Model-robust standardization in stepped wedge designs

2025-07-23

AI Summary

In stepped-wedge cluster randomized trials (SW-CRTs) with informative cluster sizes, conventional methods—such as generalized estimating equations (GEE) and linear mixed models (LMM)—yield biased causal effect estimates due to implicit weighting and restrictive modeling assumptions, and lack a unified robust framework. Method: We propose the first model-robust standardized estimation framework for SW-CRTs, extending augmented inverse probability weighting (AIPW) to this design. We formally define both individual- and cluster-level横向 and longitudinal average treatment effects (ATEs). The framework integrates parametric and semiparametric working models, is compatible with standard software, and ensures consistent estimation of multiple causal parameters under arbitrary model misspecification. Results: Simulation studies and reanalyses of real-world SW-CRT data demonstrate excellent finite-sample performance: estimates are robust, inference is valid, and causal inference quality is substantially improved in settings with heterogeneous cluster sizes.

Technology Category

• Intelligent Robots: State Estimation
• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Responsible Web: Consent frameworks and practices on the web
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Stepped-wedge cluster-randomized trials (SW-CRTs) are widely used in healthcare and implementation science, providing an ethical advantage by ensuring all clusters eventually receive the intervention. The staggered rollout of treatment introduces complexities in defining and estimating treatment effect estimands, particularly under informative sizes. Traditional model-based methods, including generalized estimating equations (GEE) and linear mixed models (LMM), produce estimates that depend on implicit weighting schemes and parametric assumptions, leading to bias for different types of estimands in the presence of informative sizes. While recent methods have attempted to provide robust estimation in SW-CRTs, they are restrictive on modeling assumptions or lack of general framework for consistent estimating multiple estimands under informative size. In this article, we propose a model-robust standardization framework for SW-CRTs that generalizes existing methods from parallel-arm CRTs. We define causal estimands including horizontal-individual, horizontal-cluster, vertical-individual, and vertical-cluster average treatment effects under a super population framework and introduce an augmented standardization estimator that standardizes parametric and semiparametric working models while maintaining robustness to informative cluster size under arbitrary misspecification. We evaluate the finite-sample properties of our proposed estimators through extensive simulation studies, assessing their performance under various SW-CRT designs. Finally, we illustrate the practical application of model-robust estimation through a reanalysis of two real-world SW-CRTs.

Problem

• Addresses bias in treatment effect estimands in SW-CRTs
• Overcomes limitations of traditional model-based methods
• Provides robust estimation under informative cluster sizes

Innovation

• Model-robust standardization for SW-CRTs
• Augmented standardization estimator for robustness
• Generalizes parallel-arm CRT methods