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Biometrics. 2019 Aug 7. doi: 10.1111/biom.13125. [Epub ahead of print]

Randomization inference with general interference and censoring.

Author information

1
Department of Data Analysis, Ghent University, Gent, Belgium.
2
Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina, USA.
3
Department of Epidemiology, University of California, Los Angeles, California, USA.
4
Department of International Health, Johns Hopkins University, Baltimore, Maryland, USA.
5
Department of Geography, University of North Carolina, Chapel Hill, North Carolina, USA.

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. This article is protected by copyright. All rights reserved.

KEYWORDS:

causal inference; censoring; interference; permutation test; randomization inference; spillover effects

PMID:
31388990
DOI:
10.1111/biom.13125

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