Inverse Probability Weighted Estimators of Vaccine Effects Accommodating Partial Interference and Censoring

TL;DR

This paper extends inverse probability weighted estimators to account for partial interference and censoring, enabling more accurate estimation of vaccine effects in complex real-world scenarios.

Contribution

It introduces an extension of the IPW estimator incorporating censoring via IPCW and proportional hazards frailty models, with theoretical properties and practical application.

Findings

Estimator performs well in finite samples

Method successfully applied to cholera vaccine data

Provides robust causal effect estimates under interference and censoring

Abstract

Estimating population-level effects of a vaccine is challenging because there may be interference, i.e., the outcome of one individual may depend on the vaccination status of another individual. Partial interference occurs when individuals can be partitioned into groups such that interference occurs only within groups. In the absence of interference, inverse probability weighted (IPW) estimators are commonly used to draw inference about causal effects of an exposure or treatment. Tchetgen Tchetgen and VanderWeele (2012) proposed a modified IPW estimator for causal effects in the presence of partial interference. Motivated by a cholera vaccine study in Bangladesh, this paper considers an extension of the Tchetgen Tchetgen and VanderWeele IPW estimator to the setting where the outcome is subject to right censoring using inverse probability of censoring weights (IPCW). Censoring weights are estimated using proportional hazards frailty models. The large sample properties of the IPCW estimators are derived, and simulation studies are presented demonstrating the estimators' performance in finite samples. The methods are then used to analyze data from the cholera vaccine study.