The broad, long-term objective of this research project is to develop new statistical methodology for analyzing multivariate or correlated failure time data from cardiovascular disease research and other biomedical studies. Multivariate failure time data arise naturally in biomedical studies. For example, in family studies, the ages of disease occurrence are recorded for members of families;in epidemiological cohort studies, individual study subjects are followed for the occurrence of different events;or, in clinical trials, patients are followed for repeated recurrent events. A common feature of the data in these examples is that the failure times could be correlated. Valid statistical methods need to account for this correlation. There are 5 specific aims in this competing renewal application.
The first aim concerns statistical inferences for multivariate failure time data from case-cohort studies. Marginal hazards model will be considered and an estimating equation approach will be developed for parameter estimation.
The second aim studies a class of semi-parametric additive risk model with mixed effects for multivariate failure time data. An estimating equations approach and a maximum likelihood approach are proposed for estimation.
The third aim considers time-varying coefficient rate models for recurrent event data. Regression splines and penalized regression splines will be considered to estimate the time-varying coefficient.
The fourth aim concerns frailty models with flexible frailty distribution for multivariate failure time data. Estimation will be based on the sieve likelihood estimation procedure. The fifth aim investigates inference procedures for marginal hazards models with multivariate failure time data from case-control studies. Parameter estimation will be conducted through a weighted estimating equation approach. The strength and weakness of each proposed method will be critically examined via theoretical investigations and simulation studies. Related software will be developed. Data sets from epidemiologic studies on cardiovascular disease and other biomedical studies will be analyzed using the proposed methods. This research will provide valuable new tools to cardiovascular disease researchers and other biomedical researchers. It will help the researchers to better understand the risk factors associated with cardiovascular diseases.
|Kang, Sangwook; Cai, Jianwen; Chambless, Lloyd (2013) Marginal additive hazards model for case-cohort studies with multiple disease outcomes: an application to the Atherosclerosis Risk in Communities (ARIC) study. Biostatistics 14:28-41|
|Kang, Chaeryon; Qaqish, Bahjat; Monaco, Jane et al. (2013) Kappa statistic for clustered dichotomous responses from physicians and patients. Stat Med 32:3700-19|
|Esserman, Denise; Zhao, Yingqi; Tang, Yiyun et al. (2013) Sample size estimation in educational intervention trials with subgroup heterogeneity in only one arm. Stat Med 32:2140-54|
|Chen, Xiaolin; Wang, Qihua; Cai, Jianwen et al. (2012) Semiparametric additive marginal regression models for multiple type recurrent events. Lifetime Data Anal 18:504-27|
|Liu, Yanyan; Yuan, Zhongshang; Cai, Jianwen et al. (2012) Marginal hazard regression for correlated failure time data with auxiliary covariates. Lifetime Data Anal 18:116-38|
|Zeng, Donglin; Schaubel, Douglas E; Cai, Jianwen (2011) Semiparametric Transformation Rate Model for Recurrent Event Data. Stat Biosci 3:187-207|
|Zhou, Haibo; Wu, Yuanshan; Liu, Yanyan et al. (2011) Semiparametric inference for a 2-stage outcome-auxiliary-dependent sampling design with continuous outcome. Biostatistics 12:521-34|
|Cai, Jianwen; Zeng, Donglin (2011) Additive mixed effect model for clustered failure time data. Biometrics 67:1340-51|
|Kang, Sangwook; Cai, Jianwen (2010) Asymptotic results for fitting marginal hazards models from stratified case-cohort studies with multiple disease outcomes. J Korean Stat Soc 39:371-385|
|Zeng, Donglin; Cai, Jianwen (2010) Additive transformation models for clustered failure time data. Lifetime Data Anal 16:333-52|
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