title: A maximum pseudo-profile likelihood estimator for the Cox model under length-biased sampling.
abstract: This paper considers semiparametric estimation of the Cox proportional hazards model for right-censored and length-biased data arising from prevalent sampling. To exploit the special structure of length-biased sampling, we propose a maximum pseudo-profile likelihood estimator, which can handle time-dependent covariates and is consistent under covariate-dependent censoring. Simulation studies show that the proposed estimator is more efficient than its competitors. A data analysis illustrates the methods and theory.
authors.affiliation: Biostatistics Research Branch, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland 20892, U.S.A. , huangchi@niaid.nih.gov.
authors.forename: Chiung-Yu
authors.initials: CY
authors.lastname: Huang
authors.forename: Jing
authors.initials: J
authors.lastname: Qin
authors.forename: Dean A
authors.initials: DA
authors.lastname: Follmann
keywords: Approximate likelihood; Cross-sectional sampling; Product-limit estimator; Random truncation; Screening trials