Generalizing trial findings using nested trial designs with sub-sampling of non-randomized individuals

TL;DR

This paper introduces a nested trial design with sub-sampling of non-randomized individuals to efficiently generalize trial findings to a broader population, proposing a doubly robust estimator and evaluating its performance.

Contribution

It presents a novel two-stage nested trial design with sub-sampling, along with a doubly robust estimator for the target population mean, enhancing research efficiency.

Findings

The proposed estimator is consistent if either the outcome model or the participation model is correct.

Sub-sampling affects the asymptotic variance of the estimator.

Simulation studies demonstrate the estimator's finite-sample performance.

Abstract

To generalize inferences from a randomized trial to the target population of all trial-eligible individuals, investigators can use nested trial designs, where the randomized individuals are nested within a cohort of trial-eligible individuals, including those who are not offered or refuse randomization. In these designs, data on baseline covariates are collected from the entire cohort, and treatment and outcome data need only be collected from randomized individuals. In this paper, we describe nested trial designs that improve research economy by collecting additional baseline covariate data after sub-sampling non-randomized individuals (i.e., a two-stage design), using sampling probabilities that may depend on the initial set of baseline covariates available from all individuals in the cohort. We propose an estimator for the potential outcome mean in the target population of all trial-eligible individuals and show that our estimator is doubly robust, in the sense that it is consistent when either the model for the conditional outcome mean among randomized individuals or the model for the probability of trial participation is correctly specified. We assess the impact of sub-sampling on the asymptotic variance of our estimator and examine the estimator's finite-sample performance in a simulation study. We illustrate the methods using data from the Coronary Artery Surgery Study (CASS).