Pseudo-observations under covariate-dependent censoring

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A regression analysis using jack-knife pseudo-observations from the Kaplan–Meier estimator, or related estimators, can be biased when censoring times depend on event times or covariates. We study ways in which other, covariate-dependent, estimators can be used in place of the Kaplan–Meier related estimators to overcome the problem. These estimators are inverse probability weighted estimators, weighting with an estimate of the probability of observation based on a model of the censoring distribution. We study an additive hazard model and a proportional hazards model for the censoring distribution. We argue that, under certain assumptions, the pseudo-observation method with pseudo-observations from such estimators will produce consistent and asymptotically normal parameter estimates.

Original languageEnglish
JournalJournal of Statistical Planning and Inference
Pages (from-to)112-122
Number of pages11
Publication statusPublished - Sep 2019

    Research areas

  • Functional approach, IPCW, Pseudo-value, Survival analysis, p-variation, GENERALIZED LINEAR-MODELS

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