Emulating Stepped-Wedge Cluster Randomized Trials to Evaluate Health Policies and Interventions

TL;DR

This paper advocates for emulating stepped-wedge cluster randomized trials within the target trial framework to improve design, reporting, and causal inference in health policy evaluations.

Contribution

It introduces a conceptual framework and standards for emulating stepped-wedge trials, enhancing clarity and cross-disciplinary insights in observational health policy research.

Findings

Encourages consideration of policy heterogeneity and time effects.

Highlights the benefits of trial emulation for study design.

Identifies settings unsuitable for existing methods.

Abstract

Both cluster randomized trials and quasi-experimental designs are used to evaluate the impact of health and social policies and interventions. Stepped-wedge cluster randomized trials randomize a staggered adoption approach, while recent difference-in-differences methods allow analysis of non-randomized settings where similar policies are adopted at different time points. These approaches have become common, but the sheer variety of methods for analyzing observational studies with staggered adoption makes it challenging to clearly design and report such studies. We propose that observational and quasi-experimental study investigators can address these challenges by emulating stepped-wedge cluster randomized trials in the target trial emulation framework. The conceptual framework and reporting standards of trial emulation will encourage consideration of key features of these designs, such as policy heterogeneity and time-varying effects, and clear reporting of the estimand and assumptions. It also highlights areas where those interested in randomized trials and quasi-experimental designs can benefit from one another's experience by bringing insights across disciplines. Questions of treatment effect heterogeneity, power, spillovers, and anticipation effects, among others, are common to both fields and can benefit from cross-pollination. This article also demonstrates how trial emulation can identify settings that are not well-served by either approach, thereby avoiding studies unlikely to generate high-quality causal evidence. Finally, it informs the bias-variance-generalizability trade-off that arises with design and analysis choices made in these settings, supporting better evidence generation and interpretation in settings where important questions can be answered.