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Randomization inference with general interference and censoring

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

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.

Suggested Citation

  • Wen Wei Loh & Michael G. Hudgens & John D. Clemens & Mohammad Ali & Michael E. Emch, 2020. "Randomization inference with general interference and censoring," Biometrics, The International Biometric Society, vol. 76(1), pages 235-245, March.
  • Handle: RePEc:bla:biomet:v:76:y:2020:i:1:p:235-245
    DOI: 10.1111/biom.13125
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    Cited by:

    1. Shaina J. Alexandria & Michael G. Hudgens & Allison E. Aiello, 2023. "Assessing intervention effects in a randomized trial within a social network," Biometrics, The International Biometric Society, vol. 79(2), pages 1409-1419, June.
    2. Joseph Puleo & Ashley Buchanan & Natallia Katenka & M. Elizabeth Halloran & Samuel R. Friedman & Georgios Nikolopoulos, 2024. "Assessing Spillover Effects of Medications for Opioid Use Disorder on HIV Risk Behaviors among a Network of People Who Inject Drugs," Stats, MDPI, vol. 7(2), pages 1-27, June.
    3. Sujatro Chakladar & Samuel Rosin & Michael G. Hudgens & M. Elizabeth Halloran & John D. Clemens & Mohammad Ali & Michael E. Emch, 2022. "Inverse probability weighted estimators of vaccine effects accommodating partial interference and censoring," Biometrics, The International Biometric Society, vol. 78(2), pages 777-788, June.

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