Semiparametric varying-coefficient model with right-censored and length-biased data

被引:6
作者
Lin, Cunjie [1 ]
Zhou, Yong [2 ,3 ]
机构
[1] Renmin Univ China, Sch Stat, Beijing, Peoples R China
[2] Chinese Acad Sci, Acad Math & Syst Sci, Beijing, Peoples R China
[3] Shanghai Univ Finance & Econ, Sch Stat & Management, Shanghai, Peoples R China
基金
中国国家自然科学基金;
关键词
Length-biased data; Local linear regression; Right-censored; Semiparametric varying-coefficient model; PARTIALLY LINEAR-MODELS; NONPARAMETRIC-ESTIMATION; EMPIRICAL DISTRIBUTIONS; EFFICIENT ESTIMATION; SURVIVAL-DATA; LARGE-SAMPLE; COX MODEL; COHORT;
D O I
10.1016/j.jmva.2016.08.008
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
The analysis of right-censored and length-biased data is commonly encountered in prevalent cohort studies. The special structure of length-biased data is different from the structure of traditional survival data and the methods for traditional survival data cannot be directly applied to length-biased data, because the assumption of independent censoring is often violated in the presence of biased sampling and the assumed model for the underlying population is no longer satisfied. In this paper, we propose a flexible semiparametric varying-coefficient model for analyzing the covariate effects on the population survival time under length-biased sampling. To estimate the parameters, we develop a three-stage estimation procedure, which can improve the efficiency of the estimators. In addition to the case where the censoring variable is independent of the covariates, we consider the case where the censoring variable depends on covariates. The asymptotic properties of the proposed estimators are derived under regularity conditions, and a resampling procedure is used to confirm the methods through simulations. Finally, we illustrate the methods using data concerning the Academy Awards. (C) 2016 Elsevier Inc. All rights reserved.
引用
收藏
页码:119 / 144
页数:26
相关论文
共 32 条
[1]   A formal test for the stationarity of the incidence rate using data from a prevalent cohort study with follow-up [J].
Addona, Vittorio ;
Wolfson, David B. .
LIFETIME DATA ANALYSIS, 2006, 12 (03) :267-284
[2]   COX REGRESSION-MODEL FOR COUNTING-PROCESSES - A LARGE SAMPLE STUDY [J].
ANDERSEN, PK ;
GILL, RD .
ANNALS OF STATISTICS, 1982, 10 (04) :1100-1120
[3]  
[Anonymous], 1991, Counting Processes and Survival Analysis
[4]  
[Anonymous], VERW GEBIETE
[5]   Varying coefficient transformation models with censored data [J].
Chen, Kani ;
Tong, Xingwei .
BIOMETRIKA, 2010, 97 (04) :969-976
[6]  
COX DR, 1972, J R STAT SOC B, V34, P187
[7]   Profile likelihood inferences on semiparametric varying-coefficient partially linear models [J].
Fan, JQ ;
Huang, T .
BERNOULLI, 2005, 11 (06) :1031-1057
[8]   LARGE SAMPLE THEORY OF EMPIRICAL DISTRIBUTIONS IN BIASED SAMPLING MODELS [J].
GILL, RD ;
VARDI, Y ;
WELLNER, JA .
ANNALS OF STATISTICS, 1988, 16 (03) :1069-1112
[9]  
Gonzalez-Manteiga W., 1994, J. Nonparametric Stat, V4, P65, DOI DOI 10.1080/10485259408832601
[10]  
HASTIE T, 1993, J ROY STAT SOC B MET, V55, P757