Inference for probability of selection with dependently truncated data using a Cox model |
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| Authors: | Xu Zhang Ji Li Yang Liu |
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| Affiliation: | 1. Center of Biostatistics and Bioinformatics, Cancer Institute, University of Mississippi Medical Center, Jackson, Mississippi, USA;2. Department of Biostatistics and Epidemiology, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, USA;3. Division of Analysis, Research, and Practice Integration, National Center for Injury Prevention and Control, U.S. Centers for Disease Control and Prevention, Atlanta, Georgia, USA |
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| Abstract: | A truncated sample consists of realizations of two variables L and T subject to the constraint L < T. One simple solution to dependently truncated data is to take L as a covariate of T in the Cox model. We aimed at studying the probability of selection, P(L < T), in this framework. We proposed the point estimator and derived its asymptotic distribution. Both truncated-only data and censored and truncated data were generated in the simulation study. The proposed point and variance estimators showed good performance in various simulated settings. The bone marrow transplant registry data were analyzed as the illustrative example. |
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| Keywords: | Dependently truncated data Inverse probability weighting Quasi-independence |
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