Mean residual life cure models for right‐censored data with and without length‐biased sampling
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Published:2023-04-17
Issue:5
Volume:65
Page:
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ISSN:0323-3847
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Container-title:Biometrical Journal
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language:en
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Short-container-title:Biometrical J
Author:
Chen Chyong‐Mei1ORCID,
Chen Hsin‐Jen1,
Peng Yingwei2ORCID
Affiliation:
1. Institute of Public Health School of Medicine National Yang Ming Chiao Tung University Taipai Taiwan ROC
2. Departments of Public Health Sciences and Mathematics and Statistics Queen's University Kingston Ontario Canada
Abstract
AbstractWe propose a semiparametric mean residual life mixture cure model for right‐censored survival data with a cured fraction. The model employs the proportional mean residual life model to describe the effects of covariates on the mean residual time of uncured subjects and the logistic regression model to describe the effects of covariates on the cure rate. We develop estimating equations to estimate the proposed cure model for the right‐censored data with and without length‐biased sampling, the latter is often found in prevalent cohort studies. In particular, we propose two estimating equations to estimate the effects of covariates in the cure rate and a method to combine them to improve the estimation efficiency. The consistency and asymptotic normality of the proposed estimates are established. The finite sample performance of the estimates is confirmed with simulations. The proposed estimation methods are applied to a clinical trial study on melanoma and a prevalent cohort study on early‐onset type 2 diabetes mellitus.
Funder
Natural Sciences and Engineering Research Council of Canada
Ministry of Science and Technology, Taiwan
Subject
Statistics, Probability and Uncertainty,General Medicine,Statistics and Probability
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