Evidence map›Paper›PMID 40990530›Full record

ArticleStatistics in medicine2025

Analysis of Stepped-Wedge Cluster Randomized Trials When Treatment Effects Vary by Exposure Time or Calendar Time.

Kenneth M Lee, Elizabeth L Turner, Avi Kenny

Abstract read
In one paragraph

Article in Statistics in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Article
  2. Article
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  4. Article
  5. Model-robust standardization in stepped wedge cluster randomized trials.Journal of the Royal Statistical Society. Series A, (Statistics in Society) · 2026
    Article
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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.

Kenneth M LeeDepartment of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.ORCID https://orcid.org/0000-0002-0454-4537
Elizabeth L TurnerDepartment of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina, USA.
Avi KennyDepartment of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina, USA.

Funding

North STAR Trial: Specialty Telemedicine Access for Referrals in Rural AlaskaR01DC020026 · NIDCD · UNIV OF ARKANSAS FOR MED SCIS · PI EMMETT, SUSAN DAVIS, HIRSCHFELD, MATTHEW J · 2021 to 2025
$3.3M
Appalachian STAR TrialU01OD033247 · OD · UNIV OF ARKANSAS FOR MED SCIS · PI BUSH, MATTHEW LEE, EMMETT, SUSAN DAVIS · 2021 to 2021
$1.8M
NIDCD NIH HHS R01 DC020026NIDCD NIH HHS R01-DC020026NIDCR NIH HHS U01-OD033247NIH HHS U01 OD033247
6 · The paper itself

Abstract

Stepped-wedge cluster randomized trials (SW-CRTs) are traditionally analyzed with models that assume an immediate and sustained treatment effect. Previous work has shown that making such an assumption in the analysis of SW-CRTs when the true underlying treatment effect varies by exposure time can produce severely misleading estimates. Alternatively, the true underlying treatment effect might vary by calendar time. Comparatively less work has examined treatment effect structure misspecification in this setting. Here, we evaluate the behavior of the linear mixed effects model-based immediate treatment effect, exposure time-averaged treatment effect, and calendar time-averaged treatment effect estimators in different scenarios where these estimators are misspecified for the true underlying treatment effect structure. We show that the immediate treatment effect estimator is relatively robust to bias when estimating a true underlying calendar time-averaged treatment effect estimand. However, when there is a true underlying calendar (exposure) time-varying treatment effect, misspecifying an analysis with an exposure (calendar) time-averaged treatment effect estimator can yield severely misleading estimates which may converge to a value with the opposite sign of the true calendar (exposure) time-averaged treatment effect estimand. In this article, we highlight these two different time scales on which treatment effects can vary in SW-CRTs and clarify potential vulnerabilities that may arise when considering different types of time-varying treatment effects in a SW design. Accordingly, we emphasize the need for researchers to carefully consider whether the treatment effect may vary as a function of exposure time or calendar time in the analysis of SW-CRTs.

Indexed as

Linear ModelsRandomized Controlled Trials as TopicTime FactorsComputer SimulationData Interpretation, StatisticalHumansTreatment Outcomecalendar timeestimandsexposure timemodel misspecificationstepped‐wedge cluster randomized trialtime‐varying treatment effects

Identifiers

PMID40990530
PMCPMC12459233

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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 OpenQuestion graph.