Study Designs for Extending Causal Inferences From a Randomized Trial to a Target Population

Abstract

In this article, we examine study designs for extending (generalizing or transporting) causal inferences from a randomized trial to a target population. Specifically, we consider nested trial designs, where randomized individuals are nested within a sample from the target population, and nonnested trial designs, including composite data-set designs, where observations from a randomized trial are combined with those from a separately obtained sample of nonrandomized individuals from the target population. We show that the counterfactual quantities that can be identified in each study design depend on what is known about the probability of sampling nonrandomized individuals. For each study design, we examine identification of counterfactual outcome means via the g-formula and inverse probability weighting. Last, we explore the implications of the sampling properties underlying the designs for the identification and estimation of the probability of trial participation.

Keywords: causal inference; generalizability; randomized trials; transportability.

Figure 1

Sampling designs for studies extending inferences from a randomized trial to a target population. For detailed descriptions of notation, see the main text. Briefly, X denotes baseline covariates, S the trial participation indicator, and D the indicator for sampling into the study sample. The dashed box around D = 0 denotes the fact that covariate data may not be available from this subset and that in nonnested trial designs the size of the subset may be unknown.