Extending inferences from a cluster randomized trial to a target population

TL;DR

This paper introduces methods to generalize causal inferences from cluster randomized trials to broader populations, leveraging individual data and machine learning for improved efficiency.

Contribution

It proposes doubly robust estimators for potential outcomes in the target population under a flexible nonparametric model, accommodating complex within-cluster dependence.

Findings

Applied methods to influenza vaccination trial in nursing homes

Demonstrated improved efficiency with machine learning integration

Extended inferences to a broader nursing home population

Abstract

We describe methods that extend (generalize or transport) causal inferences from cluster randomized trials to a target population of clusters, under a general nonparametric model that allows for arbitrary within-cluster dependence. We propose doubly robust estimators of potential outcome means in the target population that exploit individual-level data on covariates and outcomes to improve efficiency and are appropriate for use with machine learning methods. We illustrate the methods using a cluster randomized trial of influenza vaccination strategies conducted in 818 nursing homes nested in a cohort of 4,475 trial-eligible Medicare-certified nursing homes.