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Randomization inference with general interference and censoring

(2020) BIOMETRICS. 76(1). p.235-245
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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.
Keywords
causal inference, censoring, interference, permutation test, randomization inference, spillover effects, oral cholera vaccines, causal inference, Bangladesh

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Please use this url to cite or link to this publication:

MLA
Loh, Wen Wei, et al. “Randomization Inference with General Interference and Censoring.” BIOMETRICS, vol. 76, no. 1, 2020, pp. 235–45.
APA
Loh, W. W., Hudgens, M. G., Clemens, J. D., Ali, M., & Emch, M. E. (2020). Randomization inference with general interference and censoring. BIOMETRICS, 76(1), 235–245.
Chicago author-date
Loh, Wen Wei, Michael G. Hudgens, John D. Clemens, Mohammad Ali, and Michael E. Emch. 2020. “Randomization Inference with General Interference and Censoring.” BIOMETRICS 76 (1): 235–45.
Chicago author-date (all authors)
Loh, Wen Wei, Michael G. Hudgens, John D. Clemens, Mohammad Ali, and Michael E. Emch. 2020. “Randomization Inference with General Interference and Censoring.” BIOMETRICS 76 (1): 235–245.
Vancouver
1.
Loh WW, Hudgens MG, Clemens JD, Ali M, Emch ME. Randomization inference with general interference and censoring. BIOMETRICS. 2020;76(1):235–45.
IEEE
[1]
W. W. Loh, M. G. Hudgens, J. D. Clemens, M. Ali, and M. E. Emch, “Randomization inference with general interference and censoring,” BIOMETRICS, vol. 76, no. 1, pp. 235–245, 2020.
@article{8634218,
  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.},
  author       = {Loh, Wen Wei and Hudgens, Michael G. and Clemens, John D. and Ali, Mohammad and Emch, Michael E.},
  issn         = {0006-341X},
  journal      = {BIOMETRICS},
  keywords     = {causal inference,censoring,interference,permutation test,randomization inference,spillover effects,oral cholera vaccines,causal inference,Bangladesh},
  language     = {eng},
  number       = {1},
  pages        = {235--245},
  title        = {Randomization inference with general interference and censoring},
  url          = {http://dx.doi.org/10.1111/biom.13125},
  volume       = {76},
  year         = {2020},
}

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