학술논문
The mean residual life model for the right-censored data in the presence of covariate measurement errors.
Document Type
Academic Journal
Author
Chen CM; Institute of Public Health, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan.; Weng SC; Department of Post-Baccalaureate Medicine, College of Medicine, National Chung Hsing University, Taichung, Taiwan.; Department of Internal Medicine, Division of Nephrology, Center for Geriatrics and Gerontology, Taichung Veterans General Hospital, Taichung, Taiwan.; Institute of Clinical Medicine, School of Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan.; Research Center for Geriatrics and Gerontology, College of Medicine, National Chung Hsing University, Taichung, Taiwan.; Tsai JR; Department of Statistics and Information Science, Fu Jen Catholic University, New Taipei City, Taiwan.; Shen PS; Department of Statistics, Tunghai University, Taichung, Taiwan.
Source
Publisher: Wiley Country of Publication: England NLM ID: 8215016 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1097-0258 (Electronic) Linking ISSN: 02776715 NLM ISO Abbreviation: Stat Med Subsets: MEDLINE
Subject
Language
English
Abstract
In this article, we consider the mean residual life regression model in the presence of covariate measurement errors. In the whole cohort, the surrogate variable of the error-prone covariate is available for each subject, while the instrumental variable (IV), which is related to the underlying true covariates, is measured only for some subjects, the calibration sample. Without specifying distributions of measurement errors but assuming that the IV is missing at random, we develop two estimation methods, the IV calibration and cohort estimators, for the regression parameters by solving estimation equations (EEs) based on the calibration sample and cohort sample, respectively. To improve estimation efficiency, a synthetic estimator is derived by applying the generalized method of moment for all EEs. The large sample properties of the proposed estimators are established and their finite sample performance are evaluated via simulation studies. Simulation results show that the cohort and synthetic estimators outperform the IV calibration estimator and the relative efficiency of the cohort and synthetic estimators mainly depends on the missing rate of IV. In the case of low missing rate, the synthetic estimator is more efficient than the cohort estimator, while the result can be reversed when the missing rate is high. We illustrate the proposed method by application to data from the patients with stage 5 chronic kidney disease in Taiwan.
(© 2023 John Wiley & Sons Ltd.)
(© 2023 John Wiley & Sons Ltd.)