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多类型复发事件数据下一类Box-Cox转移模型

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多类型复发事件数据下一类Box-Cox转移模型 郦博文1, 张海祥21. 中国科学技术大学统计与金融系, 合肥 230026;
2. 中国科学院数学与系统科学研究院, 北京 100190 A Class of Box-Cox Transformation Models for Multiple Type Recurrent Event Data LI Bowen1, ZHANG Haixiang21. Departmnet of Statistics and Finance, University of Science and Technology of China, Hefei 230026, China;
2. Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
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摘要在本文中,我们针对多类型的复发事件数据提出了一类Box-Cox转移模型,它包含了比例模型作为其特殊情况.该模型在刻画协变量对于计数过程的均值函数效应时具有很大的灵活性.对于模型的推断问题,我们基于估计方程方法给出了未知参数的估计,并研究了该估计的大样本性质.最后,我们提出了一种基于残差的模型检验方法.
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收稿日期: 2015-06-05
PACS:O212.7
引用本文:
郦博文, 张海祥. 多类型复发事件数据下一类Box-Cox转移模型[J]. 应用数学学报, 2016, 39(5): 656-668. LI Bowen, ZHANG Haixiang. A Class of Box-Cox Transformation Models for Multiple Type Recurrent Event Data. Acta Mathematicae Applicatae Sinica, 2016, 39(5): 656-668.
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http://123.57.41.99/jweb_yysxxb/CN/ http://123.57.41.99/jweb_yysxxb/CN/Y2016/V39/I5/656


[1] Therneau T, Hamilton S. rhDNase as an example of recurrent event analysis. Statistics in Medicine, 1997, 16:2029-2047
[2] Li Q H, Lagakos S W. Use of the Wei-Lin-Weissfeld method for the analysis of a recurring and a terminating event. Statistics in Medicine, 1997, 16:925-940
[3] Abu-Libdeh H, Turnbull B, Clark L. Analysis of multi-type recurrent events in longitudinal studies:application to a skin cancer prevention trial. Biometrics, 1990, 46:1017-1034
[4] Prentice R, Williams B, Peterson A. On the regression analysis of multivariate failure time data. Biometrika, 1981, 68:373-379
[5] Andersen P, Gill R. Cox's regression model for counting processes:a large sample study. Ann. Stat., 1982, 10:1100-1120
[6] Chang S. Wang, M. Conditional regression analysis for recurrence time data. J. Am. Stat. Assoc., 1999, 94:1221-1230
[7] Zeng D, Lin D Y. Efficient estimation of semiparametric transformation models for counting processes. Biometrika, 2006, 93:627-640
[8] Wei L J, Lin D Y, Weissfeld L. Regression analysis of multivariate incomplete failure time data by modeling marginal distributions. J. Am. Stat. Assoc., 1989, 84:1065-1073
[9] Pepe M, Cai, J. Some graphical displays and marginal regression analyses for recurrent failure times and time-dependent covariates. J. Am. Stat. Assoc., 1993, 88:811-820
[10] Lawless J, Nadeau, C. Some simple robust methods for the analysis of recurrent events. Technometrics, 1995, 37:158-168
[11] Lin D Y, Wei L J, Yang I, Ying, Z. Semiparametric regression for the mean and rate function of recurrent events. J. R. Stat. Soc. B, 2000, 69:711-730
[12] Lin D Y, Wei L J, Ying Z. Semiparametric transformationmodels for point processes. J. Am. Stat. Assoc., 2001, 96:620-628
[13] Nielsen G G, Gill R D, Andersen P K, Sorensen T. A counting process approach to maximum likelihood estimation in frailty models. Scand. J. Stat., 1992, 19:25-43
[14] Murphy S. Consistency in a proportional hazards model incorporating a random effect. Ann. Stat., 1994, 22:712-731
[15] Zeng D, Lin D Y. Semiparametric transformation models with random effects for recurrent events. J. Am. Stat. Assoc., 2007, 102:167-180
[16] Ghosh D, Lin D. Marginal regression models for recurrent and terminal events. Statistica Sinica, 2002, 12:663-688
[17] Zeng D, Lin D Y. Semiparametric transformation models with random effects for joint analysis of recurrent and terminal events. Biometrics, 2009, 65:746-752
[18] Zeng D, Cai J. A semiparametric additive rate model for recurrent events with an informative terminal event. Biometrika, 2010, 97:699-712
[19] Zhao X, Zhou J, Sun L. Semiparametric transformation models with time-varying coefficients for recurrent and terminal events. Biometrics, 2011, 67:404-414
[20] Sun L, Tong X, Zhou X. A class of Box-Cox transformation models for recurrent event data. Lifetime Data Anal., 2011, 17(2):280-301
[21] Sun L, Zhao X, Zhou, J. A class of mixed models for recurrent event data. Canad. J. Statist., 2011, 39(4):578-590
[22] Sun L, Zhou X, Guo S. Marginal regression models with time-varying coefficients for recurrent event data. Stat. Med., 2011, 30(18):2265-2277
[23] Tong X, Zhu L, Sun, J. Variable selection for recurrent event data via nonconcave penalized estimating function. Lifetime Data Analysis, 2009, 15:197-215
[24] Zhu L, Sun J, Srivastava D K, Tong X, Leisenring W, Robison L L. Semiparametric transformation models for joint analysis of multivariate recurrent and terminal events. Stat. Med., 2011, 30:3010-3023
[25] Harrington D P, Fleming T R. A class of rank test procedures for censored survival data. Biometrika, 1982, 69:553-566
[26] Bagdonavi?ius V, Nikulin M. Generalized proportional hazards model based on modified partial likelihood. Lifetime Data Anal., 1999, 5:329-350
[27] Scharfstein D O, Tsiatis A A, Gilbert P B. Semiparametric efficient estimation in the generalized oddsrate class of regression models for right-censored time-to-event data. Lifetime Data Anal., 1998, 4:355-391
[28] Yang S. Some inequalities about the Kaplan-Meier estimator. Ann. Stat. 1992, 20:535-544
[29] Lin D Y, Ying Z. Semiparametric and nonparametric regression analysis of longitudinal data. J. Am. Stat. Assoc., 2001, 96:103-126
[30] Gill R D, Johansen S. A survey of product-integration with a view toward application in survival analysis. Annals of Statistics, 1990, 18:1501-1555
[31] Pollard D. Empirical Processes:Theory and Applications. Institute of Mathematical Statistics, Hayward, California, 1990
[32] Lai T L, Ying Z. Stochastic integrals of empirical-Type processes with applications to censored regression. J. Multivariate Anal., 1988, 27:334-358
[33] Lin D Y, Wei L J, Ying Z. Accelerated failure time models for counting processes. Biometrika, 1998, 85:605-618
[34] Liu L, Wolfe R A, Huang X. Shared frailty models for recurrent events and a terminal event. Biometrics, 2004, 60:747-756

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