Semiparametric Inference of the Complier Average Causal Effect with Nonignorable Missing Outcomes

TL;DR

This paper develops a semiparametric method to estimate the causal effect of treatment among compliers in randomized trials with nonignorable missing continuous outcomes, avoiding parametric assumptions about the missing data mechanism.

Contribution

It introduces a two-step maximum likelihood estimator for nonignorable missing data that does not require specifying a parametric missing data model.

Findings

Estimator performs well in simulations

Method is robust to misspecification of the missing data mechanism

Application demonstrates practical utility in clinical trial data

Abstract

Noncompliance and missing data often occur in randomized trials, which complicate the inference of causal effects. When both noncompliance and missing data are present, previous papers proposed moment and maximum likelihood estimators for binary and normally distributed continuous outcomes under the latent ignorable missing data mechanism. However, the latent ignorable missing data mechanism may be violated in practice, because the missing data mechanism may depend directly on the missing outcome itself. Under noncompliance and an outcome-dependent nonignorable missing data mechanism, previous studies showed the identifiability of complier average causal effect for discrete outcomes. In this paper, we study the semiparametric identifiability and estimation of complier average causal effect in randomized clinical trials with both all-or-none noncompliance and the outcome-dependent nonignorable missing continuous outcomes, and propose a two-step maximum likelihood estimator in order to eliminate the infinite dimensional nuisance parameter. Our method does not need to specify a parametric form for the missing data mechanism. We also evaluate the finite sample property of our method via extensive simulation studies and sensitivity analysis, with an application to a double-blinded psychiatric clinical trial.