Randomization inference with general interference and censoring.

Biometrics
Wen Wei LohMichael 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 a...Continue Reading

References

Jul 1, 1988·The Journal of Infectious Diseases·J D ClemensM R Khan
Dec 17, 2008·Journal of the American Statistical Association·Michael G Hudgens, M Elizabeth Halloran
Jan 13, 2009·Social Science & Medicine·Michael EmchMohammad Yunus
Mar 25, 2014·Journal of the American Statistical Association·Lan Liu, Michael G Hudgens
May 23, 2014·Biometrics·Carolina Perez-HeydrichMichael E Emch
Jan 31, 2017·Current Epidemiology Reports·M Elizabeth Halloran, Michael G Hudgens

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