应用数学学报
首页  |  期刊介绍  |  编 委 会  |  投稿指南  |  期刊订阅  |  广告服务  |  相关链接  |  下载中心  |  联系我们  |  留言板
 
应用数学学报 英文版  
   
   
高级检索 »  
应用数学学报  2013, Vol. 36 Issue (6): 961-977    DOI:
论文 最新目录 | 下期目录 | 过刊浏览 | 高级检索  |   
有偏抽样下带终止时间和带信息观察时间的纵向数据分析
苗瑞1, 李怿2
1. 中国科学院数学与系统科学研究院, 北京 100190;
2. 中国科学技术大学统计与金融系, 合肥 230026
Analysis of Longitudinal Data with Informative Observation and Terminal Event Times Under Biased Sampling
MIAO Rui1, LI Yi2
1. Academy of Mathematics and System Sciences, Chinese Academy of Sciences, Beijing 100190;
2. Department of Statistics and Finance, University of Science and Technology of China, Hefei 230026
 全文: PDF (432 KB)   HTML (1 KB)   输出: BibTeX | EndNote (RIS)      背景资料
摘要 纵向数据经常出现在生物医学等实际领域,而且在许多情况下,观察时间和终止时间是带信息的,同时可能会存在有偏抽样. 本文在有偏抽样下,对于带信息观察时间和终止时间的纵向数据,提出了一个联合建模方法,并利用借代力量方法给出了模型参数的估计,同时获得了这些估计的渐近性质. 最后,提出一种模型检验方法.
服务
把本文推荐给朋友
加入我的书架
加入引用管理器
E-mail Alert
RSS
作者相关文章
苗瑞
李怿
关键词有偏抽样   带信息观察   终止时间   联合建模   潜在变量   估计方程     
Abstract: Longitudinal data frequently occur in medical follow-up studies, and in many situations, there may exist informative observation times and a dependent terminal event time, as well as biased sampling. In this article, we propose joint modeling and analysis of longitudinal data with possibly informative observation times and a dependent terminal event time under biased sampling. A borrow-strength estimation procedure is developed for parameter estimation, and asymptotic properties of the proposed estimators are established. In addition, a model checking method is presented for assessing the adequacy of the model.
Key wordsbiased sampling   informative observation times   terminal event time   joint modeling   Latent variables   estimating equation   
收稿日期: 2013-08-21;
基金资助:国家自然科学基金(11231010,11171330,11021161)资助项目.
引用本文:   
苗瑞,李怿. 有偏抽样下带终止时间和带信息观察时间的纵向数据分析[J]. 应用数学学报, 2013, 36(6): 961-977.
MIAO Rui,LI Yi. Analysis of Longitudinal Data with Informative Observation and Terminal Event Times Under Biased Sampling[J]. Acta Mathematicae Applicatae Sinica, 2013, 36(6): 961-977.
 
[1] Welsh A H, Lin X, Carroll R J. Marginal Longitudinal Nonparametric Regression: Locality and Efficiency of Spline and Kernel Methods. Journal of the American Statistical Association, 2002, 97: 482-493
[2] Davis C S. (2002). Statistical Methods for the Analysis of Repeated Measurements. New York: Springer-Verlag, 2002
[3] Fitzmaurice G M, Laird N M, Ware J H. Applied Longitudinal Analysis. New York: John Wiley and Sons, 2004
[4] Fan J, Li R. New Estimation and Model Selection Procedures for Semiparametric Modeling in Longitudinal Data Analysis. Journal of the American Statistical Association, 2004, 99: 710-723
[5] Diggle P J, Liang K Y, Zeger S L. The Analysis of Longitudinal Data. Oxford: Oxford University Press, 1994
[6] Lin D Y, Ying Z. Semiparametric and Nonparametric Regression Analysis of Longitudinal Data. Journal of the American Statistical Association, 2001, 96: 103-126
[7] Lin H, Scharfstein D O, Rosenheck D O. Analysis of Longitudinal Data with Irregular Outcome-dependent Follow-up. Journal of Royal Statistical Society B, 2004, 66: 791-813
[8] Sun J, Park D -H, Sun L, Zhao X. Semiparametric Regression Analysis of Longitudinal Data with Informative Observation Times. Journal of the American Statistical Association, 2005, 100: 882-889
[9] Sun J, Sun L, Liu D. Regression Analysis of Longitudinal Data in the Presence of Informative Observation and Censoring Times. Journal of the American Statistical Association, 2007, 102: 1397-1406
[10] Liang Y, Lu W, Ying, Z. Joint Modeling and Analysis of Longitudinal Data with Informative Observation Times. Biometrics, 2009, 65: 377-384
[11] Liu L, Huang X, O'Quigley J. Analysis of Longitudinal Data in the Presence of Informative Observational Times and a Dependent Terminal Event, with Application to Medical Cost Data. Biometrics, 2008, 64: 950-958
[12] Liu L, Wolfe R A, Huang X. Shared Frailty Models for Recurrent Events and a Terminal Event. Biometrics, 2004, 60: 747-756
[13] Liu L, Wolfe R A, Kalbfleisch J D. A Shared Random Effects Model for Censored Medical Costs and Mortality. Statistics in Medicine, 2007, 26: 139-155
[14] He X, Tong X, Sun J. Semiparametric Analysis of Panel Count Data with Correlated Observation and Follow-up Times. Lifetime Data Analysis, 2009, 15: 177-196
[15] Sun L, Song X, Zhou J, Liu L. Joint Analysis of Longitudinal Data with Informative Observation Times and a Dependent Terminal Event. Journal of the American Statistical Association, 2012, 107: 688-700
[16] Bůžková P, Lumley T. Semiparametric Log-linear Regression for Longitudinal Measurements Subject to Outcome-dependent Follow-up. Journal of Statistical Planning and Inference, 2008, 138: 2450-2461
[17] 刘焕彬, 苗瑞, 孙六全. 有偏抽样下带信息观察和删失的面板数据的统计分析. 中国科学: 数学, 2011, 41(4): 365-376 (Liu H, Miao R, Sun L. Analysis of Panel Data with Informative Observation and Censoring Times under Biased Sampling. Science China, Series A, 2011, 41: 365-376)
[18] Wang M C, Qin J, Chiang C T. Analyzing Recurrent Event Data with Informative Censoring. Journal of the American Statistical Association, 2001, 96: 1057-1065
[19] Huang C Y, Wang M C. Joint Modeling and Estimation for Recurrent Event Processes and Failure Time Data. Journal of the American Statistical Association, 2004, 99: 1153-1165
[20] Schoenfeld D. Partial Residuals for the Proportional Hazards Regression Model. Biometrika, 1982, 69: 239-241
[21] Lin D Y, Wei L J, Ying Z. Checking the Cox Model with Cumulative Sums of Martingale-based Residuals. Biometrika, 1993, 85: 605-619
[22] Zeng D, Cai J. A Semiparametric Additive Rate Model for Recurrent Events with an Informative Terminal Event. Biometrika, 2010, 97: 699-712
[23] Huang C Y, Qin J, Wang M C. Semiparametric Analysis for Recurrent Event Data with Time-dependent Covariates and Informative Censoring. Biometrics, 2010, 65: 39-49
[24] Sun L, Song X, Zhou J. Regression Analysis of Longitudinal Data with Time-dependent Covariates in the Presence of Informative Observation and Censoring Times. Journal of Statistical Planning and Inference, 2011, 141: 2902-2919
[25] Song X, Mu X, Sun L. Regression Analysis of Longitudinal Data with Time-dependent Covariates and Informative Observation Times. Scandinavian Journal of Statistics, 2012, 39: 248-258
[26] Lin D Y, Wei L J, Ying Z. Semiparametric Transformation Models for Point Processes. Journal of the American Statistical Association, 2001, 96: 620-628
[27] Lin D Y, Wei L J, Yang I, Ying Z. Semiparametric Regression for the Mean and Rate Functions of Recurrent Events. Journal of Royal Statistical Society B, 2000, 62: 711-730
[1] 何穗, 程希明, 周洁. 带终止事件的多类型复发事件的一般加性乘积比例模型[J]. 应用数学学报, 2012, 35(5): 804-816.
[2] 何穗, 王芬. 成组复发事件下的半参数变换模型[J]. 应用数学学报, 2012, (4): 728-736.
[3] 何穗, 王芬, 刘焕彬. 成组复发事件下的一般半参数加性乘积比率回归模型[J]. 应用数学学报, 2010, 33(3): 412-423.
[4] 张忠占. 使用Mantel-Haenszel方法在套情形控制研究中估计风险比率[J]. 应用数学学报, 2001, 17(4): 457-468.
[5] 张忠占. 使用Mantel-Haenszel方法在套情形控制研究中估计风险比率[J]. 应用数学学报, 2001, 17(4): 457-468.
  版权所有 © 2009 应用数学学报编辑部   E-mail: amas@amt.ac.cn
京ICP备05002806号-9