Inference in randomized studies with informative censoring and discrete time-to-event endpoints

Daniel Scharfstein, James M. Robins, Wesley Eddings, Andrea Rotnitzky

Research output: Contribution to journalArticlepeer-review

36 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)404-413
Number of pages10
Issue number2
StatePublished - Jun 2001
Externally publishedYes


  • Coarsening at random
  • Competing risks
  • Curse of dimensionality
  • Inverse probability of censoring weighted estimation
  • Kaplan-Meier estimator
  • Sequential ignorability of censoring

ASJC Scopus subject areas

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


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