This project will develop and investigate new methodology for analyzing recurrent event data from cardiovascular disease, asthma study and other biomedical research. The proposal describes four projects. The first project concerns statistical inferences for multiple-type recurrent events data. Parameter estimation will be based on an estimating equations approach. To increase efficiency, weighted estimating equations will be developed with weights inversely proportional to intra-subject covariance; parametric and non-parametric correlation estimators will be developed. Inference will be based on the multivariate central limit theorem and modern empirical processes theory. Asymptotic and finite sample properties will be examined. The proposed methods will be used to analyze data from a clinical study of left ventricular dysfunction patients and a retrospective cohort study of childhood asthma. The second project considers an accelerated failure time marginal means model and conditional multiplicative means model for analyzing censored recurrent event data which allow for terminating events. Parameter estimation will be based on an estimating equation approach. The strengths and weaknesses of the proposed method will be critically examined via theoretical investigations and simulation studies. Data from a study of kidney transplant patients will be analyzed using the proposed methods. The third project concerns marginal and conditional means models for analyzing censored recurrent event data, which accommodate both terminating events and dependent censoring. Parameter estimation will be conducted through an estimating equation approach, with inference based on the multivariate central limit theorem and empirical processes. Asymptotic and finite sample properties will be examined. Methods proposed will be applied to analyze data from a clinical study of dialysis patients. The fourth project investigates additive means models for censored recurrent event data. We will propose methods of estimation, which are applicable for recurrent events with independent censoring only, with terminal events and independent censoring, and with both terminal events and dependent censoring. Asymptotic and finite sample properties will be examined. Data sets from asthma, dialysis and renal transplant studies will be analyzed using the proposed methods.

Agency
National Institute of Health (NIH)
Institute
National Heart, Lung, and Blood Institute (NHLBI)
Type
Research Project (R01)
Project #
5R01HL057444-07
Application #
6621596
Study Section
Special Emphasis Panel (ZRG1-SNEM-5 (01))
Program Officer
Wolz, Michael
Project Start
1997-01-01
Project End
2005-12-31
Budget Start
2003-01-01
Budget End
2003-12-31
Support Year
7
Fiscal Year
2003
Total Cost
$144,179
Indirect Cost
Name
University of North Carolina Chapel Hill
Department
Biostatistics & Other Math Sci
Type
Schools of Public Health
DUNS #
608195277
City
Chapel Hill
State
NC
Country
United States
Zip Code
27599
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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