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  1. Home
  3. Time-varying Treatment Effect Models In Stepped-wedge Cluster-randomized Trials With Multiple Interventions.
  1. Home
  3. Time-varying Treatment Effect Models In Stepped-wedge Cluster-randomized Trials With Multiple Interventions.

Related Experiment Video

The Adjuvant Efficacy of Angong Niuhuang Pill in the Treatment of Viral Encephalitis: A Meta-Analysis of Randomized Controlled Trials
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Time-Varying Treatment Effect Models in Stepped-Wedge Cluster-Randomized Trials With Multiple Interventions.

Zhe Chen1, Wei Wang2, Yingying Lu2

  • 1Center for Clinical Trials Innovation, Department of Biostatistics, Epidemiology, Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.

Statistics in Medicine
|May 29, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

When treatment effects change over time in stepped-wedge cluster-randomized trials, the standard constant effect model can be biased. New models accounting for time-varying effects are crucial for accurate estimation.

Keywords:
linear mixed effects modelsmodel misspecificationmultiarm randomized trialstepped‐wedge cluster‐randomized trialstime‐varying treatment effect

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Area of Science:

  • Clinical Trials Methodology
  • Biostatistics
  • Epidemiology

Background:

  • Stepped-wedge cluster-randomized trials (SW-CRTs) traditionally assume homogeneous treatment effects over time.
  • This assumption can lead to biased estimation if true treatment effects vary across time periods.
  • Multiple interventions and time-varying effects present challenges for standard SW-CRT models.

Purpose of the Study:

  • To derive the expected value of the constant effect estimator in SW-CRTs with time-varying treatment effects.
  • To evaluate the performance of standard and time-varying effect models under different SW-CRT designs.
  • To assess the impact of ignoring time heterogeneity on treatment effect estimation and inference.

Main Methods:

  • Derivation of the expected value of the constant effect estimator under exchangeable within-cluster correlation.
  • Application to concurrent and factorial SW-CRT designs with multiple interventions.
  • Extensive simulation studies comparing constant effect and time-varying effect models.
  • Analysis of the Prognosticating Outcomes and Nudging Decisions in the Electronic Health Record (PONDER) trial data.

    • Main Results:

    • The constant effect estimator converges to a weighted average of time-specific effects, with potentially non-intuitive weights.
    • Ignoring time heterogeneity leads to biased estimation and inadequate coverage for the average treatment effect.
    • Time-varying fixed effect models showed comparable power for concurrent and factorial designs across various effect curve shapes.
    • The PONDER trial data analysis indicated no significant treatment effect for either intervention across all models, but with differing precision.

    Conclusions:

    • Standard constant effect models are inadequate for SW-CRTs when treatment effects vary over time.
    • Models accommodating time-varying effects are necessary for unbiased estimation and reliable inference in complex SW-CRT designs.
    • The choice of model impacts the precision of treatment effect estimates, even when no significant effect is detected.