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An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
A maximum pseudo-profile likelihood estimator for the Cox model under length-biased sampling
Chiung-Yu Huang1, Jing Qin, Dean A Follmann
1Biostatistics Research Branch, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland 20892, U.S.A. , huangchi@niaid.nih.gov.
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.
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