Regression analysis of multivariate panel count data with an informative observation process

被引:16
|
作者
Zhang, Haixiang [1 ]
Zhao, Hui [2 ]
Sun, Jianguo [1 ,3 ]
Wang, Dehui [1 ]
Kim, KyungMann [4 ,5 ]
机构
[1] Jilin Univ, Sch Math, Changchun 130012, Peoples R China
[2] Cent China Normal Univ, Dept Stat, Wuhan 430079, Peoples R China
[3] Univ Missouri, Dept Stat, Columbia, MO 65211 USA
[4] Univ Wisconsin, Dept Biostat & Med Informat, Madison, WI 53792 USA
[5] Univ Wisconsin, Dept Stat, Madison, WI 53792 USA
基金
中国国家自然科学基金;
关键词
Estimating equation; Informative observation process; Marginal mean model; Model checking; SEMIPARAMETRIC TRANSFORMATION MODELS; DEPENDENT OBSERVATION; OBSERVATION TIMES; POINT-PROCESSES;
D O I
10.1016/j.jmva.2013.04.012
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
Multivariate panel count data arise in event history studies on recurrent events if there exist several related events and study subjects can be examined or observed only at discrete time points instead of over continuous periods. In these situations, a complicated issue that may arise is that the observation time points or process may be related to the underlying recurrent event process of interest. That is, we have informative observation processes. It is obvious that to perform a valid analysis, both the relationship among different types of recurrent events and the informative observation process need to be taken into account. To address these, we propose a robust joint modeling approach. For the estimation of regression parameters, an estimating equation-based inference procedure is developed and the asymptotic properties of the resulting estimates are established. Numerical studies indicate that the proposed approach works well for practical situations and the methodology is applied to a skin cancer study that motivates this study. (C) 2013 Elsevier Inc. All rights reserved.
引用
收藏
页码:71 / 80
页数:10
相关论文
共 50 条
  • [21] Semiparametric transformation models for multivariate panel count data with dependent observation process
    Li, Ni
    Park, Do-Hwan
    Sun, Jianguo
    Kim, KyungMann
    [J]. CANADIAN JOURNAL OF STATISTICS-REVUE CANADIENNE DE STATISTIQUE, 2011, 39 (03): : 458 - 474
  • [22] Robust estimation for panel count data with informative observation times
    Zhao, Xingqiu
    Tong, Xingwei
    Sun, Jianguo
    [J]. COMPUTATIONAL STATISTICS & DATA ANALYSIS, 2013, 57 (01) : 33 - 40
  • [23] Regression analysis of mixed panel count data with dependent observation processes
    Ge, Lei
    Choi, Jaihee
    Zhao, Hui
    Li, Yang
    Sun, Jianguo
    [J]. JOURNAL OF NONPARAMETRIC STATISTICS, 2023, 35 (04) : 669 - 684
  • [24] Semiparametric analysis of multivariate panel count data with dependent observation processes and a terminal event
    Zhao, Hui
    Li, Yang
    Sun, Jianguo
    [J]. JOURNAL OF NONPARAMETRIC STATISTICS, 2013, 25 (02) : 379 - 394
  • [25] Robust estimation for panel count data with informative observation times and censoring times
    Jiang, Hangjin
    Su, Wen
    Zhao, Xingqiu
    [J]. LIFETIME DATA ANALYSIS, 2020, 26 (01) : 65 - 84
  • [26] Robust estimation for panel count data with informative observation times and censoring times
    Hangjin Jiang
    Wen Su
    Xingqiu Zhao
    [J]. Lifetime Data Analysis, 2020, 26 : 65 - 84
  • [27] Regression analysis of panel count data with covariate-dependent observation and censoring times
    Sun, JG
    Wei, LJ
    [J]. JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY, 2000, 62 : 293 - 302
  • [28] Analysis of Gap Times Based on Panel Count Data With Informative Observation Times and Unknown Start Time
    Ma, Ling
    Sundaram, Rajeshwari
    [J]. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 2018, 113 (521) : 294 - 305
  • [29] AUGMENTED ESTIMATING EQUATIONS FOR SEMIPARAMETRIC PANEL COUNT REGRESSION WITH INFORMATIVE OBSERVATION TIMES AND CENSORING TIME
    Wang, Xiaojing
    Ma, Shuangge
    Yan, Jun
    [J]. STATISTICA SINICA, 2013, 23 (01) : 359 - 381
  • [30] Efficient estimation of panel count data with dependent observation process
    Wang, Weiwei
    Wang, Yijun
    Wu, Xianyi
    Zhao, Xiaobing
    [J]. JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION, 2021, 91 (03) : 464 - 476