 95%CI:1.73-1.89) and other dementias (HR:1.19, 95%CI:1.15-1.24). Evidence from a number of control outcomes indicated the presence of substantial residual confounding by indication (ischaemic heart disease HR: 1.62, 95%CI: 1.59-1.64; backpain HR: 1.04, 95%CI: 1.03-1.05; and Type 2 diabetes HR: 1.50, 95%CI: 1.48-1.51).ConclusionLipid-regulating agents were not associated with reduced Alzheimer’s disease risk. There was some evidence of an increased the risk of all-cause, vascular and other dementias, likely due to residual confounding by indication.3Key messagesA large cohort of patients from the Clinical Practice Research Datalink (CPRD) electronic health record database was assembled to examine the association of lipid-regulating agents, such as statins, with dementia outcomes.There was little evidence that lipid-regulating agents were associated with Alzheimer’s disease, but there was some evidence for a harmful association with all-cause, vascular and other dementias. In all cases, the estimated associations were driven by the any statin subgroup, which comprised most participants in our cohort.Evidence from the control outcome analyses indicated strong residual confounding by indication, mostly likely related to vascular factors.

BackgroundTwo-sample Mendelian randomization (2SMR) is an increasingly popular epidemiological method that uses genetic variants as instruments for making causal inferences. Clear reporting of methods employed in such studies is important for evaluating their underlying quality. However, the quality of methodological reporting of 2SMR studies is currently unclear.ObjectivesWe aimed to assess the reporting quality of studies that used MR-Base, one of the most popular platforms for implementing 2SMR analysis.MethodsWe created a bespoke reporting checklist to evaluate reporting quality of 2SMR studies. We then searched Web of Science Core Collection, PsycInfo, MEDLINE, EMBASE and Google Scholar citations of the MR-Base descriptor paper to identify published MR studies that used MR-Base for any component of the MR analysis. Study screening and data extraction were performed by at least two independent reviewers.Results87 studies were included in the primary analysis. Reporting quality was generally poor across studies with a mean of 53% (SD = 14%) of items reported in each study. Many items required for evaluating the validity of key assumptions made in MR were poorly reported: only 44% of studies provided sufficient details for assessing if the genetic variant associates with the exposure (‘relevance’ assumption), 31% for assessing if there are any variant-outcome confounders (‘independence’ assumption), 89% for the assessing if the variant causes the outcome independently of the exposure (‘exclusion restriction’ assumption), and 32% for assumptions of falsification tests. We did not find evidence of a change in reporting quality over time or a difference in reporting quality between studies that used MR-Base and a random sample of MR studies that did not use this platform.DiscussionThe quality of reporting of two-sample Mendelian randomization studies in our sample was generally poor. Journals and researchers should implement the STROBE-MR guidelines to improve reporting quality.Other: FundingESRC, CRUK, MRC, John Climax Benevolent Fund, University of Bristol, and the Wellcome Trust. Registration: This study pre-registered on the OSF, and the protocol can be found at DOI 10.17605/OSF.IO/NFM27

IntroductionHead and neck cancer squamous cell carcinoma (HNSCC) is the 6th most common cancer internationally. Established risk factors include smoking, alcohol and presence of human papillomavirus (HPV). The incidence rate of new disease continues to rise, despite falls in alcohol consumption and a reduction in smoking, the rising rates are unlikely to be solely attributed to HPV status alone. Obesity and its associated conditions such as type 2 diabetes (T2D) are implicated in the risk and progression of a variety of cancers but there is paucity of evidence regarding its role in HNSCC.Methods and analysisA systematic review of cohort studies, reporting a risk of incident head and neck squamous cell carcinoma will be included. A systematic search strategy has been developed, multiple databases will be searched from January 1966, including Cochrane Library, OVID SP versions of Medline and EMBASE. The primary outcome will be incident HNSCC based on exposures of type 2 diabetes, obesity, dyslipidemia and hypertension as defined by the World Health Organisation (WHO). A combined risk effect across studies will be calculated using meta-analysis, although depending on the heterogeneity in study design, exposure and outcome reporting this may not be possible.Ethics and disseminationNo ethical approval is required for this systematic review. The review will be published in a revelant peer-review journal and findings will be presented at scientific meetings in both poster and oral presentation form.Registration detailsThis study has been registered with the international prospective register of systematic reviews (PROSPERO) with study registration number CRD42021250520. This protocol has been developed in accordance with the preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) guidance statement.Article SummaryStrengths and limitations of this studyThis systematic review will be the first to comprehensively review the literature and provide a meta-analysis of the effect of metabolic disorders and the risk of incident HNSCC.The publication of this protocol provides a clear representation of the methods used in this review for transparency and to prevent future duplication.This systematic review will be one of the first to pilot the The ROBINS-E tool (Risk Of Bias In Non-randomised Studies - of Exposures).Despite the metabolic disorders being described as separate entities, the authors recognise the potential for disease processes to be related.Among cohort studies there is significant variability in terms of length of follow-up for the outcome of interest, HNSCC.

There is widespread concern over the potential impact of the COVID-19 pandemic on suicide and self-harm globally, particularly in low- and middle-income countries (LMIC) where the burden of these behaviours is greatest. We synthesised the evidence from the published literature on the impact of the pandemic on suicide and self-harm in LMIC. This review is nested within a living systematic review (PROSPERO ID CRD42020183326) that continuously identifies published evidence (all languages) through a comprehensive automated search of multiple databases (PubMed; Scopus; medRxiv, PsyArXiv; SocArXiv; bioRxiv; the WHO COVID-19 database; and the COVID-19 Open Research Dataset by Semantic Scholar (up to 11/2020), including data from Microsoft Academic, Elsevier, arXiv and PubMed Central.) All articles identified by the 4th August 2021 were screened. Papers reporting on data from a LMIC and presenting evidence on the impact of the pandemic on suicide or self-harm were included. Methodological quality was assessed using an appropriate tool, and a narrative synthesis presented. A total of 22 studies from LMIC were identified representing data from 12 countries. There was an absence of data from Africa, the Pacific, and the Caribbean. The reviewed studies mostly report on the early months of COVID-19 and were generally methodologically poor. Few studies directly assessed the impact of the pandemic. The most robust evidence, from time-series studies, indicate either a reduction or no change in suicide and self-harm behaviour. As LMIC continue to experience repeated waves of the virus and increased associated mortality, against a backdrop of vaccine inaccessibility and limited welfare support, continued efforts are needed to track the indirect impact of the pandemic on suicide and self-harm in these countries.

Bias can arise because of the actions of primary study investigators or because of the actions of review authors, or may be unavoidable due to constraints on how research can be undertaken in practice. Where possible, assessments of risk of bias in a systematic review should be informed by evidence. This chapter summarizes some of the key evidence about bias that informs our guidance on risk-of-bias assessments in Cochrane Reviews. Many results are often available in trial reports, so review authors should think carefully about which results to assess for risk of bias. Review authors who restrict their primary analysis in this way are encouraged to perform sensitivity analyses to show how conclusions might be affected if studies at a high risk of bias were included. The chapter also addresses how source of funding and conflicts of interest of study authors may impact on study design, conduct and reporting.

BackgroundMany studies have examined ‘non-specific’ vaccine effects on infant mortality: attention has been particularly drawn to diphtheria-tetanus-pertussis (DTP) vaccine, which has been proposed to be associated with an increased mortality risk. Both right and left censoring are common in such studies.MethodsWe conducted simulation studies examining right censoring (at measles vaccination) and left censoring (by excluding early follow-up) in a variety of scenarios in which confounding was and was not present. We estimated both unadjusted and adjusted hazard ratios (HRs), averaged across simulations.ResultsWe identified scenarios in which right-censoring at measles vaccination was informative and so introduced bias in the direction of a detrimental effect of DTP vaccine. In some, but not all, situations, adjusting for confounding by health status removed the bias caused by censoring. However, such adjustment will not always remove bias due to informative censoring: inverse probability weighting was required in one scenario. Bias due to left censoring arose when both health status and DTP vaccination were associated with mortality during the censored early follow-up and was in the direction of attenuating a beneficial effect of DTP on mortality. Such bias was more severe when the effect of DTP changed over time.ConclusionsEstimates of non-specific effects of vaccines may be biased by informative right or left censoring. Authors of studies estimating such effects should consider the potential for such bias and use appropriate statistical approaches to control for it. Such approaches require measurement of prognostic factors that predict censoring.

Selective outcome reporting and publication bias threaten the validity of systematic reviews and meta-analysis and ultimately can affect clinical decision-making. A rigorous methodology to evaluate the impact of this bias on the meta-analysis results of a network of interventions is still lacking. We present a tool to assess the Risk Of Bias due to Missing Evidence in Network meta-analysis (ROB-MEN) by expanding the methods previously developed for pairwise meta-analysis (ROB-ME, http://www.riskofbias.info).ROB-MEN first evaluates the risk of bias due to missing evidence for each pairwise comparison separately. This step considers possible bias due to the presence of studies with unavailable results (known unknowns) and the potential for unpublished studies (unknown unknowns). The second step combines the overall judgements about the risk of bias due to missing evidence in pairwise comparisons with the percentage contribution of direct comparisons on the NMA estimates, the presence or absence of small-study effects, as evaluated by network meta-regression, and any bias from unobserved comparisons. Then, a level of “low risk”, “some concerns” or “high risk” for the bias due to missing evidence is assigned to each NMA estimate, which is our tool’s final output.We describe the methodology of ROB-MEN step-by-step using an illustrative example from a published NMA of non-diagnostic modalities for the detection of coronary artery disease in patients with low risk acute coronary syndrome. We also report a full application of the tool on a larger and more complex published network of 18 drugs from head-to-head studies for the acute treatment of adults with major depressive disorder. The ROB-MEN tool is the first tool for evaluating the risk of bias due to missing evidence in NMA and it is applicable to networks of all sizes and geometry.

Aims Glioma is a fatal disease that causes significant years of life lost to an individual. Mutations in the driver gene BRAF, such as the V600 alteration, may contribute to gliomagenesis in adults and children through abnormal signaling causing uncontrolled cell proliferation. The use of BRAF-inhibitor drugs including Vemurafenib and Dabrafenib have shown a favorable response in 48% and 50% of melanoma patients with BRAF V600 mutations respectively. BRAF inhibitors and MEK inhibitors have shown efficacy in certain paediatric gliomas in the recurrent setting. Despite the potential benefit of BRAF inhibitors, the prevalence of BRAF V600 within primary gliomas is not fully discovered. Some studies identify the prevalence to be over 50%, while others find the prevalence to be around 1%. We performed a comprehensive systematic review to determine the prevalence of BRAF V600 within the adult and paediatric glioma population in different diagnostic groups. Method A systematic literature search was performed using Ovid MEDLINE and Embase from genesis to the 22nd October 2020. Studies were not restricted by language. Studies were eligible if patients were histologically diagnosed according to WHO guidelines as a primary glioma evaluating the prevalence of BRAF V600 and included ≥ 10 primary glioma patients. The review protocol was registered in PROSPERO (CRD42019127704). Search results were managed using Endnote. Two independent reviewers assessed the eligibility of the publications using Rayyan, conflicts were evaluated by a third reviewer. Included articles were extracted by one reviewer and confirmed by a second reviewer. Risk of bias assessments were conducted using Hoy et al’s risk of bias tool. Results were synthesized using “metaprop” in R. The meta-analysis was carried out in R which produced forest plots. Results Our cohort included 182 studies with a total of 13669 adult and paediatric glioma patients classified diagnostically according to WHO guidelines. Among 48 glioma entities, BRAF V600 was identified most commonly in epithelioid glioblastoma with a prevalence of 69% (95% confidence interval (CI): 45-89%), followed by pleomorphic xanthoastrocytoma with a prevalence of 56% (95% CI: 48-64%), anaplastic pleomorphic xanthoastrocytoma with a prevalence of 38% (95% CI: 23-54%), ganglioglioma with a prevalence of 40% (95% CI: 33-46%), and anaplastic ganglioglioma with a prevalence of 46% (95% CI: 18-76%). Other glioma entities were found to have a prevalence of BRAF V600, these include astroblastoma (24%), desmoplastic infantile astrocytoma (16%), subependymal giant cell astrocytoma (8%), dysembryoplastic neuroepithelial tumour (3%), diffuse astrocytoma (3%), and pilocytic astrocytoma (3%). Conclusion To our knowledge, this is the largest systematic review examining the prevalence of BRAF V600 in adult and paediatric glioma classified according to diagnostic WHO criteria. However, there were some limitations in this review. The sample sizes of some studies were very small, and the method of mutational analysis for BRAF V600 varied between papers. We found BRAF V600 in a significant prevalence of epithelioid glioblastoma, pleomorphic xanthoastrocytoma, anaplastic pleomorphic xanthoastrocytoma, ganglioglioma, and anaplastic ganglioglioma. Of interest, BRAF V600 mutation was found in a lower prevalence of astroblastoma, desmoplastic infantile astrocytoma, subependymal giant cell astrocytoma, dysembryoplastic neuroepithelial tumour, diffuse astrocytoma, and pilocytic astrocytoma. Consideration of assessment of BRAF V600 mutation may enable further treatment options with BRAF and/or MEK inhibitors in these particular diagnostic entities.

Background: Glioblastoma is an aggressive form of brain cancer. Approximately five in 100 people with glioblastoma survive for five years past diagnosis. Glioblastomas that have a particular modification to their DNA (called methylation) in a particular region (the O6-methylguanine–DNA methyltransferase (MGMT) promoter) respond better to treatment with chemotherapy using a drug called temozolomide.Objectives: To assess which way of measuring methylation of the MGMT promoter best predicts survival when people with glioblastoma are treated with temozolomide.Search methods: We searched MEDLINE, Embase, BIOSIS, Web of Science Conference Proceedings Citation Index to December 2018, and examined reference lists. For economic evaluation studies we additionally searched NHS Economic Evaluation Database (EED) up to December 2014.Selection criteria: Eligible studies were longitudinal (cohort) studies of adults with diagnosed glioblastoma treated with temozolomide with/without radiation therapy/surgery. Studies had to have related MGMT status in tumour tissue (assessed by one or more method) with overall survival and presented results as hazard ratios or with su*icient information (e.g. Kaplan-Meier curves) for us to estimate hazard ratios. We focus mainly on studies comparing two or more methods, and list brief details of articles that examined a single method of measuring MGMT promoter methylation. We also sought economic evaluations conducted alongside trials, modelling studies, and cost analysis.Data collection and analysis: All steps of the identification and data extraction process for multiple-method studies were undertaken by two reviewers independently. We assessed risk of bias and applicability using our own modified and extended version of the QUIPS tool. Comparisons of different techniques, exact promoter regions (CpG sites) and thresholds for interpretation were compared within studies by examining hazard ratios. We performed meta-analyses for comparisons of the three most commonly examined methods (immunohistochemistry (IHC), methylation-specific polymerase chain reaction (MSP) and pyrosequencing (PSQ)), with ratios of hazard ratios, using an imputed value of the correlation between results based on the same individuals.Main results: We included 32 independent cohorts involving 3474 people in which two or more methods were compared. In meta-analyses of ratios of hazard ratios (RHR), we found evidence that MSP (CpG sites 76 to 80 and 84 to 87) is more prognostic than IHC for MGMT protein at varying thresholds: (RHR = 1.31; 95% confidence interval (CI) 1.01 to 1.71). We also found evidence that PSQ is more prognostic than IHC for MGMT protein at various thresholds (RHR = 1.36; 95% CI 1.01 to 1.84). The data suggest that PSQ (mainly at CpG sites 74 to 78, using various thresholds) is slightly more prognostic than MSP at sites 76 to 80 and 84 to 87 (RHR = 1.14; 95% CI 0.87 to 1.48). Many variants of PSQ have been compared, although we did not see any strong and consistent messages from the results. Targeting multiple CpG sites is likely to be more prognostic than targeting just one. In addition, we identified and summarized 190 articles describing a single method for measuring MGMT promoter methylation status.Authors' conclusions: Pyrosequencing and methylation-specific PCR appear to be more prognostic for overall survival than immunohistochemistry. Strong evidence is not available to draw conclusions with confidence about the best CpG sites or thresholds for quantitative methods. MSP has been studied mainly for CpG sites 76 to 80 and 84 to 87 and PSQ at CpG sites ranging from 72 to 95. A threshold of 9% for CpG sites 74 to 78 was found to perform better than higher thresholds of 28% or 29% in two of three good quality studies making such comparisons.

Background Investigations of transparency, reproducibility and replicability in science have been directed largely at individual studies. It is just as critical to explore these issues in syntheses of studies, such as systematic reviews, given their influence on decision-making and future research. We aim to explore various aspects relating to the transparency, reproducibility and replicability of several components of systematic reviews with meta-analysis of the effects of health, social, behavioural and educational interventions. Methods The REPRISE (REProducibility and Replicability In Syntheses of Evidence) project consists of four studies. We will evaluate the completeness of reporting and sharing of review data, analytic code and other materials in a random sample of 300 systematic reviews of interventions published in 2020 (Study 1). We will survey authors of systematic reviews to explore their views on sharing review data, analytic code and other materials and their understanding of and opinions about replication of systematic reviews (Study 2). We will then evaluate the extent of variation in results when we (a) independently reproduce meta-analyses using the same computational steps and analytic code (if available) as used in the original review (Study 3), and (b) crowdsource teams of systematic reviewers to independently replicate a subset of methods (searches for studies, selection of studies for inclusion, collection of outcome data, and synthesis of results) in a sample of the original reviews; 30 reviews will be replicated by 1 team each and 2 reviews will be replicated by 15 teams (Study 4). Discussion The REPRISE project takes a systematic approach to determine how reliable systematic reviews of interventions are. We anticipate that results of the REPRISE project will inform strategies to improve the conduct and reporting of future systematic reviews.

BACKGROUND AND AIM: Globally, nearly one in five people who inject drugs (PWID) are living with HIV, and the rate of new HIV infections in PWID is increasing in some settings. Early diagnosis is crucial for effective HIV control. We reviewed the evidence on the association between opioid agonist therapy (OAT) and HIV testing uptake among PWID. METHODS: We conducted a systematic review searching MEDLINE, Scopus, Web of Science, Cochrane Central Register of Controlled Trials and PsycINFO for studies published from January 2000 to March 2019. Reference lists and conference proceedings were hand-searched. Observational and intervention studies were eligible for inclusion. Risk of bias was assessed using the Risk of Bias in Non-Randomised Studies of Interventions (ROBINS-I) tool. Meta-analyses were conducted using random-effects models. RESULTS: Of 13 373 records identified, 11 studies from Australia, Europe, Malaysia and the United States were included. All studies had at least a serious risk of bias, largely due to confounding and selection bias, making it difficult to draw causal conclusions from the evidence. Ten studies provided data on the association between current OAT use and recent HIV testing. Six showed a positive association, while four provided little evidence of an association: pooled odds ratio (OR) = 1.71, 95% confidence interval (CI) = 1.28-2.27. Looking at having ever been on OAT and having ever been HIV tested, seven studies showed a positive association and three showed either weak or no evidence of an association: pooled OR = 3.82, 95% CI = 2.96-4.95. CONCLUSIONS: Opioid agonist therapy may increase uptake of HIV testing among people who inject drugs, providing further evidence that opioid agonist therapy improves the HIV treatment care cascade.

This research on data extraction methods applies recent advances in natural language processing to evidence synthesis based on medical texts. Texts of interest include abstracts of clinical trials in English and in multilingual contexts. The main focus is on information characterized via the Population, Intervention, Comparator, and Outcome (PICO) framework, but data extraction is not limited to these fields. Recent neural network architectures based on transformers show capacities for transfer learning and increased performance on downstream natural language processing tasks such as universal reading comprehension, brought forward by this architecture's use of contextualized word embeddings and self-attention mechanisms. This paper contributes to solving problems related to ambiguity in PICO sentence prediction tasks, as well as highlighting how annotations for training named entity recognition systems are used to train a high-performing, but nevertheless flexible architecture for question answering in systematic review automation. Additionally, it demonstrates how the problem of insufficient amounts of training annotations for PICO entity extraction is tackled by augmentation. All models in this paper were created with the aim to support systematic review (semi)automation. They achieve high F1 scores, and demonstrate the feasibility of applying transformer-based classification methods to support data mining in the biomedical literature.

Major depressive disorder is one of the most common, burdensome, and costly psychiatric disorders worldwide in adults. Pharmacological and non-pharmacological treatments are available; however, because of inadequate resources, antidepressants are used more frequently than psychological interventions. Prescription of these agents should be informed by the best available evidence. Therefore, we aimed to update and expand our previous work to compare and rank antidepressants for the acute treatment of adults with unipolar major depressive disorder.We did a systematic review and network meta-analysis. We searched Cochrane Central Register of Controlled Trials, CINAHL, Embase, LILACS database, MEDLINE, MEDLINE In-Process, PsycINFO, the websites of regulatory agencies, and international registers for published and unpublished, double-blind, randomised controlled trials from their inception to Jan 8, 2016. We included placebo-controlled and head-to-head trials of 21 antidepressants used for the acute treatment of adults (≥18 years old and of both sexes) with major depressive disorder diagnosed according to standard operationalised criteria. We excluded quasi-randomised trials and trials that were incomplete or included 20% or more of participants with bipolar disorder, psychotic depression, or treatment-resistant depression; or patients with a serious concomitant medical illness. We extracted data following a predefined hierarchy. In network meta-analysis, we used group-level data. We assessed the studies' risk of bias in accordance to the Cochrane Handbook for Systematic Reviews of Interventions, and certainty of evidence using the Grading of Recommendations Assessment, Development and Evaluation framework. Primary outcomes were efficacy (response rate) and acceptability (treatment discontinuations due to any cause). We estimated summary odds ratios (ORs) using pairwise and network meta-analysis with random effects. This study is registered with PROSPERO, number CRD42012002291.We identified 28 552 citations and of these included 522 trials comprising 116 477 participants. In terms of efficacy, all antidepressants were more effective than placebo, with ORs ranging between 2·13 (95% credible interval [CrI] 1·89–2·41) for amitriptyline and 1·37 (1·16–1·63) for reboxetine. For acceptability, only agomelatine (OR 0·84, 95% CrI 0·72–0·97) and fluoxetine (0·88, 0·80–0·96) were associated with fewer dropouts than placebo, whereas clomipramine was worse than placebo (1·30, 1·01–1·68). When all trials were considered, differences in ORs between antidepressants ranged from 1·15 to 1·55 for efficacy and from 0·64 to 0·83 for acceptability, with wide CrIs on most of the comparative analyses. In head-to-head studies, agomelatine, amitriptyline, escitalopram, mirtazapine, paroxetine, venlafaxine, and vortioxetine were more effective than other antidepressants (range of ORs 1·19–1·96), whereas fluoxetine, fluvoxamine, reboxetine, and trazodone were the least efficacious drugs (0·51–0·84). For acceptability, agomelatine, citalopram, escitalopram, fluoxetine, sertraline, and vortioxetine were more tolerable than other antidepressants (range of ORs 0·43–0·77), whereas amitriptyline, clomipramine, duloxetine, fluvoxamine, reboxetine, trazodone, and venlafaxine had the highest dropout rates (1·30–2·32). 46 (9%) of 522 trials were rated as high risk of bias, 380 (73%) trials as moderate, and 96 (18%) as low; and the certainty of evidence was moderate to very low.All antidepressants were more efficacious than placebo in adults with major depressive disorder. Smaller differences between active drugs were found when placebo-controlled trials were included in the analysis, whereas there was more variability in efficacy and acceptability in head-to-head trials. These results should serve evidence-based practice and inform patients, physicians, guideline developers, and policy makers on the relative merits of the different antidepressants.National Institute for Health Research Oxford Health Biomedical Research Centre and the Japan Society for the Promotion of Science., 抗うつ剤の効果を網羅的に比較 --効き目と副作用頻度、臨床試験データを統合--. 京都大学プレスリリース. 2018-02-22.

Objectives: Safe drinking water, sanitation and hygiene are protective against diarrhoeal disease; a leading cause of child mortality. The main objective was an updated assessment of the impact of unsafe water, sanitation and hygiene (WaSH) on childhood diarrhoeal disease. Methods: We undertook a systematic review of articles published between 1970 and February 2016. Study results were combined and analysed using meta-analysis and meta-regression. Results: A total of 135 studies met the inclusion criteria. Several water, sanitation and hygiene interventions were associated with lower risk of diarrhoeal morbidity. Point-of-use filter interventions with safe storage reduced diarrhoea risk by 61% (RR = 0.39; 95% CI: 0.32, 0.48); piped water to premises of higher quality and continuous availability by 75% and 36% (RR = 0.25 (0.09, 0.67) and 0.64 (0.42, 0.98)), respectively compared to a baseline of unimproved drinking water; sanitation interventions by 25% (RR = 0.75 (0.63, 0.88)) with evidence for greater reductions when high sanitation coverage is reached; and interventions promoting handwashing with soap by 30% (RR = 0.70 (0.64, 0.77)) vs. no intervention. Results of the analysis of sanitation and hygiene interventions are sensitive to certain differences in study methods and conditions. Correcting for non-blinding would reduce the associations with diarrhoea to some extent. Conclusions: Although evidence is limited, results suggest that household connections of water supply and higher levels of community coverage for sanitation appear particularly impactful which is in line with targets of the Sustainable Development Goals.

BackgroundHeart failure (HF) affects around 500,000 people in the UK. HF medications are frequently underprescribed and B-type natriuretic peptide (BNP)-guided therapy may help to optimise treatment.ObjectiveTo evaluate the clinical effectiveness and cost-effectiveness of BNP-guided therapy compared with symptom-guided therapy in HF patients.DesignSystematic review, cohort study and cost-effectiveness model.SettingA literature review and usual care in the NHS.Participants(a) HF patients in randomised controlled trials (RCTs) of BNP-guided therapy; and (b) patients having usual care for HF in the NHS.InterventionsSystematic review: BNP-guided therapy or symptom-guided therapy in primary or secondary care.Cohort study: BNP monitored (≥ 6 months’ follow-upandthree or more BNP testsandtwo or more tests per year), BNP tested (≥ 1 tests but not BNP monitored) or never tested.Cost-effectiveness model: BNP-guided therapy in specialist clinics.Main outcome measuresMortality, hospital admission (all cause and HF related) and adverse events; and quality-adjusted life-years (QALYs) for the cost-effectiveness model.Data sourcesSystematic review: Individual participant or aggregate data from eligible RCTs.Cohort study: The Clinical Practice Research Datalink, Hospital Episode Statistics and National Heart Failure Audit (NHFA).Review methodsA systematic literature search (five databases, trial registries, grey literature and reference lists of publications) for published and unpublished RCTs.ResultsFive RCTs contributed individual participant data (IPD) and eight RCTs contributed aggregate data (1536 participants were randomised to BNP-guided therapy and 1538 participants were randomised to symptom-guided therapy). For all-cause mortality, the hazard ratio (HR) for BNP-guided therapy was 0.87 [95% confidence interval (CI) 0.73 to 1.04]. Patients who were aged p = 0.03): n = 13,632), BNP tested (n = 3392) or BNP monitored (n = 71). Median survival was 5 years; all-cause mortality was 141.5 out of 1000 person-years (95% CI 138.5 to 144.6 person-years). All-cause mortality and hospital admission rate were highest in the BNP-monitored group, and median survival among 130,433 NHFA patients (1 January 2007–1 March 2013) was 2.2 years. The admission rate was 1.1 patients per year (interquartile range 0.5–3.5 patients). In the cost-effectiveness model, in patients aged LimitationsThe limitations of the trial were a lack of IPD for most RCTs and heterogeneous interventions; the inability to identify BNP monitoring confidently, to determine medication doses or to distinguish between HFrEF and HFpEF; the use of a simplified two-state Markov model; a focus on health service costs and a paucity of data on HFpEF patients aged ConclusionsThe efficacy of BNP-guided therapy in specialist HF clinics is uncertain. If efficacious, it would be cost-effective for patients aged Future workIdentify an optimal BNP-monitoring strategy and how to optimise HF management in accordance with guidelines; update the IPD meta-analysis to include the Guiding Evidence Based Therapy Using Biomarker Intensified Treatment (GUIDE-IT) RCT; collect routine long-term outcome data for completed and ongoing RCTs.Trial registrationCurrent Controlled Trials ISRCTN37248047 and PROSPERO CRD42013005335.FundingThis project was funded by the NIHR Health Technology Assessment programme and will be published in full inHealth Technology Assessment; Vol. 21, No. 40. See the NIHR Journals Library website for further project information. The British Heart Foundation paid for Chris A Rogers’ and Maria Pufulete’s time contributing to the study. Syed Mohiuddin’s time is supported by the NIHR Collaboration for Leadership in Applied Health Research and Care West at University Hospitals Bristol NHS Foundation Trust. Rachel Maishman contributed to the study when she was in receipt of a NIHR Methodology Research Fellowship.

BackgroundAtrial fibrillation (AF) is a common cardiac arrhythmia that increases the risk of thromboembolic events. Anticoagulation therapy to prevent AF-related stroke has been shown to be cost-effective. A national screening programme for AF may prevent AF-related events, but would involve a substantial investment of NHS resources.ObjectivesTo conduct a systematic review of the diagnostic test accuracy (DTA) of screening tests for AF, update a systematic review of comparative studies evaluating screening strategies for AF, develop an economic model to compare the cost-effectiveness of different screening strategies and review observational studies of AF screening to provide inputs to the model.DesignSystematic review, meta-analysis and cost-effectiveness analysis.SettingPrimary care.ParticipantsAdults.InterventionScreening strategies, defined by screening test, age at initial and final screens, screening interval and format of screening {systematic opportunistic screening [individuals offered screening if they consult with their general practitioner (GP)] or systematic population screening (when all eligible individuals are invited to screening)}.Main outcome measuresSensitivity, specificity and diagnostic odds ratios; the odds ratio of detecting new AF cases compared with no screening; and the mean incremental net benefit compared with no screening.Review methodsTwo reviewers screened the search results, extracted data and assessed the risk of bias. A DTA meta-analysis was perfomed, and a decision tree and Markov model was used to evaluate the cost-effectiveness of the screening strategies.ResultsDiagnostic test accuracy depended on the screening test and how it was interpreted. In general, the screening tests identified in our review had high sensitivity (> 0.9). Systematic population and systematic opportunistic screening strategies were found to be similarly effective, with an estimated 170 individuals needed to be screened to detect one additional AF case compared with no screening. Systematic opportunistic screening was more likely to be cost-effective than systematic population screening, as long as the uptake of opportunistic screening observed in randomised controlled trials translates to practice. Modified blood pressure monitors, photoplethysmography or nurse pulse palpation were more likely to be cost-effective than other screening tests. A screening strategy with an initial screening age of 65 years and repeated screens every 5 years until age 80 years was likely to be cost-effective, provided that compliance with treatment does not decline with increasing age.ConclusionsA national screening programme for AF is likely to represent a cost-effective use of resources. Systematic opportunistic screening is more likely to be cost-effective than systematic population screening. Nurse pulse palpation or modified blood pressure monitors would be appropriate screening tests, with confirmation by diagnostic 12-lead electrocardiography interpreted by a trained GP, with referral to a specialist in the case of an unclear diagnosis. Implementation strategies to operationalise uptake of systematic opportunistic screening in primary care should accompany any screening recommendations.LimitationsMany inputs for the economic model relied on a single trial [the Screening for Atrial Fibrillation in the Elderly (SAFE) study] and DTA results were based on a few studies at high risk of bias/of low applicability.Future workComparative studies measuring long-term outcomes of screening strategies and DTA studies for new, emerging technologies and to replicate the results for photoplethysmography and GP interpretation of 12-lead electrocardiography in a screening population.Study registrationThis study is registered as PROSPERO CRD42014013739.FundingThe National Institute for Health Research Health Technology Assessment programme.

When we speak about heterogeneity in a meta-analysis, our intent is usually to understand the substantive implications of the heterogeneity. If an intervention yields a mean effect size of 50 points, we want to know if the effect size in different populations varies from 40 to 60, or from 10 to 90, because this speaks to the potential utility of the intervention. While there is a common belief that the I2 statistic provides this information, it actually does not. In this example, if we are told that I2 is 50%, we have no way of knowing if the effects range from 40 to 60, or from 10 to 90, or across some other range. Rather, if we want to communicate the predicted range of effects, then we should simply report this range. This gives readers the information they think is being captured by I2 and does so in a way that is concise and unambiguous. Copyright © 2017 John Wiley & Sons, Ltd.

ObjectiveTo examine the design characteristics, risk of bias, and reporting adequacy of pivotal randomised controlled trials of cancer drugs approved by the European Medicines Agency (EMA).DesignCross sectional analysis.SettingEuropean regulatory documents, clinical trial registry records, protocols, journal publications, and supplementary appendices.Eligibility criteriaPivotal randomised controlled trials of new cancer drugs approved by the EMA between 2014 and 2016.Main outcome measuresStudy design characteristics (randomisation, comparators, and endpoints); risk of bias using the revised Cochrane tool (bias arising from the randomisation process, deviations from intended interventions, missing outcome data, measurement of the outcome, and selection of the reported result); and reporting adequacy (completeness and consistency of information in trial protocols, publications, supplementary appendices, clinical trial registry records, and regulatory documents).ResultsBetween 2014 and 2016, the EMA approved 32 new cancer drugs on the basis of 54 pivotal studies. Of these, 41 (76%) were randomised controlled trials and 13 (24%) were either non-randomised studies or single arm studies. 39/41 randomised controlled trials had available publications and were included in our study. Only 10 randomised controlled trials (26%) measured overall survival as either a primary or coprimary endpoint, with the remaining trials evaluating surrogate measures such as progression free survival and response rates. Overall, 19 randomised controlled trials (49%) were judged to be at high risk of bias for their primary outcome. Concerns about missing outcome data (n=10) and measurement of the outcome (n=7) were the most common domains leading to high risk of bias judgments. Fewer randomised controlled trials that evaluated overall survival as the primary endpoint were at high risk of bias than those that evaluated surrogate efficacy endpoints (2/10 (20%)v16/29 (55%), respectively). When information available in regulatory documents and the scientific literature was considered separately, overall risk of bias judgments differed for eight randomised controlled trials (21%), which reflects reporting inadequacies in both sources of information. Regulators identified additional deficits beyond the domains captured in risk of bias assessments for 10 drugs (31%). These deficits included magnitude of clinical benefit, inappropriate comparators, and non-preferred study endpoints, which were not disclosed as limitations in scientific publications.ConclusionsMost pivotal studies forming the basis of EMA approval of new cancer drugs between 2014 and 2016 were randomised controlled trials. However, almost half of these were judged to be at high risk of bias based on their design, conduct, or analysis, some of which might be unavoidable because of the complexity of cancer trials. Regulatory documents and the scientific literature had gaps in their reporting. Journal publications did not acknowledge the key limitations of the available evidence identified in regulatory documents.

This is a protocol for a Cochrane Review (Prognosis). The objectives are as follows: Primary objective The primary objective of this review is to determine which technique (test) for assessing MGMT methylation status best predicts overall survival in people diagnosed with glioblastoma who are treated with temozolomide. We will consider each MGMT test as a separate prognostic factor. See Table 2 for the review question in population, index prognostic factor, comparator prognostic factor(s), outcome, timing, and setting (PICOTS) format. Secondary objective We will undertake a full integrated economic review to identify economic evaluations in relation to the different methods of assessing MGMT methylation status effect on overall survival. Furthermore, we will develop a simple cost-effectiveness decision model exploring the cost-effectiveness of alternative approaches to assessing MGMT methylation status. Investigation of sources of heterogeneity If we identify a sufficient number of studies for inclusion, we will examine for each technique/test whether any of the following features is best associated with overall survival. • Promoter region/ 5’-C-phosphate-G-3’ (CpG)s analysed (or the antibody used in the case of immunohistochemistry) • Cut-off used (where relevant) • Type of tumour sample (FFPE or frozen) We will also investigate the effect of population characteristics including the following. • Age • Extent of tumour resection • Karnofsky performance status • IDH status • Recurrent versus first diagnosis We are assuming constant HRs. To confirm the validity of this assumption we will investigate length of follow-up as a source of heterogeneity. If studies have started follow-up for overall survival from different timepoints, we will also investigate this as a source of heterogeneity.

Evaluation of the credibility of results from a meta-analysis has become an intrinsic part of the evidence synthesis process. We present a methodological framework to evaluate Confidence In the results from Network Meta-Analysis (CINeMA) when multiple interventions are compared. CINeMA considers six domains and we outline the methods used to form judgements about within-study bias, across-studies bias, indirectness, imprecision, heterogeneity and incoherence. Key to judgements about within-study bias and indirectness is the percentage contribution matrix, which shows how much information each study contributes to the results from network meta-analysis. The use of contribution matrix allows the semi-automation of the process, implemented in a freely available web application (cinema.ispm.ch). In evaluating imprecision, heterogeneity and inconsistency we consider the impact of these components of variability in forming clinical decisions. Via three examples, we show that CINeMA improves transparency and avoids the selective use of evidence when forming judgements, thus limiting subjectivity in the process. CINeMA is easy to apply even in large and complicated networks, like a network involving 18 different antidepressant drugs.

Randomized clinical trials underpin evidence-based clinical practice, but flaws in their conduct may lead to biased estimates of intervention effects and hence invalid treatment recommendations. The main approach to the empirical study of bias is to collate a number of meta-analyses and, within each, compare the results of trials with and without a methodological characteristic such as blinding of participants and health professionals. Estimated within-meta-analysis differences are combined across meta-analyses, leading to an estimate of mean bias. Such “meta-epidemiological” studies are published in increasing numbers and have the potential to inform trial design, assessment of risk of bias, and reporting guidelines. However, their interpretation is complicated by issues of confounding, imprecision, and applicability. We developed a guide for interpreting meta-epidemiological studies, illustrated using MetaBLIND, a large study on the impact of blinding. Applying generally accepted principles of research methodology to meta-epidemiology, we framed 10 questions covering the main issues to consider when interpreting results of such studies, including risk of systematic error, risk of random error, issues related to heterogeneity, and theoretical plausibility. We suggest that readers of a meta-epidemiological study reflect comprehensively on the research question posed in the study, whether an experimental intervention was unequivocally identified for all included trials, the risk of misclassification of the trial characteristic, and the risk of confounding, i.e the adequacy of any adjustment for the likely confounders. We hope that our guide to interpretation of results of meta-epidemiological studies is helpful for readers of such studies.

Missing outcome data can invalidate the results of randomized trials and their meta-analysis. However, addressing missing data is often a challenging issue because it requires untestable assumptions. The impact of missing outcome data on the meta-analysis summary effect can be explored by assuming a relationship between the outcome in the observed and the missing participants via an informative missingness parameter. The informative missingness parameters cannot be estimated from the observed data, but they can be specified, with associated uncertainty, using evidence external to the meta-analysis, such as expert opinion. The use of informative missingness parameters in pairwise meta-analysis of aggregate data with binary outcomes has been previously implemented in Stata by the metamiss command. In this article, we present the new command metamiss2, which is an extension of metamiss for binary or continuous data in pairwise or network meta-analysis. The command can be used to explore the robustness of results to different assumptions about the missing data via sensitivity analysis.

Background: The reliable and usable (semi)automation of data extraction can support the field of systematic review by reducing the workload required to gather information about the conduct and results of the included studies. This living systematic review examines published approaches for data extraction from reports of clinical studies. Methods: We systematically and continually search MEDLINE, Institute of Electrical and Electronics Engineers (IEEE), arXiv, and the dblp computer science bibliography databases. Full text screening and data extraction are conducted within an open-source living systematic review application created for the purpose of this review. This iteration of the living review includes publications up to a cut-off date of 22 April 2020. Results: In total, 53 publications are included in this version of our review. Of these, 41 (77%) of the publications addressed extraction of data from abstracts, while 14 (26%) used full texts. A total of 48 (90%) publications developed and evaluated classifiers that used randomised controlled trials as the main target texts. Over 30 entities were extracted, with PICOs (population, intervention, comparator, outcome) being the most frequently extracted. A description of their datasets was provided by 49 publications (94%), but only seven (13%) made the data publicly available. Code was made available by 10 (19%) publications, and five (9%) implemented publicly available tools. Conclusions: This living systematic review presents an overview of (semi)automated data-extraction literature of interest to different types of systematic review. We identified a broad evidence base of publications describing data extraction for interventional reviews and a small number of publications extracting epidemiological or diagnostic accuracy data. The lack of publicly available gold-standard data for evaluation, and lack of application thereof, makes it difficult to draw conclusions on which is the best-performing system for each data extraction target. With this living review we aim to review the literature continually.

In meta-analysis, the random-effects model is often used to account for heterogeneity. The model assumes that heterogeneity has an additive effect on the variance of effect sizes. An alternative model, which assumes multiplicative heterogeneity, has been little used in the medical statistics community, but is widely used by particle physicists. In this paper, we compare the two models using a random sample of 448 meta-analyses drawn from the Cochrane Database of Systematic Reviews. In general, differences in goodness of fit are modest. The multiplicative model tends to give results that are closer to the null, with a narrower confidence interval. Both approaches make different assumptions about the outcome of the meta-analysis. In our opinion, the selection of the more appropriate model will often be guided by whether the multiplicative model's assumption of a single effect size is plausible. Copyright © 2016 John Wiley & Sons, Ltd.

Random-effects meta-analysis methods include an estimate of between-study heterogeneity variance. We present a systematic review of simulation studies comparing the performance of different estimation methods for this parameter. We summarise the performance of methods in relation to estimation of heterogeneity and of the overall effect estimate, and of confidence intervals for the latter. Among the twelve included simulation studies, the DerSimonian and Laird method was most commonly evaluated. This estimate is negatively biased when heterogeneity is moderate to high and therefore most studies recommended alternatives. The Paule-Mandel method was recommended by three studies: it is simple to implement, is less biased than DerSimonian and Laird and performs well in meta-analyses with dichotomous and continuous outcomes. In many of the included simulation studies, results were based on data that do not represent meta-analyses observed in practice, and only small selections of methods were compared. Furthermore, potential conflicts of interest were present when authors of novel methods interpreted their results. On the basis of current evidence, we provisionally recommend the Paule-Mandel method for estimating the heterogeneity variance, and using this estimate to calculate the mean effect and its 95% confidence interval. However, further simulation studies are required to draw firm conclusions. Copyright © 2016 John Wiley & Sons, Ltd.

Background: The COVID-19 pandemic has caused morbidity and mortality, as well as, widespread disruption to people’s lives and livelihoods around the world. Given the health and economic threats posed by the pandemic to the global community, there are concerns that rates of suicide and suicidal behaviour may rise during and in its aftermath. Our living systematic review (LSR) focuses on suicide prevention in relation to COVID-19, with this iteration synthesising relevant evidence up to June 7th 2020. Method: Automated daily searches feed into a web-based database with screening and data extraction functionalities. Eligibility criteria include incidence/prevalence of suicidal behaviour, exposure-outcome relationships and effects of interventions in relation to the COVID-19 pandemic. Outcomes of interest are suicide, self-harm or attempted suicide and suicidal thoughts. No restrictions are placed on language or study type, except for single-person case reports. Results: Searches identified 2070 articles, 29 (28 studies) met our inclusion criteria, of which 14 articles were research letters or pre-prints awaiting peer review. All articles reported observational data: 12 cross-sectional; eight case series; five modelling; and three service utilisation studies. No studies reported on changes in rates of suicidal behaviour. Case series were largely drawn from news reporting in low/middle income countries and factors associated with suicide included fear of infection, social isolation and economic concerns. Conclusions: A marked improvement in the quality of design, methods, and reporting in future studies is needed. There is thus far no clear evidence of an increase in suicide, self-harm, suicidal behaviour, or suicidal thoughts associated with the pandemic. However, suicide data are challenging to collect in real time and economic effects are evolving. Our LSR will provide a regular synthesis of the most up-to-date research evidence to guide public health and clinical policy to mitigate the impact of COVID-19 on suicide. PROSPERO registration: CRD42020183326 01/05/2020

Background: Researchers in evidence-based medicine cannot keep up with the amounts of both old and newly published primary research articles. Support for the early stages of the systematic review process – searching and screening studies for eligibility – is necessary because it is currently impossible to search for relevant research with precision. Better automated data extraction may not only facilitate the stage of review traditionally labelled ‘data extraction’, but also change earlier phases of the review process by making it possible to identify relevant research. Exponential improvements in computational processing speed and data storage are fostering the development of data mining models and algorithms. This, in combination with quicker pathways to publication, led to a large landscape of tools and methods for data mining and extraction. Objective: To review published methods and tools for data extraction to (semi)automate the systematic reviewing process. Methods: We propose to conduct a living review. With this methodology we aim to do constant evidence surveillance, bi-monthly search updates, as well as review updates every 6 months if new evidence permits it. In a cross-sectional analysis we will extract methodological characteristics and assess the quality of reporting in our included papers. Conclusions: We aim to increase transparency in the reporting and assessment of automation technologies to the benefit of data scientists, systematic reviewers and funders of health research. This living review will help to reduce duplicate efforts by data scientists who develop data mining methods. It will also serve to inform systematic reviewers about possibilities to support their data extraction.

To develop ROBIS, a new tool for assessing the risk of bias in systematic reviews (rather than in primary studies).We used four-stage approach to develop ROBIS: define the scope, review the evidence base, hold a face-to-face meeting, and refine the tool through piloting.ROBIS is currently aimed at four broad categories of reviews mainly within health care settings: interventions, diagnosis, prognosis, and etiology. The target audience of ROBIS is primarily guideline developers, authors of overviews of systematic reviews ("reviews of reviews"), and review authors who might want to assess or avoid risk of bias in their reviews. The tool is completed in three phases: 1) assess relevance (optional), 2) identify concerns with the review process, and 3) judge risk of bias. Phase 2 covers four domains through which bias may be introduced into a systematic review: 1) study eligibility criteria; 2) identification and selection of studies; 3) data collection and study appraisal; and 4) synthesis and findings. Phase 3 assesses the overall risk of bias in the interpretation of review findings and whether this considered limitations identified in any of the phase 2 domains. Signaling questions are included to help judge concerns with the review process (phase 2) and the overall risk of bias in the review (phase 3); these questions flag aspects of review design related to the potential for bias and aim to help assessors judge risk of bias in the review process, results, and conclusions.ROBIS is the first rigorously developed tool designed specifically to assess the risk of bias in systematic reviews.

Background We estimated the effectiveness of serial B-type natriuretic peptide (BNP) blood testing to guide up-titration of medication compared with symptom-guided up-titration of medication in patients with heart failure (HF). Methods Systematic review and meta-analysis of randomised controlled trials (RCTs). We searched: MEDLINE (Ovid) 1950 to 9/06/2016; Embase (Ovid), 1980 to 2016 week 23; the Cochrane Library; ISI Web of Science (Citations Index and Conference Proceedings). The primary outcome was all-cause mortality; secondary outcomes were death related to HF, cardiovascular death, all-cause hospital admission, hospital admission for HF, adverse events, and quality of life. IPD were sought from all RCTs identified. Random-effects meta-analyses (two-stage) were used to estimate hazard ratios (HR) and confidence intervals (CIs) across RCTs, including HR estimates from published reports of studies that did not provide IPD. We estimated treatment-by-covariate interactions for age, gender, New York Heart Association (NYHA) class, HF type; diabetes status and baseline BNP subgroups. Dichotomous outcomes were analysed using random-effects odds ratio (OR) with 95% CI. Results We identified 14 eligible RCTs, five providing IPD. BNP-guided therapy reduced the hazard of hospital admission for HF by 19% (13 RCTs, HR 0.81, 95% CI 0.68 to 0.98) but not all-cause mortality (13 RCTs; HR 0.87, 95% CI 0.75 to 1.01) or cardiovascular mortality (5 RCTs; OR 0.88, 95% CI 0.67 to 1.16). For all-cause mortality, there was a significant interaction between treatment strategy and age (p = 0.034, 11 RCTs; HR 0.70, 95% CI 0.53–0.92, patients

Background There is debate in the literature as to whether inclusion of a placebo arm may alter characteristics of antidepressant trials. However, previous research has focused on response rates of various antidepressants on average only, ignoring potential differences among drugs or other aspects of trial findings. Little is known about the impact of a placebo arm on all-cause dropout and dropout due to adverse events. Methods We carried out a systematic review of published and unpublished double-blind randomized controlled trials (RCTs) for the acute treatment of unipolar major depression (update: January 2016). The probability of being allocated to placebo (π) was the exposure of interest, and we examined its influence on responders (efficacy), all-cause dropouts (acceptability) and dropouts due to adverse events (tolerability), while accounting for differences in drugs, trials and patient characteristics in multivariate random effects meta-regression. Results We included 421 studies (68 305 participants) comparing 16 antidepressants or placebo; π ranged from 20% to 50%. Response rate was lower [risk ratio (RR) 0.87; 95% confidence interval (CI) 0.83, 0.92] and all-cause dropout rate higher (RR 1.19; 95% CI 1.08, 1.31) for the same antidepressants in placebo-controlled trials compared with head-to-head trials. The probability of responding decreased by 3% (95% CI 2–5%) for every 10% increase in π, whereas the risk of all-cause dropout increased by 4% (95% CI 1–7%). Tolerability was unaffected by π. Response rate was inversely correlated with dropouts due to any cause (correlation coefficient −0.48; 95% CI −0.58, −0.36) and due to adverse events (−0.34; 95% CI −0.44, −0.23). Conclusions For the same antidepressant, response rate was on average smaller and dropouts higher when placebo was included; however, no association was found with dropouts due to adverse events. Decreased patient expectations, larger dropout rates and use of inappropriate statistical methods to impute missing data may explain this phenomenon. The findings call for caution in the integration of randomized evidence involving placebo arms.

Systematic reviews often encounter primary studies that report multiple effect sizes based on data from the same participants. These have the potential to introduce statistical dependency into the meta-analytic data set. In this paper, we provide a tutorial on dealing with effect size multiplicity within studies in the context of meta-analyses of intervention and association studies, recommending a three-step approach. The first step is to define the research question and consider the extent to which it mainly reflects interest in mean effect sizes (which we term a convergent approach) or an interest in exploring heterogeneity (which we term a divergent approach). A second step is to identify the types of multiplicities that appear in the initial database of effect sizes relevant to the research question, and we propose a categorization scheme to differentiate them. The third step is to select a strategy for dealing with each type of multiplicity. The researcher can choose between a reductionist meta-analytic approach, which is characterized by inclusion of a single effect size per study, and an integrative approach, characterized by inclusion of multiple effect sizes per study. We present an overview of available analysis strategies for dealing with effect size multiplicity within studies and provide recommendations intended to help researchers decide which strategy might be preferable in particular situations. Last, we offer caveats and cautions about addressing the challenges multiplicity poses for systematic reviews and meta-analyses.