Platform trials enable the evaluation of multiple investigational drugs for a single disease and offer flexibility in adding or dropping treatments during the trial. This design would be advantageous...

In this anniversary issue I briefly review some work on the notion of collapsibility and indicate some lingering questions.

Mendelian randomization (MR) has become an essential tool for causal inference in biomedical and public health research. By using genetic variants as instrumental variables, MR helps address unmeasur...

In the analyses of cluster-randomized trials, mixed-model analysis of covariance (ANCOVA) is a standard approach for covariate adjustment and handling within-cluster correlations. However, when the normality, linearity, or the random-intercept assumption is violated, the validity and efficiency of the mixed-model ANCOVA estimators for estimating the average treatment effect remain unclear. Under the potential outcomes framework, we prove that the mixed-model ANCOVA estimators for the average treatment effect are consistent and asymptotically normal under arbitrary misspecification of its working model. If the probability of receiving treatment is 0.5 for each cluster, we further show that the model-based variance estimator under mixed-model ANCOVA1 (ANCOVA without treatment-covariate interactions) remains consistent, clarifying that the confidence interval given by standard software is asymptotically valid even under model misspecification. Beyond robustness, we discuss several insights on precision among classical methods for analyzing cluster-randomized trials, including the mixed-model ANCOVA, individual-level ANCOVA, and cluster-level ANCOVA estimators. These insights may inform the choice of methods in practice. Our analytical results and insights are illustrated via simulation studies and analyses of three cluster-randomized trials.

On August 20, 2025, GPT-5,was reported to have solved an open problem in convex optimization. Motivated by this episode, we conducted a controlled exp…

This draft guidance provides guidance to sponsors and applicants on the appropriate use of Bayesian methods in clinical trials.

Abstract. Popular deterministic approximations of posterior distributions from, e.g. the Laplace method, variational Bayes and expectation-propagation, gen

This cross-sectional study evaluates how directed acyclic graphs are constructed, reported, and applied for statistical adjustment across leading clinical journals.

The target trial framework enables causal inference from longitudinal observational data by emulating randomized trials initiated at multiple time points. Precision is often improved by pooling inform...

Assessing the properties of the prediction interval in random-effects meta-analysis

Causal effect estimation has gained much attention in recent years, and it is not uncommon to see graduate students incorporate these analyses into their research. However, students may not be aware ...

ABSTRACT. The treatment assignment mechanism in a randomized clinical trial can be optimized for statistical efficiency within a specified class of randomi

Published in Journal of the American Statistical Association (Vol. , No. , )

In this article, we develop an online Bayesian inferential method for the Cox proportional hazards model with right-censored data. The proposed method is designed to analyze datasets where mini-bat...

Philosophies, Puzzles and Paradoxes: A Statistician Search for Truth explores the three major approaches to statistical thinking—Bayesian, Frequentist, and

This book gives guidance on implementing the estimand framework in diverse trial settings and explores real-world case studies.

Objective To characterise the reporting practices of sequential multiple assignment randomised trials (SMARTs) in human health research. Design Scoping review of protocol and primary analysis papers ...

Optimal dynamic treatment regimes (DTRs), as a key part of precision medicine, have progressively gained more attention recently. To inform clinical decision making, interpretable and parsimonious mod...

Subscription and open access journals from Sage, the world's leading independent academic publisher.

Inverse probability of treatment weighting (IPTW) is a common approach to infer causal treatment effects when covariates are imbalanced at baseline or over time among treatment groups. One limitati...

The hazards of using hazard ratios from proportional hazard models in indirect treatment comparisons

In a classical testing problem, statistical hypotheses are defined before observing the data. However, this principle is violated when hypotheses are based on a clustering of the observations, leadin...