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Objectives: The objective of the study was to outline key considerations for general clinical readers when critically evaluating publications on platform trials and for researchers when designing these types of clinical trials., Study Design and Setting: In this review, we describe key concepts of platform trials with case study discussion of two hallmark platform trials in STAMPEDE and I-SPY2. We provide reader's guide to platform trials with a critical appraisal checklist., Results: Platform trials offer flexibilities of dropping ineffective arms early based on interim data and introducing new arms into the trial. For platform trials, it is important to consider how interventions are compared and evaluated throughout and how new interventions are introduced. For intervention comparisons, it is important to consider what the primary analysis is, what and how many interventions are active simultaneously, and allocation between different arms. Interim evaluation considerations should include the number and timing of interim evaluations and outcomes and statistical rules used to drop interventions. New interventions are usually introduced based on scientific merits, so consideration of these merits is important, together with the timing and mechanisms in which new interventions are added., Conclusion: More efforts are needed to improve the scientific literacy of platform trials. Our review provides an overview of the important concepts of platform trials., (Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.)

Objective: The objectives of this study were to evaluate the current state of reporting and handling of effect modification in network meta-analyses (NMAs) and perform exploratory analyses to identify variables that are potentially associated with incomplete reporting of effect modifiers in NMAs., Study Design and Setting: We conducted a meta-epidemiological survey using a systematic review of NMAs published in 2013 and identified through MEDLINE and Embase databases., Results: The review identified 77 NMAs. The most common type of effect modifiers identified and explored were patient characteristics (50.7% or 39/77), and the most common adjustment method used was sensitivity analysis (51.7% or 30/58). Over 45% (35/77) of studies did not describe a plan, nearly 40% (30/77) did not report the results of analyses, and approximately 47% (36/77) of studies had incomplete reporting. Exploratory univariate regression analyses yielded a statistically significant association for the variables of journal impact factor, ratio of randomized controlled trials to number of comparisons, and total number of randomized controlled trials., Conclusion: Current reporting practices are largely deficient, given that almost half of identified published NMAs do not explore or report effect modification. Journal impact factor and amount of available information in a network were associated with completeness of reporting., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Therapeutic substitutions are common at the level of ministries of health, clinicians, and pharmacy dispensaries. Guidance in determining whether drugs offer similar risk-benefit profiles is limited. Those making decisions on therapeutic substitutions should be aware of potential biases that make differentiating therapeutic agents difficult. Readers should consider whether the biological mechanisms and doses are similar across agents, whether the evidence is sufficiently valid across agents, and whether the safety and therapeutic effects of each drug are similar. This article uses a problem-based format to address the biological mechanism, validity, and results of a scenario in which therapeutic substitutions may be considered., (Copyright © 2014 Elsevier Inc. All rights reserved.)

Presenting continuous outcomes in Summary of Findings tables presents particular challenges to interpretation. When each study uses the same outcome measure, and the units of that measure are intuitively interpretable (e.g., duration of hospitalization, duration of symptoms), presenting differences in means is usually desirable. When the natural units of the outcome measure are not easily interpretable, choosing a threshold to create a binary outcome and presenting relative and absolute effects become a more attractive alternative. When studies use different measures of the same construct, calculating summary measures requires converting to the same units of measurement for each study. The longest standing and most widely used approach is to divide the difference in means in each study by its standard deviation and present pooled results in standard deviation units (standardized mean difference). Disadvantages of this approach include vulnerability to varying degrees of heterogeneity in the underlying populations and difficulties in interpretation. Alternatives include presenting results in the units of the most popular or interpretable measure, converting to dichotomous measures and presenting relative and absolute effects, presenting the ratio of the means of intervention and control groups, and presenting the results in minimally important difference units. We outline the merits and limitations of each alternative and provide guidance for meta-analysts and guideline developers., (Copyright © 2013 Elsevier Inc. All rights reserved.)

GRADE suggests that examination of 95% confidence intervals (CIs) provides the optimal primary approach to decisions regarding imprecision. For practice guidelines, rating down the quality of evidence (i.e., confidence in estimates of effect) is required if clinical action would differ if the upper versus the lower boundary of the CI represented the truth. An exception to this rule occurs when an effect is large, and consideration of CIs alone suggests a robust effect, but the total sample size is not large and the number of events is small. Under these circumstances, one should consider rating down for imprecision. To inform this decision, one can calculate the number of patients required for an adequately powered individual trial (termed the "optimal information size" [OIS]). For continuous variables, we suggest a similar process, initially considering the upper and lower limits of the CI, and subsequently calculating an OIS. Systematic reviews require a somewhat different approach. If the 95% CI excludes a relative risk (RR) of 1.0, and the total number of events or patients exceeds the OIS criterion, precision is adequate. If the 95% CI includes appreciable benefit or harm (we suggest an RR of under 0.75 or over 1.25 as a rough guide) rating down for imprecision may be appropriate even if OIS criteria are met., (Copyright © 2011 Elsevier Inc. All rights reserved.)

Objective: The objective of this paper is to describe the problem of multiple comparisons in systematic reviews and to provide some guidelines on how to deal with it in practice., Study Design and Setting: We describe common reasons for multiplicity in systematic reviews, and present some examples. We provide guidance on how to deal with multiplicity when it is unavoidable., Results: We identified six common reasons for multiplicity in systematic reviews: multiple outcomes, multiple groups, multiple time points, multiple effect measures, subgroup analyses, and multiple looks at accumulating data. The existing methods to deal with multiplicity in single trials can not always be applied in systematic reviews., Conclusion: There is no simple and completely satisfactory solution to the problem of multiple comparisons in systematic reviews. More research is required to develop multiple comparison procedures for use in systematic reviews. Authors and consumers of systematic reviews should give serious attention to multiplicity in systematic reviews when presenting, interpreting and using the results of these reports.

Objectives: To evaluate meta-analyses with trial sequential analysis (TSA). TSA adjusts for random error risk and provides the required number of participants (information size) in a meta-analysis. Meta-analyses not reaching information size are analyzed with trial sequential monitoring boundaries analogous to interim monitoring boundaries in a single trial., Study Design and Setting: We applied TSA on meta-analyses performed in Cochrane Neonatal reviews. We calculated information sizes and monitoring boundaries with three different anticipated intervention effects of 30% relative risk reduction (TSA(30%)), 15% (TSA(15%)), or a risk reduction suggested by low-bias risk trials of the meta-analysis corrected for heterogeneity (TSA(LBHIS))., Results: A total of 174 meta-analyses were eligible; 79 out of 174 (45%) meta-analyses were statistically significant (P<0.05). In the significant meta-analyses, TSA(30%) showed firm evidence in 61%. TSA(15%) and TSA(LBHIS) found firm evidence in 33% and 73%, respectively. The remaining significant meta-analyses had potentially spurious evidence of effect. In the 95 statistically nonsignificant (P>or=0.05) meta-analyses, TSA(30%) showed absence of evidence in 80% (insufficient information size). TSA(15%) and TSA(LBHIS) found that 95% and 91% had absence of evidence. The remaining nonsignificant meta-analyses had evidence of lack of effect., Conclusion: TSA reveals insufficient information size and potentially false positive results in many meta-analyses.

Background and Objective: Cumulative meta-analyses are prone to produce spurious P<0.05 because of repeated testing of significance as trial data accumulate. Information size in a meta-analysis should at least equal the sample size of an adequately powered trial. Trial sequential analysis (TSA) corresponds to group sequential analysis of a single trial and may be applied to meta-analysis to evaluate the evidence., Study Design and Setting: Six randomly selected neonatal meta-analyses with at least five trials reporting a binary outcome were examined. Low-bias heterogeneity-adjusted information size and information size determined from an assumed intervention effect of 15% were calculated. These were used for constructing trial sequential monitoring boundaries. We assessed the cumulative z-curves' crossing of P=0.05 and the boundaries., Results: Five meta-analyses showed early potentially spurious P<0.05 values. In three significant meta-analyses the cumulative z-curves crossed both boundaries, establishing firm evidence of an intervention effect. In two nonsignificant meta-analyses the cumulative z-curves crossed P=0.05, but never the boundaries, demonstrating early potentially spurious P<0.05 values. In one nonsignificant meta-analysis the cumulative z-curves never crossed P=0.05 or the boundaries., Conclusion: TSAs may establish when firm evidence is reached in meta-analysis.