Extending Causal Inferences From a Randomized Trial to Another Population

Abstract

The four core chapters of this dissertation discuss identification, estimation, and sensitivity analysis for studies extending, that is, generalizing or transporting, causal inferences from a randomized trial to a well-defined target population. Chapter 1: 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 non-nested trial designs (e.g., composite dataset designs), where a randomized trial is combined with a separately obtained sample of non-randomized individuals from the target population. We show that the causal quantities that can be identified in each study design depend on what is known about the probability of sampling non-randomized individuals. For each study design, we examine identification of potential 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. Chapter 2: We use counterfactual and graphical causal models to examine under what conditions we can generalize causal inferences from a randomized trial to a target population. We offer an interpretation of generalizability analyses using the notion of a hypothetical intervention to ``scale-up'' trial engagement to the target population. We consider the interpretation of generalizability analyses when trial engagement does or does not directly affect the outcome, highlight connections with censoring in longitudinal studies, and discuss identification of the distribution of counterfactual outcomes via g-formula computation and inverse probability weighting. Lastly, we show how the methods can be extended to address time-varying treatments, non-adherence, and censoring. Chapter 3: Extending (generalizing or transporting) causal inferences from a randomized trial to a target population requires ``generalizability'' or ``exchangeability'' assumptions, which state that randomized and non-randomized individuals are exchangeable conditional on baseline covariates. These assumptions are made on the basis of background knowledge, which is often uncertain or controversial, and need to be subjected to sensitivity analysis. We present simple methods for sensitivity analysis that do not require detailed background knowledge about specific unknown or unmeasured determinants of the outcome or modifiers of the treatment effect. Instead, our methods directly parameterize violations of the assumptions using bias functions. We show how the methods can be applied to non-nested trial designs, where the trial data are combined with a separately obtained sample of non-randomized individuals, as well as to nested trial designs, where a clinical trial is nested within a cohort sampled from the target population. We illustrate the methods using data from a clinical trial comparing treatments for chronic hepatitis C infection. Chapter 4: We describe a method for global sensitivity analysis that directly parameterizes violations of the assumptions in terms of potential (counterfactual) outcome distributions. Our approach does not require detailed knowledge about the distribution of specific unmeasured effect modifiers or their relationship with the observed variables. We illustrate the method using data from a randomized trial nested within a cohort study of eligible individuals to compare coronary artery surgery plus medical therapy versus medical therapy alone for chronic coronary artery disease.