Sensitivity analyses for average treatment effects when outcome is censored by death in instrumental variable models

TL;DR

This paper develops a novel sensitivity analysis method for estimating the local average treatment effect in observational studies with censored outcomes due to death, addressing unmeasured confounding and censoring issues.

Contribution

It introduces a new instrumental variable-based sensitivity analysis approach for LATE with censored outcomes, including identification results and improved estimation procedures.

Findings

The two-step procedure is more robust and efficient than the three-step method.

Simulation studies demonstrate the effectiveness of the proposed methods.

Application to neonatal care data illustrates practical utility.

Abstract

Two problems that arise in making causal inferences for non-mortality outcomes such as bronchopulmonary dysplasia (BPD) are unmeasured confounding and censoring by death, i.e., the outcome is only observed when subjects survive. In randomized experiments with noncompliance, instrumental variable methods can be used to control for the unmeasured confounding without censoring by death. But when there is censoring by death, the average causal treatment effect cannot be identified under usual assumptions, but can be studied for a specific subpopulation by using sensitivity analysis with additional assumptions. However, in observational studies, evaluation of the local average treatment effect (LATE) in censoring by death problems with unmeasured confounding is not well studied. We develop a novel sensitivity analysis method based on instrumental variable models for studying the LATE. Specifically, we present the identification results under an additional assumption, and propose a three-step procedure for the LATE estimation. Also, we propose an improved two-step procedure by simultaneously estimating the instrument propensity score (i.e., the probability of instrument given covariates) and the parameters induced by the assumption. We have shown with simulation studies that the two-step procedure can be more robust and efficient than the three-step procedure. Finally, we apply our sensitivity analysis methods to a study of the effect of delivery at high-level neonatal intensive care units on the risk of BPD.