Open Access
June 2012 Modeling left-truncated and right-censored survival data with longitudinal covariates
Yu-Ru Su, Jane-Ling Wang
Ann. Statist. 40(3): 1465-1488 (June 2012). DOI: 10.1214/12-AOS996


There is a surge in medical follow-up studies that include longitudinal covariates in the modeling of survival data. So far, the focus has been largely on right-censored survival data. We consider survival data that are subject to both left truncation and right censoring. Left truncation is well known to produce biased sample. The sampling bias issue has been resolved in the literature for the case which involves baseline or time-varying covariates that are observable. The problem remains open, however, for the important case where longitudinal covariates are present in survival models. A joint likelihood approach has been shown in the literature to provide an effective way to overcome those difficulties for right-censored data, but this approach faces substantial additional challenges in the presence of left truncation. Here we thus propose an alternative likelihood to overcome these difficulties and show that the regression coefficient in the survival component can be estimated unbiasedly and efficiently. Issues about the bias for the longitudinal component are discussed. The new approach is illustrated numerically through simulations and data from a multi-center AIDS cohort study.


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Yu-Ru Su. Jane-Ling Wang. "Modeling left-truncated and right-censored survival data with longitudinal covariates." Ann. Statist. 40 (3) 1465 - 1488, June 2012.


Published: June 2012
First available in Project Euclid: 5 September 2012

zbMATH: 1257.62114
MathSciNet: MR3015032
Digital Object Identifier: 10.1214/12-AOS996

Primary: 62N02
Secondary: 62E20

Keywords: Biased sample , EM algorithm , Likelihood approach , Monte Carlo integration , Semiparametric efficiency

Rights: Copyright © 2012 Institute of Mathematical Statistics

Vol.40 • No. 3 • June 2012
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