Semiparametric additive time-varying coefficients model for longitudinal data with censored time origin

被引:0
|
作者
Sun, Yanqing [1 ]
Shou, Qiong [2 ]
Gilbert, Peter B. [3 ,4 ]
Heng, Fei [5 ]
Qian, Xiyuan [6 ]
机构
[1] Univ North Carolina Charlotte, Dept Math & Stat, Charlotte, NC 28223 USA
[2] MSD R&D China Co Ltd, Biostat & Res Decis Sci Asia Pacific, Beijing, Peoples R China
[3] Univ Washington, Dept Biostat, Seattle, WA 98195 USA
[4] Fred Hutchinson Canc Res Ctr, 1124 Columbia St, Seattle, WA 98104 USA
[5] Univ North Florida, Dept Math & Stat, Jacksonville, FL USA
[6] East China Univ Sci & Technol, Sch Math, Shanghai, Peoples R China
基金
美国国家科学基金会;
关键词
censored time origin; kernel smoothing; longitudinal data; random sampling times; Step vaccine trial; weight selection; PROPORTIONAL HAZARDS MODELS; CELL-MEDIATED-IMMUNITY; REGRESSION-MODEL; CLUSTERED DATA; HIV-1; VACCINE; TRIAL; INFECTION;
D O I
10.1111/biom.13610
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
Statistical analysis of longitudinal data often involves modeling treatment effects on clinically relevant longitudinal biomarkers since an initial event (the time origin). In some studies including preventive HIV vaccine efficacy trials, some participants have biomarkers measured starting at the time origin, whereas others have biomarkers measured starting later with the time origin unknown. The semiparametric additive time-varying coefficient model is investigated where the effects of some covariates vary nonparametrically with time while the effects of others remain constant. Weighted profile least squares estimators coupled with kernel smoothing are developed. The method uses the expectation maximization approach to deal with the censored time origin. The Kaplan-Meier estimator and other failure time regression models such as the Cox model can be utilized to estimate the distribution and the conditional distribution of left censored event time related to the censored time origin. Asymptotic properties of the parametric and nonparametric estimators and consistent asymptotic variance estimators are derived. A two-stage estimation procedure for choosing weight is proposed to improve estimation efficiency. Numerical simulations are conducted to examine finite sample properties of the proposed estimators. The simulation results show that the theory and methods work well. The efficiency gain of the two-stage estimation procedure depends on the distribution of the longitudinal error processes. The method is applied to analyze data from the Merck 023/HVTN 502 Step HIV vaccine study.
引用
收藏
页码:695 / 710
页数:16
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