Guidance for Addressing Individual Time Effects in Cohort Stepped Wedge Cluster Randomized Trials: A Simulation Study

2026-01-19

AI Summary

This study addresses the potential bias in estimating intervention effects in stepped wedge cluster randomized trials when individuals experience time-varying changes, such as aging. It presents the first systematic evaluation of how such temporal effects influence inference, using Monte Carlo simulations to compare the performance of four linear mixed models under both closed and open cohort designs. The authors propose a modeling strategy that incorporates fixed categorical time effects alongside dual random intercepts—accounting for both clusters and individuals—and advocate for the use of cluster-robust variance estimation (CRVE). Results demonstrate that this approach yields unbiased estimates of the intervention effect, while CRVE effectively controls Type I error rates, maintaining robustness even when time effects are nonlinear.

Technology Category

• Intelligent Robots: State Estimation
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior
• Planning, Routing, and Scheduling: Temporal Planning

Application Category

• User Modeling, Personalization and Recommendation: Studies of user behavior, including longitudinal effects of personalized systems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Models for Web evolution

Abstract

Background: Stepped wedge cluster randomized trials (SW-CRTs) involve sequential measurements within clusters over time. Initially, all clusters start in the control condition before crossing over to the intervention on a staggered schedule. In cohort designs, secular trends, cluster-level changes, and individual-level changes (e.g., aging) must be considered. Methods: We performed a Monte Carlo simulation to analyze the influence of different time effects on the estimation of the intervention effect in cohort SW-CRTs. We compared four linear mixed models with different adjustment strategies, all including random intercepts for clustering and repeated measurements. We recorded the estimated fixed intervention effects and their corresponding model-based standard errors, derived from models both without and with cluster-robust variance estimators (CRVEs). Results: Models incorporating fixed categorical time effects, a fixed intervention effect, and two random intercepts provided unbiased estimates of the intervention effect in both closed and open cohort SW-CRTs. Fixed categorical time effects captured temporal cohort changes, while random individual effects accounted for baseline differences. However, these differences can cause large, non-normally distributed random individual effects. CRVEs provide reliable standard errors for the intervention effect, controlling the Type I error rate. Conclusions: Our simulation study is the first to assess individual-level changes over time in cohort SW-CRTs. Linear mixed models incorporating fixed categorical time effects and random cluster and individual effects yield unbiased intervention effect estimates. However, cluster-robust variance estimation is necessary when time-varying independent variables exhibit nonlinear effects. We recommend always using CRVEs.

Problem

• stepped wedge cluster randomized trials
• cohort design
• time effects
• individual-level changes
• intervention effect estimation

Innovation

• stepped wedge cluster randomized trial
• cohort design
• individual time effects
• cluster-robust variance estimator
• linear mixed model