Journal Club

Purpose & Plan

The purpose is to widen the scope for understanding papers and to provide a new networking opportunity for IBS members through a regular internet forum.

The IBS Education Committee will choose a recent paper published in the Biometrics or JABES journal. The paper may highlight an interesting application or a new methodology or development in biometrics, biostatistics or biomathematics. The papers will be chosen by the Journal Club organizer, Gen Li, in coordination with the Education Committee, and shall be of wide interest among members.

The author of the paper will be asked to make a 45-minute presentation on the paper and its importance. A discussant will also be identified to highlight the main points and raise some relevant questions for a maximum time of 15 minutes. The discussion will be opened up to participants for comments, questions and responses to the paper, under the direction of the Chair. IBS members may want to email and request an opportunity to speak about the paper, but the wider audience will be able to contribute to the discussion.

For more information about the Journal Club, contact Gen Li, Journal Club Chair or Heidi Lapka, IBO, Staff Liaison.

If you are having issues accessing member-only content please contact the International Biometric Office (IBO).

Upcoming Journal Club Session

18 September 2026 | 11:00 a.m. ET | 1500 UTC
"How to Achieve Model-robust Inference in Stepped Wedge Trials with Model-based Methods?
(Biometrics, November 2024)

Speaker: Bingkai Wang, PhD, Assistant Professor, Biostatistics, University of Michigan
Discussant: Kenneth M. Lee, PhD, Research Assistant Professor Epidemiology, University of Pennsylvania

Abstract

A stepped wedge design is an unidirectional crossover design where clusters are randomized to distinct treatment sequences. While model-based analysis of stepped wedge designs is a standard practice to evaluate treatment effects accounting for clustering and adjusting for covariates, their properties under misspecification have not been systematically explored. In this article, we focus on model-based methods, including linear mixed models and generalized estimating equations with an independence, simple exchangeable, or nested exchangeable working correlation structure. We study when a potentially misspecified working model can offer consistent estimation of the marginal treatment effect estimands, which are defined nonparametrically with potential outcomes and may be functions of calendar time and/or exposure time. We prove a central result that consistency for nonparametric estimands usually requires a correctly specified treatment effect structure, but generally not the remaining aspects of the working model (functional form of covariates, random effects, and error distribution), and valid inference is obtained via the sandwich variance estimator. Furthermore, an additional g-computation step is required to achieve model-robust inference under non-identity link functions or for ratio estimands. The theoretical results are illustrated via several simulation experiments and re-analysis of a completed stepped wedge cluster randomized trial.

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