title: Inference in randomized studies with informative censoring and discrete time-to-event endpoints.
abstract: 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.
authors: Department of Biostatistics, Johns Hopkins School of Hygiene and Public Health, Baltimore, Maryland 21025, USA. dscharf@jhsph.edu
authors: D
authors: D
authors: Scharfstein
authors: J M
authors: JM
authors: Robins
authors: W
authors: W
authors: Eddings
authors: A
authors: A
authors: Rotnitzky
mesh_terms: D014150:Antipsychotic Agents / Q000627:therapeutic use; D001699:Biometry / Q000379:methods; D003198:Computer Simulation; D006801:Humans; D015233:Models, Statistical; D010919:Placebos; D011618:Psychotic Disorders / Q000188:drug therapy*; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data; D018967:Risperidone / Q000627:therapeutic use*; D012680:Sensitivity and Specificity; D013997:Time Factors; D016896:Treatment Outcome
chemical_list: D014150:Antipsychotic Agents; D010919:Placebos; D018967:Risperidone