The meta-analytic-predictive (MAP) approach is a Bayesian method to incorporate historical controls in new trials that aims to increase the statistical power and reduce the required sample size. Here we investigate how to calculate the sample size of the new trial when historical data is available, and the MAP approach is used in the analysis. In…

Rare events meta-analyses of randomized controlled trials (RCTs) are often underpowered because the outcomes are infrequent. Real-world evidence (RWE) from non-randomized studies may provide valuable complementary evidence about the effects of rare events, and there is growing interest in including such evidence in the decision-making process.…

Multi-site randomized controlled trials (RCTs) provide unbiased estimates of the average impact in the study sample. However, their ability to accurately predict the impact for individual sites outside the study sample, to inform local policy decisions, is largely unknown. To extend prior research on this question, we analyzed six multi-site RCTs…

Background: Power analysis and sample size planning are key components in designing cluster randomized trials (CRTs), a common study design to test treatment effect by randomizing clusters or groups of individuals. Sample size determination in two-level CRTs requires knowledge of more than one design parameter, such as the effect size and the…

Educational researchers advocate the use of an effect size and its confidence interval to assess the effectiveness of interventions instead of relying on a p-value, which has been blamed for lack of reproducibility of research findings and the misuse of statistics. The aim of this study is to provide a framework, which can provide direct evidence…

It is now widely accepted that the standard inferential toolkit used by the scientific research community--null-hypothesis significance testing (NHST)--is not fit for purpose. Yet despite the threat posed to the scientific enterprise, there is no agreement concerning alternative approaches for evidence assessment. This lack of consensus reflects…

Network meta-analysis is a popular method to synthesize the information obtained in a systematic review of studies (e.g., randomized clinical trials) involving subsets of multiple treatments of interest. The dominant method of analysis employs within-study information on treatment contrasts and integrates this over a network of studies. One…

For binary experimental data, we discuss randomization-based inferential procedures that do not need to invoke any modeling assumptions. We also introduce methods for likelihood and Bayesian inference based solely on the physical randomization without any hypothetical super population assumptions about the potential outcomes. These estimators have…

One important issue in Bayesian estimation is the determination of an effective informative prior. In hierarchical Bayes models, the uncertainty of hyperparameters in a prior can be further modeled via their own priors, namely, hyper priors. This study introduces a framework to construct hyper priors for both the mean and the variance…

In this response, we first show that Simpson's proposed analysis answers a different and less interesting question than ours. We then justify the choice of prior for our Bayes factors calculations, but we also demonstrate that the substantive conclusions of our article are not substantially affected by varying this choice.

Network meta-analysis is becoming a common approach to combine direct and indirect comparisons of several treatment arms. In recent research, there have been various developments and extensions of the standard methodology. Simultaneously, cluster randomized trials are experiencing an increased popularity, especially in the field of health services…

A recent paper uses Bayes factors to argue a large minority of rigorous, large-scale education RCTs are "uninformative." The definition of "uninformative" depends on the authors' hypothesis choices for calculating Bayes factors. These arguably overadjust for effect size inflation and involve a fixed prior distribution,…

In the coming years, public programs will capture even more and richer data than they do now, including data from web-based tools used by participants in employment services, from tablet-based educational curricula, and from electronic health records for Medicaid beneficiaries. Program evaluators seeking to take full advantage of these data…

Randomized controlled trials (RCTs) admit unconfounded design-based inference--randomization largely justifies the assumptions underlying statistical effect estimates--but often have limited sample sizes. However, researchers may have access to big observational data on covariates and outcomes from RCT non-participants. For example, data from A/B…

Educational stakeholders are keen to know the magnitude and importance of different interventions. However, the way evidence is communicated to support understanding of the effectiveness of an intervention is controversial. Typically studies in education have used the standardised mean difference as a measure of the impact of interventions. This…