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Inference in Randomized Studies with Informative Censoring and Discrete Time‐to‐Event Endpoints
Author(s) -
Scharfstein Daniel,
Robins James M.,
Eddings Wesley,
Rotnitzky Andrea
Publication year - 2001
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.0006-341x.2001.00404.x
Subject(s) - censoring (clinical trials) , inference , computer science , statistics , event (particle physics) , econometrics , mathematics , artificial intelligence , physics , quantum mechanics
Summary. In this article, we present a method for estimating and comparing the treatment‐specific distributions of a discrete time‐to‐event variable from right‐censored data. Our method allows for (1) adjustment for informative censoring due to measured prognostic factors for time to event and censoring and (2) quantification of the sensitivity of the inference to residual dependence between time to event and censoring due to unmeasured factors. We develop our approach in the context of a randomized trial for the treatment of chronic schizophrenia. We perform a simulation study to assess the practical performance of our methodology.