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This is a protocol for a Cochrane Review (Intervention). The objectives are as follows: This review aims to look at the benefits and harms of pre‐emptive kidney transplantation compared with kidney transplantation after dialysis for patients with end-stage kidney disease.

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.

Randomized clinical trials (RCTs) are widely accepted as the gold standard in the assessment of health interventions. However, evidence from RCTs is often unavailable. Randomized clinical trials may also be impractical, for example when the outcome of interest is so rare or so long term that appropriately sized studies would be too expensive. In addition, RCTs require clinical equipoise, and withholding established treatments may be considered unethical even if evidence for the effects of these treatments is limited. Finally, RCTs may be underway but not ready for reporting for some years.

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.

Eligible users can access the full text via NHS OpenAthens at [https://www.clinicalkey.com/#!/content/journal/1-s2.0-S2215036618301330] (login required).

BackgroundSeveral scales, checklists and domain-based tools for assessing risk of reporting biases exist, but it is unclear how much they vary in content and guidance. We conducted a systematic review of the content and measurement properties of such tools.MethodsWe searched for potentially relevant articles in Ovid MEDLINE, Ovid Embase, Ovid PsycINFO and Google Scholar from inception to February 2017. One author screened all titles, abstracts and full text articles, and collected data on tool characteristics.ResultsWe identified 18 tools that include an assessment of the risk of reporting bias. Tools varied in regard to the type of reporting bias assessed (eg, bias due to selective publication, bias due to selective non-reporting), and the level of assessment (eg, for the study as a whole, a particular result within a study or a particular synthesis of studies). Various criteria are used across tools to designate a synthesis as being at ‘high’ risk of bias due to selective publication (eg, evidence of funnel plot asymmetry, use of non-comprehensive searches). However, the relative weight assigned to each criterion in the overall judgement is unclear for most of these tools. Tools for assessing risk of bias due to selective non-reporting guide users to assess a study, or an outcome within a study, as ‘high’ risk of bias if no results are reported for an outcome. However, assessing the corresponding risk of bias in a synthesis that is missing the non-reported outcomes is outside the scope of most of these tools. Inter-rater agreement estimates were available for five tools.ConclusionThere are several limitations of existing tools for assessing risk of reporting biases, in terms of their scope, guidance for reaching risk of bias judgements and measurement properties. Development and evaluation of a new, comprehensive tool could help overcome present limitations.

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.

This paper investigates how inconsistency (as measured by the I2 statistic) among studies in a meta‐analysis may differ, according to the type of outcome data and effect measure. We used hierarchical models to analyse data from 3873 binary, 5132 continuous and 880 mixed outcome meta‐analyses within the Cochrane Database of Systematic Reviews. Predictive distributions for inconsistency expected in future meta‐analyses were obtained, which can inform priors for between‐study variance. Inconsistency estimates were highest on average for binary outcome meta‐analyses of risk differences and continuous outcome meta‐analyses. For a planned binary outcome meta‐analysis in a general research setting, the predictive distribution for inconsistency among log odds ratios had median 22% and 95% CI: 12% to 39%. For a continuous outcome meta‐analysis, the predictive distribution for inconsistency among standardized mean differences had median 40% and 95% CI: 15% to 73%. Levels of inconsistency were similar for binary data measured by log odds ratios and log relative risks. Fitted distributions for inconsistency expected in continuous outcome meta‐analyses using mean differences were almost identical to those using standardized mean differences. The empirical evidence on inconsistency gives guidance on which outcome measures are most likely to be consistent in particular circumstances and facilitates Bayesian meta‐analysis with an informative prior for heterogeneity. © 2015 The Authors. Research Synthesis Methods published by John Wiley & Sons, Ltd. © 2015 The Authors. Research Synthesis Methods published by John Wiley & Sons, Ltd.

Meta-epidemiological studies are used to compare treatment effect estimates between randomized clinical trials with and without a characteristic of interest. To our knowledge, there is presently nothing to help researchers to a priori specify the required number of meta-analyses to be included in a meta-epidemiological study. We derived a theoretical power function and sample size formula in the framework of a hierarchical model that allows for variation in the impact of the characteristic between trials within a meta-analysis and between meta-analyses. A simulation study revealed that the theoretical function overestimated power (because of the assumption of equal weights for each trial within and between meta-analyses). We also propose a simulation approach that allows for relaxing the constraints used in the theoretical approach and is more accurate. We illustrate that the two variables that mostly influence power are the number of trials per meta-analysis and the proportion of trials with the characteristic of interest. We derived a closed-form power function and sample size formula for estimating the impact of trial characteristics in meta-epidemiological studies. Our analytical results can be used as a 'rule of thumb' for sample size calculation for a meta-epidemiologic study. A more accurate sample size can be derived with a simulation study.

Heterogeneity in meta-analysis is most commonly estimated using a moment-based approach described by DerSimonian and Laird. However, this method has been shown to produce biased estimates. Alternative methods to estimate heterogeneity include the restricted maximum likelihood approach and those proposed by Paule and Mandel, Sidik and Jonkman, and Hartung and Makambi. We compared the impact of these five methods on the results of 12,894 meta-analyses extracted from the Cochrane Database of Systematic Reviews. We compared the methods in terms of the following: (1) the extent of heterogeneity, expressed as an I(2) statistic; (2) the overall effect estimate; (3) the precision of the overall effect estimate; and (4) p-values testing the no effect hypothesis. Results suggest that, in some meta-analyses, I(2) estimates differ by more than 50% when different heterogeneity estimators are used. Conclusions naively based on statistical significance (at a 5% level) were discordant for at least one pair of estimators in 7.5% of meta-analyses, indicating that the choice of heterogeneity estimator could affect the conclusions of a meta-analysis. These findings imply that using a single estimate of heterogeneity may lead to non-robust results in some meta-analyses, and researchers should consider using alternatives to the DerSimonian and Laird method.

Background The scientific literature contains a wealth of information from different fields on potential disease mechanisms. However, identifying and prioritizing mechanisms for further analytical evaluation presents enormous challenges in terms of the quantity and diversity of published research. The application of data mining approaches to the literature offers the potential to identify and prioritize mechanisms for more focused and detailed analysis. Methods Here we present MELODI, a literature mining platform that can identify mechanistic pathways between any two biomedical concepts. Results Two case studies demonstrate the potential uses of MELODI and how it can generate hypotheses for further investigation. First, an analysis of ETS-related gene ERG and prostate cancer derives the intermediate transcription factor SP1, recently confirmed to be physically interacting with ERG. Second, examining the relationship between a new potential risk factor for pancreatic cancer identifies possible mechanistic insights which can be studied in vitro. Conclusions We have demonstrated the possible applications of MELODI, including two case studies. MELODI has been implemented as a Python/Django web application, and is freely available to use at [www.melodi.biocompute.org.uk].

Summary Meta-analysis is a common tool for synthesizing results of multiple studies. Among methods for performing meta-analysis, the approach known as ‘fixed effects’ or ‘inverse variance weighting’ is popular and widely used. A common interpretation of this method is that it assumes that the underlying effects in contributing studies are identical, and for this reason it is sometimes dismissed by practitioners. However, other interpretations of fixed effects analyses do not make this assumption, yet appear to be little known in the literature. We review these alternative interpretations, describing both their strengths and their limitations. We also describe how heterogeneity of the underlying effects can be addressed, with the same minimal assumptions, through either testing or meta-regression. Recommendations for the practice of meta-analysis are given; it is hoped that these will foster more direct connection of the questions that meta-analysts wish to answer with the statistical methods they choose.

Objectives Estimation of between-study heterogeneity is problematic in small meta-analyses. Bayesian meta-analysis is beneficial because it allows incorporation of external evidence on heterogeneity. To facilitate this, we provide empirical evidence on the likely heterogeneity between studies in meta-analyses relating to specific research settings. Study Design and Setting Our analyses included 6,492 continuous-outcome meta-analyses within the Cochrane Database of Systematic Reviews. We investigated the influence of meta-analysis settings on heterogeneity by modeling study data from all meta-analyses on the standardized mean difference scale. Meta-analysis setting was described according to outcome type, intervention comparison type, and medical area. Predictive distributions for between-study variance expected in future meta-analyses were obtained, which can be used directly as informative priors. Results Among outcome types, heterogeneity was found to be lowest in meta-analyses of obstetric outcomes. Among intervention comparison types, heterogeneity was lowest in meta-analyses comparing two pharmacologic interventions. Predictive distributions are reported for different settings. In two example meta-analyses, incorporating external evidence led to a more precise heterogeneity estimate. Conclusion Heterogeneity was influenced by meta-analysis characteristics. Informative priors for between-study variance were derived for each specific setting. Our analyses thus assist the incorporation of realistic prior information into meta-analyses including few studies.

Objective To compare the efficacy, safety, and cost effectiveness of direct acting oral anticoagulants (DOACs) for patients with atrial fibrillation. Design Systematic review, network meta-analysis, and cost effectiveness analysis. Data sources Medline, PreMedline, Embase, and The Cochrane Library. Eligibility criteria for selecting studies Published randomised trials evaluating the use of a DOAC, vitamin K antagonist, or antiplatelet drug for prevention of stroke in patients with atrial fibrillation. Results 23 randomised trials involving 94 656 patients were analysed: 13 compared a DOAC with warfarin dosed to achieve a target INR of 2.0-3.0. Apixaban 5 mg twice daily (odds ratio 0.79, 95% confidence interval 0.66 to 0.94), dabigatran 150 mg twice daily (0.65, 0.52 to 0.81), edoxaban 60 mg once daily (0.86, 0.74 to 1.01), and rivaroxaban 20 mg once daily (0.88, 0.74 to 1.03) reduced the risk of stroke or systemic embolism compared with warfarin. The risk of stroke or systemic embolism was higher with edoxaban 60 mg once daily (1.33, 1.02 to 1.75) and rivaroxaban 20 mg once daily (1.35, 1.03 to 1.78) than with dabigatran 150 mg twice daily. The risk of all-cause mortality was lower with all DOACs than with warfarin. Apixaban 5 mg twice daily (0.71, 0.61 to 0.81), dabigatran 110 mg twice daily (0.80, 0.69 to 0.93), edoxaban 30 mg once daily (0.46, 0.40 to 0.54), and edoxaban 60 mg once daily (0.78, 0.69 to 0.90) reduced the risk of major bleeding compared with warfarin. The risk of major bleeding was higher with dabigatran 150 mg twice daily than apixaban 5 mg twice daily (1.33, 1.09 to 1.62), rivaroxaban 20 mg twice daily than apixaban 5 mg twice daily (1.45, 1.19 to 1.78), and rivaroxaban 20 mg twice daily than edoxaban 60 mg once daily (1.31, 1.07 to 1.59). The risk of intracranial bleeding was substantially lower for most DOACs compared with warfarin, whereas the risk of gastrointestinal bleeding was higher with some DOACs than warfarin. Apixaban 5 mg twice daily was ranked the highest for most outcomes, and was cost effective compared with warfarin. Conclusions The network meta-analysis informs the choice of DOACs for prevention of stroke in patients with atrial fibrillation. Several DOACs are of net benefit compared with warfarin. A trial directly comparing DOACs would overcome the need for indirect comparisons to be made through network meta-analysis. Systematic review registration PROSPERO CRD 42013005324.

Meta-analyses are an important tool within systematic reviews to estimate the overall effect size and its confidence interval for an outcome of interest. If heterogeneity between the results of the relevant studies is anticipated, then a random-effects model is often preferred for analysis. In this model, a prediction interval for the true effect in a new study also provides additional useful information. However, the DerSimonian and Laird method - frequently used as the default method for meta-analyses with random effects - has been long challenged due to its unfavourable statistical properties. Several alternative methods have been proposed that may have better statistical properties in specific scenarios. In this paper, we aim to provide a comprehensive overview of available methods for calculating point estimates, confidence intervals and prediction intervals for the overall effect size under the random-effects model. We indicate whether some methods are preferable than others by considering the results of comparative simulation and real-life data studies.

Background: Human, animal, and cell experimental studies; human biomarker studies; and genetic studies complement epidemiologic findings and can offer insights into biological plausibility and pathways between exposure and disease, but methods for synthesizing such studies are lacking. We, therefore, developed a methodology for identifying mechanisms and carrying out systematic reviews of mechanistic studies that underpin exposure–cancer associations. Methods: A multidisciplinary team with expertise in informatics, statistics, epidemiology, systematic reviews, cancer biology, and nutrition was assembled. Five 1-day workshops were held to brainstorm ideas; in the intervening periods we carried out searches and applied our methods to a case study to test our ideas. Results: We have developed a two-stage framework, the first stage of which is designed to identify mechanisms underpinning a specific exposure–disease relationship; the second stage is a targeted systematic review of studies on a specific mechanism. As part of the methodology, we also developed an online tool for text mining for mechanism prioritization (TeMMPo) and a new graph for displaying related but heterogeneous data from epidemiologic studies (the Albatross plot). Conclusions: We have developed novel tools for identifying mechanisms and carrying out systematic reviews of mechanistic studies of exposure–disease relationships. In doing so, we have outlined how we have overcome the challenges that we faced and provided researchers with practical guides for conducting mechanistic systematic reviews. Impact: The aforementioned methodology and tools will allow potential mechanisms to be identified and the strength of the evidence underlying a particular mechanism to be assessed. Cancer Epidemiol Biomarkers Prev; 26(11); 1667–75. ©2017 AACR.

OBJECTIVES The aim of the study was to examine the application, design, and analysis characteristics of controlled before-after (CBA) and interrupted time series (ITS) studies and their use in Cochrane reviews. STUDY DESIGN AND SETTING We searched the Cochrane library for reviews including these study designs from May 2012 to March 2015 and purposively selected, where available, two reviews each across 10 prespecified intervention types. We randomly selected two CBA and two ITS studies from each review. Two researchers independently extracted information from the studies and the respective reviews. RESULTS Sixty-nine reviews considered CBA and ITS studies for inclusion. We analyzed 21 CBA and 16 ITS studies from 11 to 8 reviews, respectively. Cochrane reviews inconsistently defined and labeled CBA and ITS studies. Many studies did not meet the Cochrane definition or the minimum criteria provided by Cochrane Effective Practice and Organisation of Care. The studies present a heterogeneous set of study features and applied a large variety of analyses. CONCLUSION While CBA and ITS studies represent important study designs to evaluate the effects of interventions, especially on a population or organizational level, unclear study design features challenge unequivocal classification and appropriate use. We discuss options for more specific definitions and explicit criteria for CBA and ITS studies.

Background When designing and analysing clinical trials, using previous relevant information, perhaps in the form of evidence syntheses, can reduce research waste. We conducted the INVEST (INVestigating the use of Evidence Synthesis in the design and analysis of clinical Trials) survey to summarise the current use of evidence synthesis in trial design and analysis, to capture opinions of trialists and methodologists on such use, and to understand any barriers. Methods Our sampling frame was all delegates attending the International Clinical Trials Methodology Conference in November 2015. Respondents were asked to indicate (1) their views on the use of evidence synthesis in trial design and analysis, (2) their own use during the past 10 years and (3) the three greatest barriers to use in practice. Results Of approximately 638 attendees of the conference, 106 (17%) completed the survey, half of whom were statisticians. Support was generally high for using a description of previous evidence, a systematic review or a meta-analysis in trial design. Generally, respondents did not seem to be using evidence syntheses as often as they felt they should. For example, only 50% (42/84 relevant respondents) had used a meta-analysis to inform whether a trial is needed compared with 74% (62/84) indicating that this is desirable. Only 6% (5/81 relevant respondents) had used a value of information analysis to inform sample size calculations versus 22% (18/81) indicating support for this. Surprisingly large numbers of participants indicated support for, and previous use of, evidence syntheses in trial analysis. For example, 79% (79/100) of respondents indicated that external information about the treatment effect should be used to inform aspects of the analysis. The greatest perceived barrier to using evidence synthesis methods in trial design or analysis was time constraints, followed by a belief that the new trial was the first in the area. Conclusions Evidence syntheses can be resource-intensive, but their use in informing the design, conduct and analysis of clinical trials is widely considered desirable. We advocate additional research, training and investment in resources dedicated to ways in which evidence syntheses can be undertaken more efficiently, offering the potential for cost savings in the long term. Electronic supplementary material The online version of this article (doi:10.1186/s13063-017-1955-y) contains supplementary material, which is available to authorized users.

MotivationThe scientific literature contains a wealth of information from different fields on potential disease mechanisms. However, prioritising mechanisms for further analytical evaluation presents enormous challenges in terms of the quantity and diversity of published research. The application of data mining approaches to the literature offers the potential to identify and prioritise mechanisms for more focused and detailed analysis.ResultsHere we present MELODI, a literature mining platform that can identify mechanistic pathways between any two biomedical concepts. Two case studies demonstrate the potential uses of MELODI and how it can generate hypotheses for further investigation. Firstly, an analysis of ERG and prostate cancer derives the intermediate transcription factor SP1, recently confirmed to be physically interacting with ERG. Secondly, examining the relationship between a new potential risk factor for pancreatic cancer identifies possible mechanistic insights which can be studiedin vitro.AvailabilityMELODI has been implemented as a Python/Django web application, and is freely available to use atwww.melodi.biocompute.org.ukContactmelodi@biocompute.org.uk

BackgroundWarfarin is effective for stroke prevention in atrial fibrillation (AF), but anticoagulation is underused in clinical care. The risk of venous thromboembolic disease during hospitalisation can be reduced by low-molecular-weight heparin (LMWH): warfarin is the most frequently prescribed anticoagulant for treatment and secondary prevention of venous thromboembolism (VTE). Warfarin-related bleeding is a major reason for hospitalisation for adverse drug effects. Warfarin is cheap but therapeutic monitoring increases treatment costs. Novel oral anticoagulants (NOACs) have more rapid onset and offset of action than warfarin, and more predictable dosing requirements.ObjectiveTo determine the best oral anticoagulant/s for prevention of stroke in AF and for primary prevention, treatment and secondary prevention of VTE.DesignFour systematic reviews, network meta-analyses (NMAs) and cost-effectiveness analyses (CEAs) of randomised controlled trials.SettingHospital (VTE primary prevention and acute treatment) and primary care/anticoagulation clinics (AF and VTE secondary prevention).ParticipantsPatients eligible for anticoagulation with warfarin (stroke prevention in AF, acute treatment or secondary prevention of VTE) or LMWH (primary prevention of VTE).InterventionsNOACs, warfarin and LMWH, together with other interventions (antiplatelet therapy, placebo) evaluated in the evidence network.Main outcome measuresEfficacy Stroke, symptomatic VTE, symptomatic deep-vein thrombosis and symptomatic pulmonary embolism.Safety Major bleeding, clinically relevant bleeding and intracranial haemorrhage. We also considered myocardial infarction and all-cause mortality and evaluated cost-effectiveness.Data sourcesMEDLINE and PREMEDLINE In-Process & Other Non-Indexed Citations, EMBASE and The Cochrane Library, reference lists of published NMAs and trial registries. We searched MEDLINE and PREMEDLINE In-Process & Other Non-Indexed Citations, EMBASE and The Cochrane Library. The stroke prevention in AF review search was run on the 12 March 2014 and updated on 15 September 2014, and covered the period 2010 to September 2014. The search for the three reviews in VTE was run on the 19 March 2014, updated on 15 September 2014, and covered the period 2008 to September 2014.Review methodsTwo reviewers screened search results, extracted and checked data, and assessed risk of bias. For each outcome we conducted standard meta-analysis and NMA. We evaluated cost-effectiveness using discrete-time Markov models.ResultsApixaban (Eliquis®, Bristol-Myers Squibb, USA; Pfizer, USA) [5 mg bd (twice daily)] was ranked as among the best interventions for stroke prevention in AF, and had the highest expected net benefit. Edoxaban (Lixiana®, Daiichi Sankyo, Japan) [60 mg od (once daily)] was ranked second for major bleeding and all-cause mortality. Neither the clinical effectiveness analysis nor the CEA provided strong evidence that NOACs should replace postoperative LMWH in primary prevention of VTE. For acute treatment and secondary prevention of VTE, we found little evidence that NOACs offer an efficacy advantage over warfarin, but the risk of bleeding complications was lower for some NOACs than for warfarin. For a willingness-to-pay threshold of > £5000, apixaban (5 mg bd) had the highest expected net benefit for acute treatment of VTE. Aspirin or no pharmacotherapy were likely to be the most cost-effective interventions for secondary prevention of VTE: our results suggest that it is not cost-effective to prescribe NOACs or warfarin for this indication.ConclusionsNOACs have advantages over warfarin in patients with AF, but we found no strong evidence that they should replace warfarin or LMWH in primary prevention, treatment or secondary prevention of VTE.LimitationsThese relate mainly to shortfalls in the primary data: in particular, there were no head-to-head comparisons between different NOAC drugs.Future workCalculating the expected value of sample information to clarify whether or not it would be justifiable to fund one or more head-to-head trials.Study registrationThis study is registered as PROSPERO CRD42013005324, CRD42013005331 and CRD42013005330.FundingThe National Institute for Health Research Health Technology Assessment programme.

BACKGROUND: Overconsumption of food, alcohol, and tobacco products increases the risk of non-communicable diseases. Interventions to change characteristics of physical micro-environments where people may select or consume these products - including shops, restaurants, workplaces, and schools - are of considerable public health policy and research interest. This review addresses two types of intervention within such environments: altering the availability (the range and/or amount of options) of these products, or their proximity (the distance at which they are positioned) to potential consumers. OBJECTIVES: 1. To assess the impact on selection and consumption of altering the availability or proximity of (a) food (including non-alcoholic beverages), (b) alcohol, and (c) tobacco products.2. To assess the extent to which the impact of these interventions is modified by characteristics of: i. studies, ii. interventions, and iii. PARTICIPANTS: SEARCH METHODS: We searched CENTRAL, MEDLINE, Embase, PsycINFO, and seven other published or grey literature databases, as well as trial registries and key websites, up to 23 July 2018, followed by citation searches. SELECTION CRITERIA: We included randomised controlled trials with between-participants (parallel group) or within-participants (cross-over) designs. Eligible studies compared effects of exposure to at least two different levels of availability of a product or its proximity, and included a measure of selection or consumption of the manipulated product. DATA COLLECTION AND ANALYSIS: We used a novel semi-automated screening workflow and applied standard Cochrane methods to select eligible studies, collect data, and assess risk of bias. In separate analyses for availability interventions and proximity interventions, we combined results using random-effects meta-analysis and meta-regression models to estimate summary effect sizes (as standardised mean differences (SMDs)) and to investigate associations between summary effect sizes and selected study, intervention, or participant characteristics. We rated the certainty of evidence for each outcome using GRADE. MAIN RESULTS: We included 24 studies, with the majority (20/24) giving concerns about risk of bias. All of the included studies investigated food products; none investigated alcohol or tobacco. The majority were conducted in laboratory settings (14/24), with adult participants (17/24), and used between-participants designs (19/24). All studies were conducted in high-income countries, predominantly in the USA (14/24).Six studies investigated availability interventions, of which two changed the absolute number of different options available, and four altered the relative proportion of less-healthy (to healthier) options. Most studies (4/6) manipulated snack foods or drinks. For selection outcomes, meta-analysis of three comparisons from three studies (n = 154) found that exposure to fewer options resulted in a large reduction in selection of the targeted food(s): SMD -1.13 (95% confidence interval (CI) -1.90 to -0.37) (low certainty evidence). For consumption outcomes, meta-analysis of three comparisons from two studies (n = 150) found that exposure to fewer options resulted in a moderate reduction in consumption of those foods, but with considerable uncertainty: SMD -0.55 (95% CI -1.27 to 0.18) (low certainty evidence).Eighteen studies investigated proximity interventions. Most (14/18) changed the distance at which a snack food or drink was placed from the participants, whilst four studies changed the order of meal components encountered along a line. For selection outcomes, only one study with one comparison (n = 41) was identified, which found that food placed farther away resulted in a moderate reduction in its selection: SMD -0.65 (95% CI -1.29 to -0.01) (very low certainty evidence). For consumption outcomes, meta-analysis of 15 comparisons from 12 studies (n = 1098) found that exposure to food placed farther away resulted in a moderate reduction in its consumption: SMD -0.60 (95% CI -0.84 to -0.36) (low certainty evidence). Meta-regression analyses indicated that this effect was greater: the farther away the product was placed; when only the targeted product(s) was available; when participants were of low deprivation status; and when the study was at high risk of bias. AUTHORS' CONCLUSIONS: The current evidence suggests that changing the number of available food options or altering the positioning of foods could contribute to meaningful changes in behaviour, justifying policy actions to promote such changes within food environments. However, the certainty of this evidence as assessed by GRADE is low or very low. To enable more certain and generalisable conclusions about these potentially important effects, further research is warranted in real-world settings, intervening across a wider range of foods - as well as alcohol and tobacco products - and over sustained time periods.

Numerous meta-analyses in healthcare research combine results from only a small number of studies, for which the variance representing between-study heterogeneity is estimated imprecisely. A Bayesian approach to estimation allows external evidence on the expected magnitude of heterogeneity to be incorporated. The aim of this paper is to provide tools that improve the accessibility of Bayesian meta-analysis. We present two methods for implementing Bayesian meta-analysis, using numerical integration and importance sampling techniques. Based on 14 886 binary outcome meta-analyses in the Cochrane Database of Systematic Reviews, we derive a novel set of predictive distributions for the degree of heterogeneity expected in 80 settings depending on the outcomes assessed and comparisons made. These can be used as prior distributions for heterogeneity in future meta-analyses. The two methods are implemented in R, for which code is provided. Both methods produce equivalent results to standard but more complex Markov chain Monte Carlo approaches. The priors are derived as log-normal distributions for the between-study variance, applicable to meta-analyses of binary outcomes on the log odds-ratio scale. The methods are applied to two example meta-analyses, incorporating the relevant predictive distributions as prior distributions for between-study heterogeneity. We have provided resources to facilitate Bayesian meta-analysis, in a form accessible to applied researchers, which allow relevant prior information on the degree of heterogeneity to be incorporated. © 2014 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.

What is the drug of choice for condition x? is among the most commonly asked questions in primary care.1 Reflecting the complexity of prescribing decisions, answering this question requires a difficult trade-off between the benefits and harms of multiple drugs for a given condition. The principles of evidence-based medicine suggest that prescribing decisions should be guided by an objective benchmark, namely scientific evidence.2 Such evidence is particularly important when choosing a first-line treatment among multiple alternatives. Unfortunately, existing clinical evidence on benefits and harms is rarely adequate to inform prescribing decisions. A randomized controlled trial comparing all relevant drugs would provide such information. However, clinical trials are often designed for regulatory purposes and, therefore, include selective patient populations and do not include all available comparator drugs.3,4 To obtain insight into the comparative benefits and harms of multiple drugs, prescribers turn to summaries of evidence to discern the most promising drugs from their less effective comparators. Recent methods used to synthesize existing evidence provide much-needed information on the comparative benefits and harms of multiple drugs. Network meta-analysis is one such method that allows for the combination of direct and indirect evidences from randomized trials, facilitating the comparison of all relevant drugs even when they are not directly compared with each other in clinical trials.5 The recent surge in the number of network meta-analyses in the general medical literature is a testament to the increasing need for comparative evidence in prescribing decisions.6 Even when comparative evidence from network meta-analyses exists, however, making sense of it remains a challenge. In particular, prescribers and patients often struggle to weigh the relative benefits and harms of multiple alternatives. In this proof-of-concept study, we discuss the important yet challenging role of comparative clinical evidence in guiding prescribing decisions in …

objective To estimate the global prevalence of handwashing with soap and derive a pooled estimate of the effect of hygiene on diarrhoeal diseases, based on a systematic search of the literature. methods Studies with data on observed rates of handwashing with soap published between 1990 and August 2013 were identified from a systematic search of PubMed, Embase and ISI Web of Knowledge. A separate search was conducted for studies on the effect of hygiene on diarrhoeal disease that included randomised controlled trials, quasi-randomised trials with control group, observational studies using matching techniques and observational studies with a control group where the intervention was well defined. The search used Cochrane Library, Global Health, BIOSIS, PubMed, and Embase databases supplemented with reference lists from previously published systematic reviews to identify studies published between 1970 and August 2013. Results were combined using multilevel modelling for handwashing prevalence and meta-regression for risk estimates. results From the 42 studies reporting handwashing prevalence we estimate that approximately 19% of the world population washes hands with soap after contact with excreta (i.e. use of a sanitation facility or contact with children’s excreta). Meta-regression of risk estimates suggests that handwashing reduces the risk of diarrhoeal disease by 40% (risk ratio 0.60, 95% CI 0.53–0.68); however, when we included an adjustment for unblinded studies, the effect estimate was reduced to 23% (risk ratio 0.77, 95% CI 0.32–1.86). conclusions Our results show that handwashing after contact with excreta is poorly practiced globally, despite the likely positive health benefits.

Objective: To assess the impact of inadequate water and sanitation on diarrhoeal disease in low- and middle-income settings. Methods: The search strategy used Cochrane Library, MEDLINE & PubMed, Global Health, Embase and BIOSIS supplemented by screening of reference lists from previously published systematic reviews, to identify studies reporting on interventions examining the effect of drinking water and sanitation improvements in low- and middle-income settings published between 1970 and May 2013. Studies including randomised controlled trials, quasi-randomised trials with control group, observational studies using matching techniques and observational studies with a control group where the intervention was well defined were eligible. Risk of bias was assessed using a modified Ottawa-Newcastle scale. Study results were combined using meta-analysis and meta-regression to derive overall and intervention-specific risk estimates. Results: Of 6819 records identified for drinking water, 61 studies met the inclusion criteria, and of 12 515 records identified for sanitation, 11 studies were included. Overall, improvements in drinking water and sanitation were associated with decreased risks of diarrhoea. Specific improvements, such as the use of water filters, provision of high-quality piped water and sewer connections, were associated with greater reductions in diarrhoea compared with other interventions. Conclusions: The results show that inadequate water and sanitation are associated with considerable risks of diarrhoeal disease and that there are notable differences in illness reduction according to the type of improved water and sanitation implemented.

Network meta-analysis is becoming more popular as a way to analyse multiple treatments simultaneously and, in the right circumstances, rank treatments. A difficulty in practice is the possibility of ‘inconsistency’ or ‘incoherence’, where direct evidence and indirect evidence are not in agreement. Here, we develop a random-effects implementation of the recently proposed design-by-treatment interaction model, using these random effects to model inconsistency and estimate the parameters of primary interest. Our proposal is a generalisation of the model proposed by Lumley and allows trials with three or more arms to be included in the analysis. Our methods also facilitate the ranking of treatments under inconsistency. We derive R and I2 statistics to quantify the impact of the between-study heterogeneity and the inconsistency. We apply our model to two examples. © 2014 The Authors. Statistics in Medicine published by John Wiley & Sons, Ltd.

Objectives Although there is increasing interest in the evaluation of complex interventions, there is little guidance on how evidence from complex interventions may be reviewed and synthesized, and the relevance of the plethora of evidence synthesis methods to complexity is unclear. This article aims to explore how different meta-analytical approaches can be used to examine aspects of complexity; describe the contribution of various narrative, tabular, and graphical approaches to synthesis; and give an overview of the potential choice of selected qualitative and mixed-method evidence synthesis approaches. Study Design and Setting The methodological discussions presented here build on a 2-day workshop held in Montebello, Canada, in January 2012, involving methodological experts from the Campbell and Cochrane Collaborations and from other international review centers (Anderson L, Petticrew M, Chandler J, et al. Introduction: systematic reviews of complex interventions. In press). These systematic review methodologists discussed the broad range of existing methods and considered the relevance of these methods to reviews of complex interventions. Results The evidence from primary studies of complex interventions may be qualitative or quantitative. There is a wide range of methodological options for reviewing and presenting this evidence. Specific contributions of statistical approaches include the use of meta-analysis, meta-regression, and Bayesian methods, whereas narrative summary approaches provide valuable precursors or alternatives to these. Qualitative and mixed-method approaches include thematic synthesis, framework synthesis, and realist synthesis. A suitable combination of these approaches allows synthesis of evidence for understanding complex interventions. Conclusion Reviewers need to consider which aspects of complex interventions should be a focus of their review and what types of quantitative and/or qualitative studies they will be including, and this will inform their choice of review methods. These may range from standard meta-analysis through to more complex mixed-method synthesis and synthesis approaches that incorporate theory and/or user's perspectives.

Background: Because meta-analyses are increasingly prevalent and cited in the medical literature, it is important that tools are available to assess their methodological quality. When performing an empirical study of the quality of published meta-analyses, we found that existing tools did not place a strong emphasis on statistical and interpretational issues. Methods: We developed a quality-assessment tool using existing materials and expert judgment as a starting point, followed by multiple iterations of input from our working group, piloting, and discussion. After having used the tool for our empirical study, agreement for four key items in the tool was measured using weighted kappa coefficients. Results: Our tool contained 43 items divided into four key areas (data sources, analysis of individual studies, meta-analysis methods, and interpretation), and each area ended with a summary question. We also produced guidance for completing the tool. Agreement between raters was fair to moderate. Conclusions: The tool should usefully inform subsequent initiatives to develop quality-assessment tools for meta-analysis. We advocate use of consensus between independent raters when assessing statistical appropriateness and adequacy of interpretation in meta-analyses.

Context: Meta-analyses are regularly used to inform healthcare decisions. Concerns have been expressed about the quality of meta-analyses and, in particular, about those supported by the pharmaceutical industry. Objective: The objective of this study is to compare the quality of pharmaceutical-industry-supported meta-analyses with academic meta-analyses and of meta-analyses published before and after companies started to disclose their data. Data Sources: We identified industry-supported meta-analyses by searching the Scopus bibliographic database, using author affiliations. We matched each industry-supported meta-analysis with an academic meta-analysis using high-level MeSH terms in PubMed. Study Selection: We included meta-analyses of randomized trials assessing the efficacy or safety of any pharmaceutical intervention in humans, published in 2002–2004 or 2008–2009. Cochrane reviews were excluded. Two individuals independently selected papers, with discrepancies resolved by two further individuals. Assessment: We developed and piloted a quality-assessment tool, consisting of 43 questions in four domains, with a key summary question covering each domain. Two individuals independently assessed each meta-analysis. Results: We examined 126 meta-analysis publications in 63 matched pairs. The average quality was low, with fewer than 50% adequate in three of the four domains. Industry-supported meta-analyses less often

A key component of the Cochrane Collaboration's risk of bias tool for critically evaluating randomised trials is the consideration of whether baseline characteristics of the treatment groups being compared are systematically different. Considered under the domain of 'selection bias', this is currently evaluated by looking at the methods of randomisation and specifically at the generation of the randomised allocation sequence and the concealment of this sequence during the process of randomisation. Assessment of the actual similarity of baseline variables across groups in demographic and clinical characteristics is seldom performed. Even when performed, the link with selection bias is sometimes not considered. Methods of randomisation and allocation concealment are often poorly reported in published trials, yet baseline data tables are presented in a large majority of trial reports. In this article, we propose that assessment of trial baseline data should form a key and prominent part of selection bias judgements when using the risk of bias tool. We outline the possible benefits from using this approach, including reduced uncertainty in systematic review conclusions, reduced risk of chance findings being ascribed to treatment effects and better use of available evidence by a more considered approach to evaluating studies using imperfect randomisation and allocation methods.

Background There is increasing interest from review authors about including non-randomized studies (NRS) in their systematic reviews of health care interventions. This series from the Ottawa Non-Randomized Studies Workshop consists of six papers identifying methodological issues when doing this. Aim To format the guidance from the preceding papers on study design and bias, confounding and meta-analysis, selective reporting, and applicability/directness into checklists of issues for review authors to consider when including NRS in a systematic review. Checklists Checklists were devised providing frameworks to describe/assess: (1) study designs based on study design features; (2) risk of residual confounding and when to consider meta-analysing data from NRS; (3) risk of selective reporting based on the Cochrane framework for detecting selective outcome reporting in trials but extended to selective reporting of analyses; and (4) directness of evidence contributed by a study to aid integration of NRS findings into summary of findings tables. Summary The checklists described will allow review groups to operationalize the inclusion of NRS in systematic reviews in a more consistent way. The next major step is extending the existing Cochrane Risk of Bias tool so that it can assess the risk of bias to NRS included in a review. Copyright © 2013 John Wiley & Sons, Ltd.

Objectives: The number of systematic reviews that aim to compare multiple interventions using network meta-analysis is increasing. In this paper, we highlight aspects of a standard systematic review protocol that may need modification when multiple interventions are to be compared.Study Design and setting: We take the protocol format suggested by Cochrane for a standard systematic review as our reference, and compare the considerations for a pairwise review with those required for a valid comparison of multiple interventions. We suggest new sections for protocols of systematic reviews including network meta-analyses with a focus on how to evaluate theirassumptions. We provide example text from published protocols to exemplify the considerations.Results and Conclusion: Standard systematic review protocols for pairwise meta-analyses need extensions to accommodate the increased complexity of network meta-analysis. Our suggested modifications are widely applicable to both Cochrane and non-Cochrane systematic reviews involving network meta-analyses.

ObjectiveWe investigated the associations between risk of bias judgments from Cochrane reviews for sequence generation, allocation concealment and blinding and between-trial heterogeneity.Study Design and SettingBayesian hierarchical models were fitted to binary data from 117 meta-analyses, to estimate the ratio λ by which heterogeneity changes for trials at high/unclear risk of bias, compared to trials at low risk of bias. We estimated the proportion of between-trial heterogeneity in each meta-analysis that could be explained by the bias associated with specific design characteristics.ResultsUnivariable analyses showed that heterogeneity variances were, on average, increased among trials at high/unclear risk of bias for sequence generation (Math Eq 1.14, 95% interval: 0.57 to 2.30) and blinding (Math Eq 1.74, 95% interval: 0.85 to 3.47). Trials at high/unclear risk of bias for allocation concealment were on average less heterogeneous (Math Eq 0.75, 95% interval: 0.35 to 1.61). Multivariable analyses showed that a median of 37% (95% interval: 0% to 71%) heterogeneity variance could be explained by trials at high/unclear risk of bias for sequence generation, allocation concealment and/or blinding. All 95% intervals for changes in heterogeneity were wide and included the null of no difference.ConclusionOur interpretation of the results is limited by imprecise estimates. There is some indication that between-trial heterogeneity could be partially explained by reported design characteristics, and hence adjustment for bias could potentially improve accuracy of meta-analysis results.

Background Methods need to be further developed to include non-randomised studies (NRS) in systematic reviews of the effects of health care interventions. NRS are often required to answer questions about harms and interventions for which evidence from randomised controlled trials (RCTs) is not available. Methods used to review randomised controlled trials may be inappropriate or insufficient for NRS. Aim and methods A workshop was convened to discuss relevant methodological issues. Participants were invited from important stakeholder constituencies, including methods and review groups of the Cochrane and Campbell Collaborations, the Cochrane Editorial Unit and organisations that commission reviews and make health policy decisions. The aim was to discuss methods for reviewing evidence when including NRS and to formulate methodological guidance for review authors. Workshop format The workshop was structured around four sessions on topics considered in advance to be most critical: (i) study designs and bias; (ii) confounding and meta-analysis; (iii) selective reporting; and (iv) applicability. These sessions were scheduled between introductory and concluding sessions. Summary This is the first of six papers and provides an overview. Subsequent papers describe the discussions and conclusions from the four main sessions (papers 2 to 5) and summarise the proposed guidance into lists of issues for review authors to consider (paper 6). Copyright © 2013 John Wiley & Sons, Ltd.

Background In a meta-analysis of trials with missing outcome data, a parameter known as informative missing odds ratio (IMOR) can be used to quantify the relationship between informative missingness and a binary outcome. IMORs also account for the increased uncertainty due to missingness in the meta-analysis results. Purpose To extend the idea of IMOR into a network meta-analysis (NMA) setting in order to explore the impact of missing outcome data on the inferences about the relative effectiveness of several competing treatments in psychiatric trials. Methods IMORs were estimated in two datasets comparing anti-manic treatments and antidepressants. The outcome was response to treatments. In the original meta-analyses, missing participants were assumed to have failed regardless the treatment they were allocated to. To evaluate the robustness of this assumption in each dataset, several imputations of the missing outcomes were studied by an IMOR parameter in the NMA model. By comparing the odds ratios for efficacy under the initial analysis and under several assumptions about the missingness, we assessed the consistency of the conclusions. The missing data mechanism was studied by comparing the prior with the posterior IMOR distribution. Models were fitted using Markov chain Monte Carlo (MCMC) in WinBUGS. Results In both datasets, the relative effectiveness of the treatments seems to be affected only by the two extreme imputation scenarios of worst- and best-case analyses. Moreover, heterogeneity increases in both datasets under these two extreme scenarios. Overall, there is a non-significant change on the ranking of the anti-manic and antidepressant treatments. The posterior and prior IMOR distributions are very similar showing that the data do not provide any information about the true outcome in missing participants. There is a very weak indication that missing participants tend to fail in placebo and paroxetine, while the opposite occurs for sertraline, fluoxetine, and fluvoxamine. Limitations Investigation of informative missingness was limited two classes of treatments and for dichotomous outcome measures. The proportion of missing outcomes was very low overall, and hence, the power of detecting any differences in effectiveness estimated under the various imputation methods is small. Conclusions Sensitivity analysis to account for missing outcome data and their uncertainty in the NMA can be undertaken by extending the idea of IMOR. In two case examples, we found no differences between the various models due to low missing data rate. In line with previous observations, data carry little information about the reason of missingness.

Many meta-analyses combine results from only a small number of studies, a situation in which the between-study variance is imprecisely estimated when standard methods are applied. Bayesian meta-analysis allows incorporation of external evidence on heterogeneity, providing the potential for more robust inference on the effect size of interest. We present a method for performing Bayesian meta-analysis using data augmentation, in which we represent an informative conjugate prior for between-study variance by pseudo data and use meta-regression for estimation. To assist in this, we derive predictive inverse-gamma distributions for the between-study variance expected in future meta-analyses. These may serve as priors for heterogeneity in new meta-analyses. In a simulation study, we compare approximate Bayesian methods using meta-regression and pseudo data against fully Bayesian approaches based on importance sampling techniques and Markov chain Monte Carlo (MCMC). We compare the frequentist properties of these Bayesian methods with those of the commonly used frequentist DerSimonian and Laird procedure. The method is implemented in standard statistical software and provides a less complex alternative to standard MCMC approaches. An importance sampling approach produces almost identical results to standard MCMC approaches, and results obtained through meta-regression and pseudo data are very similar. On average, data augmentation provides closer results to MCMC, if implemented using restricted maximum likelihood estimation rather than DerSimonian and Laird or maximum likelihood estimation. The methods are applied to real datasets, and an extension to network meta-analysis is described. The proposed method facilitates Bayesian meta-analysis in a way that is accessible to applied researchers. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

Multiple corticosteroids and treatment regimens have been used as adjuncts in the treatment of septic shock. Qualitative and quantitative differences exist at cellular and tissular levels between the different drugs and their patterns of delivery. The objective of this study was to elucidate any differences between the drugs and their treatment regimens regarding outcomes for corticosteroid use in adult patients with septic shock.Network meta-analysis of the data used for the recently conducted Cochrane review was performed. Studies that included children and were designed to assess respiratory function in pneumonia and acute respiratory distress syndrome, as well as cross-over studies, were excluded. Network plots were created for each outcome, and all analyses were conducted using a frequentist approach assuming a random-effects model.Complete data from 22 studies and partial data from 1 study were included. Network meta-analysis provided no clear evidence that any intervention or treatment regimen is better than any other across the spectrum of outcomes. There was strong evidence of differential efficacy in only one area: shock reversal. Hydrocortisone boluses and infusions were more likely than methylprednisolone boluses and placebo to result in shock reversal.There was no clear evidence that any one corticosteroid drug or treatment regimen is more likely to be effective in reducing mortality or reducing the incidence of gastrointestinal bleeding or superinfection in septic shock. Hydrocortisone delivered as a bolus or as an infusion was more likely than placebo and methylprednisolone to result in shock reversal.

Trialists and epidemiologists often employ different terminology to refer to biases in randomized trials and observational studies, even though many biases have a similar structure in both types of study. We use causal diagrams to represent the structure of biases, as described by the Cochrane Collaboration for randomized trials, and provide a translation to the usual epidemiologic terms of confounding, selection bias, and measurement bias. This structural approach clarifies that an explicit description of the inferential goal—the intention-to-treat effect or the per-protocol effect—is necessary to assess risk of bias in the estimates. Being aware of each other’s terminologies will enhance communication between trialists and epidemiologists when considering key concepts and methods for causal inference.

Objectives To evaluate the effects on non-specific and all cause mortality, in children under 5, of Bacillus Calmette-Guérin (BCG), diphtheria-tetanus-pertussis (DTP), and standard titre measles containing vaccines (MCV); to examine internal validity of the studies; and to examine any modifying effects of sex, age, vaccine sequence, and co-administration of vitamin A.Design Systematic review, including assessment of risk of bias, and meta-analyses of similar studies.Study eligibility criteria Clinical trials, cohort studies, and case-control studies of the effects on mortality of BCG, whole cell DTP, and standard titre MCV in children under 5.Data sources Searches of Medline, Embase, Global Index Medicus, and the WHO International Clinical Trials Registry Platform, supplemented by contact with experts in the field. To avoid overlap in children studied across the included articles, findings from non-overlapping birth cohorts were identified.Results Results from 34 birth cohorts were identified. Most evidence was from observational studies, with some from short term clinical trials. Most studies reported on all cause (rather than non-specific) mortality. Receipt of BCG vaccine was associated with a reduction in all cause mortality: the average relative risks were 0.70 (95% confidence interval 0.49 to 1.01) from five clinical trials and 0.47 (0.32 to 0.69) from nine observational studies at high risk of bias. Receipt of DTP (almost always with oral polio vaccine) was associated with a possible increase in all cause mortality on average (relative risk 1.38, 0.92 to 2.08) from 10 studies at high risk of bias; this effect seemed stronger in girls than in boys. Receipt of standard titre MCV was associated with a reduction in all cause mortality (relative risks 0.74 (0.51 to 1.07) from four clinical trials and 0.51 (0.42 to 0.63) from 18 observational studies at high risk of bias); this effect seemed stronger in girls than in boys. Seven observational studies, assessed as being at high risk of bias, have compared sequences of vaccines; results of a subset of these suggest that administering DTP with or after MCV may be associated with higher mortality than administering it before MCV.Conclusions Evidence suggests that receipt of BCG and MCV reduce overall mortality by more than would be expected through their effects on the diseases they prevent, and receipt of DTP may be associated with an increase in all cause mortality. Although efforts should be made to ensure that all children are immunised on schedule with BCG, DTP, and MCV, randomised trials are needed to compare the effects of different sequences.

Non-randomized studies of the effects of interventions are critical to many areas of health care evaluation, but their results may be biased. It is therefore important to understand and appraise their strengths and weaknesses. We developed ROBINS-I (“Risk Of Bias In Non-randomized Studies - of Interventions”), a new tool for evaluating risk of bias in estimates of the comparative effectiveness (harm or benefit) of interventions from studies that did not use randomization to allocate units (individuals or clusters of individuals) to comparison groups. The tool will be particularly useful to those undertaking systematic reviews that include non-randomized studies.

BACKGROUND: It is uncertain whether concurrent use of low-dose aspirin removes the gastrointestinal benefit displayed by COX-2 selective inhibitors (coxibs) when compared to traditional nonsteroidal anti-inflammatory drugs (NSAIDs).AIM: To evaluate the gastrointestinal risks associated with coxibs and traditional NSAIDs and the interaction with concurrent use of low-dose aspirin.METHODS: We searched MEDLINE, EMBASE and the Cochrane Library through April 2016 to identify randomised trials comparing the gastrointestinal risk between coxibs and traditional NSAIDs in patients taking or not taking low-dose aspirin. Results were combined using random effects meta-analysis. Subgroup analyses by concurrent use of aspirin were undertaken.RESULTS: Eleven trials (84 150 participants) were included. The overall relative risk (RR) of coxibs vs. traditional NSAIDs for complicated gastrointestinal events was 0.54 (95% CI, confidence interval 0.32-0.92), with a significant subgroup difference (P = 0.04) according to concurrent use of aspirin (used: RR = 0.96, 95% CI 0.66-1.24; not used: RR = 0.33, 95% CI 0.14-0.83). The overall RR for clinical gastrointestinal events was 0.59 (95% CI 0.47-0.75), with a significant subgroup difference according to aspirin usage (P = 0.008; used: RR = 0.77, 95% CI 0.62-0.95; not used: RR = 0.50, 95% CI 0.39-0.64). Overall coxibs were associated with significantly lower risk of symptomatic ulcers (RR = 0.60, 95% CI 0.50-0.72) and endoscopic ulcers (RR = 0.29, 95% CI 0.16-0.53) than traditional NSAIDs; a significant subgroup difference was shown for endoscopic ulcers (P = 0.05) but not for symptomatic ulcers (P = 0.27).CONCLUSION: Concomitant use of low-dose aspirin reduces but does not completely eliminate the gastrointestinal benefit of coxibs over traditional NSAIDs.

Patient dropout is common in mental health trials. It is important to understand why patients drop out from trials, so that measures can be taken to minimize its occurrence. This research sought to identify trial characteristics that have an impact on premature discontinuation in antipsychotic trials for schizophrenia. Methods: Poisson regression analysis was applied with dropout rate per patient-week as the dependent variable and trial characteristics as independent variables. Multinomial logistic regression analysis was performed to examine whether the same characteristics predict whether patients drop out without providing any outcome data and whether they drop out with sufficient early data for a 'last observation carried forward' analysis to be performed. Results: trials with adequate allocation concealment, double blinding, placebo as control, higher precision, larger trial size, at least three treatment arms, recent publication, conduct in the United States and enrollment of inpatients were all associated with higher dropout rates. Similar factors were associated with whether a patient was more likely to be evaluated at least once, or be excluded entirely from the analysis. However, blinding status did not predict the former type of dropout, and allocation concealment, higher precision and larger sample size, number of arms, recent publication and recruiting inpatient did not predict the latter type of dropout. Conclusions: high dropout rates in antipsychotic trials can be associated with various characteristics, and appears to be particularly associated with use of placebo and study size. © 2013 Elsevier B.V. and ECNP.

In this issue of Evidence-Based Mental Health , Gartlehner and Gaynes1 comment (see page 98) on our recently published systematic review2 that investigated the comparative efficacy and acceptability of 12 new generation antidepressants (see page 107) . In their view, methodological shortcomings limit the validity of our results and the conclusions reached. In this commentary, our aim is to explain the rationale for doing this systematic review, outline its main findings and address the points raised by Gartlehner and Gaynes.1 Scientific debate can illuminate and clarify complex analyses and we are, therefore, delighted to respond to their critique. While we consider that some of the issues raised are substantive and merit reasoned response, we also believe that some of their criticisms seem rather overstated. We understand that Gartlehner and Gaynes too have published an analysis comparing antidepressants and we note that this is now the third occasion on which they have published similar criticisms of our work.3 ,4 In most countries, demonstration of a difference against placebo, and not against an active comparator, makes a new drug eligible for registration. The European Medicines Agency, for example, is willing to evaluate new antidepressants in the absence of comparison with active existing treatments.5 In situations where no (or a few) active treatments are available, this may not be important. In the field of antidepressants, however, where many potentially effective agents are already available, this process has serious implications. The approval of a new antidepressant as effective and safe in comparison solely with placebo allows the marketing of new drugs that may, in fact, be potentially more effective, similarly effective or even less effective than others currently in use. Many antidepressants have never been directly compared with each other. The picture is further complicated by …

Summary Background In recent years, infections with carbapenemase-producing Enterobacteriaceae (CPE) have been increasing globally and present a major public health challenge. Aim To review the international literature: (i) to describe CPE outbreaks in acute hospital settings globally; and (ii) to identify the control measures used during these outbreaks and report on their effectiveness. Methods A systematic search of MEDLINE and EMBASE databases, abstract lists for key conferences and reference lists of key reviews was undertaken, and information on unpublished outbreaks was sought for 2000–2015. Where relevant, risk of bias was assessed using the Newcastle–Ottawa scale. A narrative synthesis of the evidence was conducted. Findings Ninety-eight outbreaks were eligible. These occurred worldwide, with 53 reports from Europe. The number of cases (CPE infection or colonization) involved in outbreaks varied widely, from two to 803. In the vast majority of outbreaks, multi-component infection control measures were used, commonly including: patient screening; contact precautions (e.g. gowns, gloves); handwashing interventions; staff education or monitoring; enhanced environmental cleaning/decontamination; cohorting of patients and/or staff; and patient isolation. Seven studies were identified as providing the best-available evidence on the effectiveness of control measures. These demonstrated that CPE outbreaks can be controlled successfully using a range of appropriate, commonly used, infection control measures. However, risk of bias was considered relatively high for these studies. Conclusion The findings indicate that CPE outbreaks can be controlled using combinations of existing measures. However, the quality of the evidence base is weak and further high-quality research is needed, particularly on the effectiveness of individual infection control measures.

Background Selective reporting is included as a core domain of Cochrane’s tool for assessing risk of bias in randomised trials. There has been no evaluation of review authors’ use of this domain. We aimed to evaluate assessments of selective reporting in a cross-section of Cochrane reviews and to outline areas for improvement. Methods We obtained data on selective reporting judgements for 8434 studies included in 586 Cochrane reviews published from issue 1–8, 2015. One author classified the reasons for judgements of high risk of selective reporting bias. We randomly selected 100 reviews with at least one trial rated at high risk of outcome non-reporting bias (non-/partial reporting of an outcome on the basis of its results). One author recorded whether the authors of these reviews incorporated the selective reporting assessment when interpreting results. Results Of the 8434 studies, 1055 (13 %) were rated at high risk of bias on the selective reporting domain. The most common reason was concern about outcome non-reporting bias. Few studies were rated at high risk because of concerns about bias in selection of the reported result (e.g. reporting of only a subset of measurements, analysis methods or subsets of the data that were pre-specified). Review authors often specified in the risk of bias tables the study outcomes that were not reported (84 % of studies) but less frequently specified the outcomes that were partially reported (61 % of studies). At least one study was rated at high risk of outcome non-reporting bias in 31 % of reviews. In the random sample of these reviews, only 30 % incorporated this information when interpreting results, by acknowledging that the synthesis of an outcome was missing data that were not/partially reported. Conclusions Our audit of user practice in Cochrane reviews suggests that the assessment of selective reporting in the current risk of bias tool does not work well. It is not always clear which outcomes were selectively reported or what the corresponding risk of bias is in the synthesis with missing outcome data. New tools that will make it easier for reviewers to convey this information are being developed. Electronic supplementary material The online version of this article (doi:10.1186/s13643-016-0289-2) contains supplementary material, which is available to authorized users.