Randomization inference with general interference and censoring

Wen Wei Loh, Michael G. Hudgens, John D. Clemens, Mohammad Ali, Michael E. Emch

Research output: Contribution to journalArticle

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 it is a priori assumed that 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 et al. (2012, Political Anal, 21, 97–124) and Bowers et al. (2016, Political Anal, 24, 395–403) 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. that 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 et al. (2010, Biostatistics, 11, 676–692) 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.

Original languageEnglish (US)
JournalBiometrics
DOIs
StateAccepted/In press - Jan 1 2019

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Keywords

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

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Agricultural and Biological Sciences(all)
  • Applied Mathematics

Cite this

Loh, W. W., Hudgens, M. G., Clemens, J. D., Ali, M., & Emch, M. E. (Accepted/In press). Randomization inference with general interference and censoring. Biometrics. https://doi.org/10.1111/biom.13125