Evidence map›Paper›PMID 39499239›Full record

ArticleBiometrics2024

How to achieve model-robust inference in stepped wedge trials with model-based methods?

Bingkai Wang, Xueqi Wang, Fan Li

Abstract read
In one paragraph

Article in Biometrics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

14 citing papers in PubMed.

  1. Trial
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  3. Model-robust standardization in stepped wedge cluster randomized trials.Journal of the Royal Statistical Society. Series A, (Statistics in Society) · 2026
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  14. Demystifying estimands in cluster-randomised trials.Statistical methods in medical research · 2024
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Bingkai WangDepartment of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI 48109, United States.ORCID 0000-0002-9349-2336
Xueqi WangDepartment of Biostatistics, Yale School of Public Health, New Haven, CT 06520, United States.
Fan LiDepartment of Biostatistics, Yale School of Public Health, New Haven, CT 06520, United States.ORCID 0000-0001-6183-1893

Funding

Training Core (I)U54AG063546 · NIA · BROWN UNIVERSITY · PI Abraham Aizer Brody, SUSAN L MITCHELL · 2019 to 2026
$125.9M
Yale Clinical and Translational Science Award (U Component)UL1TR001863 · NCATS · YALE UNIVERSITY · PI John H. Krystal, LUCILA OHNO-MACHADO · 2016 to 2026
$102.9M
Improving the design and statistical analysis of cluster-randomized trials on tropical infectious diseasesR00AI173395 · NIAID · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI WANG, BINGKAI · 2024 to 2025
$497k
National Institute of Allergy and Infectious DiseasesNCATS NIH HHS UL1 TR001863NIAID NIH HHS R00 AI173395NIA NIH HHS U54 AG063546NIH HHS R00AI173395Patient-Centered Outcomes Research Institute ME-2020C3-21072
6 · The paper itself

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.

Indexed as

Models, StatisticalBiometryComputer SimulationCross-Over StudiesData Interpretation, StatisticalHumansLinear ModelsRandomized Controlled Trials as TopicResearch Designcausal inferencecluster randomized trialcovariate adjustmentestimandsmodel misspecificationtime-varying treatment effect

Identifiers

PMID39499239
PMCPMC11536888

What Socratic holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.