Pseudo-observations under covariate-dependent censoring

Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avisTidsskriftartikelForskningpeer review

DOI

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.

OriginalsprogEngelsk
TidsskriftJournal of Statistical Planning and Inference
Vol/bind202
NummerSeptember
Sider (fra-til)112-122
Antal sider11
ISSN0378-3758
DOI
StatusUdgivet - sep. 2019

Se relationer på Aarhus Universitet Citationsformater

ID: 146223024