Related Experiment Video
Updated: May 31, 2026
08:36
The Adjuvant Efficacy of Angong Niuhuang Pill in the Treatment of Viral Encephalitis: A Meta-Analysis of Randomized Controlled Trials
Published on: April 19, 2024
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
Summary
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.
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.
Related Concept Videos
01:15Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs
Bioequivalence experimental study designs play a pivotal role in testing the effectiveness of various treatments. Key among these are the repeated measures, cross-over, carry-over, and Latin square designs. In the repeated measures design, each subject receives all treatments, allowing for temporal comparisons. This type of design is useful in reducing variability but requires careful planning to avoid bias.The cross-over design, an economical method, involves sequential administration of...
01:20Bioequivalence Experimental Study Designs: Completely Randomized and Randomized Block Designs
Bioequivalence experimental study designs are crucial methodologies used in evaluating and comparing the bioavailability of different drug products. These designs are categorized into various types: completely randomized, randomized block, repeated measures, cross and carry-over, and Latin square designs.Completely randomized designs involve randomly allocating treatments to all subjects participating in the experiment. This allocation is achieved by assigning unique random numbers to subjects...
01:13Randomized Experiments
The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
Simple randomization
Simple...
01:16Crossover Experiments
Crossover experiments, also called the repeated-measurements design, is a study design in which all experimental units are exposed to all treatments in different periods. Crossover experiments are generally used in psychology, the pharmaceutical industry, agriculture, and medicine.
Crossover designs are performed even with smaller sample sizes since the samples can act as their controls. These are better than simple randomized trials since patients are exposed to all the treatments.
Crossover designs are performed even with smaller sample sizes since the samples can act as their controls. These are better than simple randomized trials since patients are exposed to all the treatments.
01:20Study Designs in Epidemiology
Epidemiological study designs are fundamental tools for investigating the distribution, determinants, and control of health conditions in populations. They help researchers understand the relationships between exposures and outcomes, and they broadly fall into two categories: "observational" and "experimental" studies.
Observational studies are those where the researcher does not intervene but rather observes natural variations. They include cross-sectional, cohort, and case-control studies.
Observational studies are those where the researcher does not intervene but rather observes natural variations. They include cross-sectional, cohort, and case-control studies.
01:20Comparing the Survival Analysis of Two or More Groups
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and Cox...