Randomization inference with general interference and censoring

TL;DR

This paper extends randomization inference methods to handle general interference and right-censored outcomes, enabling more accurate causal analysis in complex experimental settings.

Contribution

It introduces a novel extension of existing randomization-based tests to accommodate general interference structures with censored survival data.

Findings

Methods successfully applied to cholera vaccine trial data

Simulation studies demonstrate robustness of the approach

Extension allows for valid inference under complex interference and censoring

Abstract

Interference occurs between individuals when the treatment (or exposure) of one individual affects the outcome of another individual. Previous work on causal inference methods in the presence of interference has focused on the setting where a priori it is assumed there is 'partial interference,' in the sense that individuals can be partitioned into groups wherein there is no interference between individuals in different groups. Bowers, Fredrickson, and Panagopoulos (2012) and Bowers, Fredrickson, and Aronow (2016) consider randomization-based inferential methods that allow for more general interference structures in the context of randomized experiments. In this paper, extensions of Bowers et al. which allow for failure time outcomes subject to right censoring are proposed. Permitting right censored outcomes is challenging because standard randomization-based tests of the null hypothesis of no treatment effect assume that whether an individual is censored does not depend on treatment. The proposed extension of Bowers et al. to allow for censoring entails adapting the method of Wang, Lagakos, and Gray (2010) for two sample survival comparisons in the presence of unequal censoring. The methods are examined via simulation studies and utilized to assess the effects of cholera vaccination in an individually-randomized trial of 73,000 children and women in Matlab, Bangladesh.