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Introduction: Bayesian adaptive designs for clinical trials have gained popularity in the recent years due to the flexibility and efficiency that they offer. We consider the scenario where the outcome of interest comprises events with relatively low risk of occurrence and different case definitions resulting in varying control group risk assumptions. This is a scenario that occurs frequently for infectious diseases in global health research., Methods: We propose a Bayesian adaptive design that incorporates different case definitions of the outcome of interest that vary in stringency. A set of stopping rules are proposed where superiority and futility may be concluded with respect to different outcome definitions and therefore maintain a realistic probability of stopping in trials with low event rates. Through a simulation study, a variety of stopping rules and design configurations are compared., Results: The simulation results are provided in an interactive web application that allows the user to explore and compare the design operating characteristics for a variety of assumptions and design parameters with respect to different outcome definitions. The results for select simulation scenarios are provided in the article., Discussion: Bayesian adaptive designs offer the potential for maximizing the information learned from the data collected through clinical trials. The proposed design enables monitoring and utilizing multiple composite outcomes based on rare events to optimize the trial design operating characteristics.

Evaluating whether an intervention works when trialled in groups of individuals can pose complex challenges for clinical research. Cluster randomised controlled trials involve the random allocation of groups or clusters of individuals to receive an intervention, and they are commonly used in global health research. In this paper, we describe the potential reasons for the increasing popularity of cluster trials in low-income and middle-income countries. We also draw on key areas of global health research for an assessment of common trial planning practices, and we address their methodological shortcomings and pitfalls. Lastly, we discuss alternative approaches for population-level intervention trials that could be useful for research undertaken in low-income and middle-income countries for situations in which the use of cluster randomisation might not be appropriate., (Copyright © 2021 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

In global health research, short-term, small-scale clinical trials with fixed, two-arm trial designs that generally do not allow for major changes throughout the trial are the most common study design. Building on the introductory paper of this Series, this paper discusses data-driven approaches to clinical trial research across several adaptive trial designs, as well as the master protocol framework that can help to harmonise clinical trial research efforts in global health research. We provide a general framework for more efficient trial research, and we discuss the importance of considering different study designs in the planning stage with statistical simulations. We conclude this second Series paper by discussing the methodological and operational complexity of adaptive trial designs and master protocols and the current funding challenges that could limit uptake of these approaches in global health research., (Copyright © 2021 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

Background: The 2018 World Health Organization HIV guidelines were based on the results of a network meta-analysis (NMA) of published trials. This study employed individual patient-level data (IPD) and aggregate data (AgD) and meta-regression methods to assess the evidence supporting the WHO recommendations and whether they needed any refinements., Methods: Access to IPD from three trials was granted through ClinicalStudyDataRequest.com (CSDR). Seven modelling approaches were applied and compared: 1) Unadjusted AgD network meta-analysis (NMA) - the original analysis; 2) AgD-NMA with meta-regression; 3) Two-stage IPD-AgD NMA; 4) Unadjusted one-stage IPD-AgD NMA; 5) One-stage IPD-AgD NMA with meta-regression (one-stage approach); 6) Two-stage IPD-AgD NMA with empirical-priors (empirical-priors approach); 7) Hierarchical meta-regression IPD-AgD NMA (HMR approach). The first two were the models used previously. Models were compared with respect to effect estimates, changes in the effect estimates, coefficient estimates, DIC and model fit, rankings and between-study heterogeneity., Results: IPD were available for 2160 patients, representing 6.5% of the evidence base and 3 of 24 edges. The aspect of the model affected by the choice of modeling appeared to differ across outcomes. HMR consistently generated larger intervals, often with credible intervals (CrI) containing the null value. Discontinuations due to adverse events and viral suppression at 96 weeks were the only two outcomes for which the unadjusted AgD NMA would not be selected. For the first, the selected model shifted the principal comparison of interest from an odds ratio of 0.28 (95% CrI: 10.17, 0.44) to 0.37 (95% CrI: 0.23, 0.58). Throughout all outcomes, the regression estimates differed substantially between AgD and IPD methods, with the latter being more often larger in magnitude and statistically significant., Conclusions: Overall, the use of IPD often impacted the coefficient estimates, but not sufficiently as to necessitate altering the final recommendations of the 2018 WHO Guidelines. Future work should examine the features of a network where adjustments will have an impact, such as how much IPD is required in a given size of network.

Response adaptive randomized clinical trials have gained popularity due to their flexibility for adjusting design components, including arm allocation probabilities, at any point in the trial according to the intermediate results. In the Bayesian framework, allocation probabilities to different treatment arms are commonly defined as functionals of the posterior distributions of parameters of the outcome distribution for each treatment. In a non-conjugate model, however, repeated updates of the posterior distribution can be computationally intensive. In this article, we propose an adaptation of sequential Monte Carlo for efficiently updating the posterior distribution of parameters as new outcomes are observed in a general adaptive trial design. An efficient computational tool facilitates implementation of more flexible designs with more frequent interim looks that can in turn reduce the required sample size and expected number of failures in clinical trials. Moreover, more complex statistical models that reflect realistic modeling assumptions can be used for analysis of trial results., (© The Author 2018. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background: Master protocols, classified as basket trials, umbrella trials, and platform trials, are novel designs that investigate multiple hypotheses through concurrent sub-studies (e.g., multiple treatments or populations or that allow adding/removing arms during the trial), offering enhanced efficiency and a more ethical approach to trial evaluation. Despite the many advantages of these designs, they are infrequently used., Methods: We conducted a landscape analysis of master protocols using a systematic literature search to determine what trials have been conducted and proposed for an overall goal of improving the literacy in this emerging concept. On July 8, 2019, English-language studies were identified from MEDLINE, EMBASE, and CENTRAL databases and hand searches of published reviews and registries., Results: We identified 83 master protocols (49 basket, 18 umbrella, and 16 platform trials). The number of master protocols has increased rapidly over the last five years. Most have been conducted in the US (n = 44/83) and investigated experimental drugs (n = 82/83) in the field of oncology (n = 76/83). The majority of basket trials were exploratory (i.e., phase I/II; n = 47/49) and not randomized (n = 44/49), and more than half (n = 28/48) investigated only a single intervention. The median sample size of basket trials was 205 participants (interquartile range, Q3-Q1 [IQR]: 500-90 = 410), and the median study duration was 22.3 (IQR: 74.1-42.9 = 31.1) months. Similar to basket trials, most umbrella trials were exploratory (n = 16/18), but the use of randomization was more common (n = 8/18). The median sample size of umbrella trials was 346 participants (IQR: 565-252 = 313), and the median study duration was 60.9 (IQR: 81.3-46.9 = 34.4) months. The median number of interventions investigated in umbrella trials was 5 (IQR: 6-4 = 2). The majority of platform trials were randomized (n = 15/16), and phase III investigation (n = 7/15; one did not report information on phase) was more common in platform trials with four of them using seamless II/III design. The median sample size was 892 (IQR: 1835-255 = 1580), and the median study duration was 58.9 (IQR: 101.3-36.9 = 64.4) months., Conclusions: We anticipate that the number of master protocols will continue to increase at a rapid pace over the upcoming decades. More efforts to improve awareness and training are needed to apply these innovative trial design methods to fields outside of oncology.

Determining the quality of a randomized clinical trial (RCT) is necessary for decision-makers to determine the believability and applicability of the trial findings. Issues that are likely to affect the utility of RCT evidence include issues of bias, random error and applicability. In this article we focus primarily on issues of bias and examine the evidence for whether reporting methodological items, including allocation concealment, sequence generation, and blinding of participants can be relied upon as evidence of bias. We present the findings of a systematic review of meta-epidemiological studies and a simulation study demonstrating that commonly examined sources of bias likely play little role in treatment exaggeration. We discuss other issues that may additionally influence trial outcomes including sample size, publication bias, and expertise of trialists. We conclude by discussing strategies to moderate the effect of known biases in assessing overall estimates of treatment effects., (Copyright © 2015 Elsevier Inc. All rights reserved.)

Background: Antipsychotic medications, particularly second-generation antipsychotics, are increasingly being used to alleviate the symptoms of schizophrenia and other severe mental disorders in the pediatric population. While evidence-based approaches examining efficacy and safety outcomes have been reported, no review has evaluated prolactin-based adverse events for antipsychotic treatments in schizophrenia and schizophrenia spectrum disorders., Methods/design: Searches involving MEDLINE, EMBASE, CENTRAL, PsycINFO, and clinical trial registries (ClinicalTrials.gov, Drug Industry Document Archive [DIDA], International Clinical Trials Registry Platform [ICTRP]) will be used to identify relevant studies. Two reviewers will independently screen abstracts and relevant full-text articles of the papers identified by the initial search according to the prospectively defined eligibility criteria. Data extraction will be conducted in duplicate independently. Pairwise random effects meta-analyses and network meta-analyses will be conducted on individual drug and class effects where appropriate., Discussion: This systematic review will evaluate prolactin-based adverse events of first- and second-generation antipsychotics in the pediatric population with schizophrenia and schizophrenia spectrum disorders. It will also seek to strengthen the evidence base of the safety of antipsychotics by incorporating both randomized controlled trials and observational studies., Systematic Review Registration: PROSPERO CRD42014009506.

Background: Randomized control trials (RCTs) stopped early for benefit (truncated RCTs) are increasingly common and, on average, overestimate the relative magnitude of benefit by approximately 30%. Investigators stop trials early when they consider it is no longer ethical to enroll patients in a control group. The goal of this systematic review is to determine how investigators of ongoing or planned RCTs respond to the publication of a truncated RCT addressing a similar question., Methods/design: We will conduct systematic reviews to update the searches of 210 truncated RCTs to identify similar trials ongoing at the time of publication, or started subsequently, to the truncated trials ('subsequent RCTs'). Reviewers will determine in duplicate the similarity between the truncated and subsequent trials. We will analyze the epidemiology, distribution, and predictors of subsequent RCTs. We will also contact authors of subsequent trials to determine reasons for beginning, continuing, or prematurely discontinuing their own trials, and the extent to which they rely on the estimates from truncated trials., Discussion: To the extent that investigators begin or continue subsequent trials they implicitly disagree with the decision to stop the truncated RCT because of an ethical mandate to administer the experimental treatment. The results of this study will help guide future decisions about when to stop RCTs early for benefit.

Background: Cochrane reviews are viewed as the gold standard in meta-analyses given their efforts to identify and limit systematic error which could cause spurious conclusions. The potential for random error to cause spurious conclusions in meta-analyses is less well appreciated., Methods: We examined all reviews approved and published by the Cochrane Heart Group in the 2012 Cochrane Library that included at least one meta-analysis with 5 or more randomized trials. We used trial sequential analysis to classify statistically significant meta-analyses as true positives if their pooled sample size and/or their cumulative Z-curve crossed the O'Brien-Fleming monitoring boundaries for detecting a RRR of at least 25%. We classified meta-analyses that did not achieve statistical significance as true negatives if their pooled sample size was sufficient to reject a RRR of 25%., Results: Twenty three (41%) of the 56 meta-analyses reported statistically significant results, and 19 (83%) were true positives. Of the 33 non-statistically significant meta-analyses, 12 (36%) were true negatives. Overall, 25 (45%) of the 56 published Cochrane reviews were too small to detect/rule out an effect size of at least 25% - 12 were acknowledged as such by their authors. Of the 22 meta-analyses which were reported to be conclusive by their authors, 12 (55%) contained insufficient data to detect/rule out a 25% relative treatment effect., Conclusion: False positive and false negative meta-analyses are common but infrequently recognized, even among methodologically robust reviews published by the Cochrane Heart Group. Meta-analysts and readers should incorporate trial sequential analysis when interpreting results., (Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.)

Background: Hypertension has been cited as the most common attributable risk factor for death worldwide, and in Canada more than one of every five adults had this diagnosis in 2007. In addition to different lifestyle modifications, such as diet and exercise, there exist many pharmaco-therapies from different drug classes which can be used to lower blood pressure, thereby reducing the risk of serious clinical outcomes. In moderate and severe cases, more than one agent may be used. The optimal mono- and combination therapies for mild hypertension and moderate/severe hypertension are unclear, and clinical guidelines provide different recommendations for first line therapy. The objective of this review is to explore the relative benefits and safety of different pharmacotherapies for management of non-diabetic patients with hypertension, whether of a mild or moderate to severe nature., Methods/design: Searches involving MEDLINE and the Cochrane Database of Systematic Reviews will be used to identify related systematic reviews and relevant randomized trials. The outcomes of interest include myocardial infarction, stroke, incident diabetes, heart failure, overall and cardiovascular related death, and important side effects (cancers, depression, syncopal episodes/falls and sexual dysfunction). Randomized controlled trials will be sought. Two reviewers will independently screen relevant reviews, titles and abstracts resulting from the literature search, and also potentially relevant full-text articles in duplicate. Data will be abstracted and quality will be appraised by two team members independently. Conflicts at all levels of screening and abstraction will be resolved through team discussion. Random effect pairwise meta-analyses and network meta-analyses will be conducted where deemed appropriate. Analyses will be geared toward studying treatment of mild hypertension and moderate/severe hypertension separately., Discussion: Our systematic review results will assess the extent of currently available evidence for single agent and multi-agent pharmacotherapies in patients with mild, moderate and severe hypertension, and will provide a rigorous and updated synthesis of a range of important clinical outcomes for clinicians, decision makers and patients., Trial Registration: PROSPERO Registration Number: CRD42013004459.

Background: Central statistical monitoring in multicenter trials could allow trialists to identify centers with problematic data or conduct and intervene while the trial is still ongoing. Currently, there are few published models that can be used for this purpose., Purpose: To develop and validate a series of risk scores to identify fabricated data within a multicenter trial, to be used in central statistical monitoring., Methods: We used a database from a multicenter trial in which data from 9 of 109 centers were documented to be fabricated. These data were used to build a series of risk scores to predict fraud at centers. All analyses were performed at the level of the center. Exploratory factor analysis was used to select from 52 possible predictors, chosen from a variety of previously published methods. The final models were selected from a total of 18 independent predictors, based on the factors identified. These models were converted to risk scores for each center., Results: Five different risk scores were identified, and each had the ability to discriminate well between centers with and without fabricated data (area under the curve values ranged from 0.90 to 0.95). True- and false-positive rates are presented for each risk score to arrive at a recommended cutoff of seven or above (high risk score). We validated these risk scores, using an independent multicenter trial database that contained no data fabrication and found the occurrence of false-positive high scores to be low and comparable to the model-building data set., Limitations: These risk score have been validated only for their false-positive rate and require validation within another trial that contains centers that have fabricated data. Validation in noncardiovascular trials is also required to gage the usefulness of these risk scores in central statistical monitoring., Conclusions: With further validation, these risk scores could become part of a series of tools that provide evidence-based central statistical monitoring, which in turn can improve the efficiency of trials, and minimize the need for more expensive on-site monitoring.

Background: Multiple treatment comparison (MTC) meta-analyses are commonly modeled in a Bayesian framework, and weakly informative priors are typically preferred to mirror familiar data driven frequentist approaches. Random-effects MTCs have commonly modeled heterogeneity under the assumption that the between-trial variance for all involved treatment comparisons are equal (i.e., the 'common variance' assumption). This approach 'borrows strength' for heterogeneity estimation across treatment comparisons, and thus, ads valuable precision when data is sparse. The homogeneous variance assumption, however, is unrealistic and can severely bias variance estimates. Consequently 95% credible intervals may not retain nominal coverage, and treatment rank probabilities may become distorted. Relaxing the homogeneous variance assumption may be equally problematic due to reduced precision. To regain good precision, moderately informative variance priors or additional mathematical assumptions may be necessary., Methods: In this paper we describe four novel approaches to modeling heterogeneity variance - two novel model structures, and two approaches for use of moderately informative variance priors. We examine the relative performance of all approaches in two illustrative MTC data sets. We particularly compare between-study heterogeneity estimates and model fits, treatment effect estimates and 95% credible intervals, and treatment rank probabilities., Results: In both data sets, use of moderately informative variance priors constructed from the pair wise meta-analysis data yielded the best model fit and narrower credible intervals. Imposing consistency equations on variance estimates, assuming variances to be exchangeable, or using empirically informed variance priors also yielded good model fits and narrow credible intervals. The homogeneous variance model yielded high precision at all times, but overall inadequate estimates of between-trial variances. Lastly, treatment rankings were similar among the novel approaches, but considerably different when compared with the homogenous variance approach., Conclusions: MTC models using a homogenous variance structure appear to perform sub-optimally when between-trial variances vary between comparisons. Using informative variance priors, assuming exchangeability or imposing consistency between heterogeneity variances can all ensure sufficiently reliable and realistic heterogeneity estimation, and thus more reliable MTC inferences. All four approaches should be viable candidates for replacing or supplementing the conventional homogeneous variance MTC model, which is currently the most widely used in practice.

Background: Network meta-analysis is becoming increasingly popular for establishing comparative effectiveness among multiple interventions for the same disease. Network meta-analysis inherits all methodological challenges of standard pairwise meta-analysis, but with increased complexity due to the multitude of intervention comparisons. One issue that is now widely recognized in pairwise meta-analysis is the issue of sample size and statistical power. This issue, however, has so far only received little attention in network meta-analysis. To date, no approaches have been proposed for evaluating the adequacy of the sample size, and thus power, in a treatment network., Findings: In this article, we develop easy-to-use flexible methods for estimating the 'effective sample size' in indirect comparison meta-analysis and network meta-analysis. The effective sample size for a particular treatment comparison can be interpreted as the number of patients in a pairwise meta-analysis that would provide the same degree and strength of evidence as that which is provided in the indirect comparison or network meta-analysis. We further develop methods for retrospectively estimating the statistical power for each comparison in a network meta-analysis. We illustrate the performance of the proposed methods for estimating effective sample size and statistical power using data from a network meta-analysis on interventions for smoking cessation including over 100 trials., Conclusion: The proposed methods are easy to use and will be of high value to regulatory agencies and decision makers who must assess the strength of the evidence supporting comparative effectiveness estimates.

Background: Meta-analyses including a limited number of patients and events are prone to yield overestimated intervention effect estimates. While many assume bias is the cause of overestimation, theoretical considerations suggest that random error may be an equal or more frequent cause. The independent impact of random error on meta-analyzed intervention effects has not previously been explored. It has been suggested that surpassing the optimal information size (i.e., the required meta-analysis sample size) provides sufficient protection against overestimation due to random error, but this claim has not yet been validated., Methods: We simulated a comprehensive array of meta-analysis scenarios where no intervention effect existed (i.e., relative risk reduction (RRR) = 0%) or where a small but possibly unimportant effect existed (RRR = 10%). We constructed different scenarios by varying the control group risk, the degree of heterogeneity, and the distribution of trial sample sizes. For each scenario, we calculated the probability of observing overestimates of RRR>20% and RRR>30% for each cumulative 500 patients and 50 events. We calculated the cumulative number of patients and events required to reduce the probability of overestimation of intervention effect to 10%, 5%, and 1%. We calculated the optimal information size for each of the simulated scenarios and explored whether meta-analyses that surpassed their optimal information size had sufficient protection against overestimation of intervention effects due to random error., Results: The risk of overestimation of intervention effects was usually high when the number of patients and events was small and this risk decreased exponentially over time as the number of patients and events increased. The number of patients and events required to limit the risk of overestimation depended considerably on the underlying simulation settings. Surpassing the optimal information size generally provided sufficient protection against overestimation., Conclusions: Random errors are a frequent cause of overestimation of intervention effects in meta-analyses. Surpassing the optimal information size will provide sufficient protection against overestimation.

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.