Abstract

This proposed research focuses on the development of models and statistical methods for analyzing event observations. The observations are obtained by following subjects undergoing intervention programs designed to prolong the time to the next occurrence of the recurrent event of interest (e.g., a return to substance abuse, another heart attack, another period of depression, to name a few). We will develop methods for predicting the time to the next occurrence, for comparing various types of interventions (e.g. competing treatments) and determining the relative efficacies, and for assessing which factors (covariates) significantly affect the time to the next event. Our models and methods are richer and more flexible than standard approaches, including Cox's proportional hazards model based on time-dependent covariates. In particular, our models simultaneously incorporate intervention effects, weakening (or strengthening) effects of the number of event occurrences, and the effects of the covariates. We will use the methods to analyze various data sets including hospitalization data from the US Renal Data System.

  Funding Agency

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM056182-02
Application #
6019307
Study Section
Special Emphasis Panel (ZRG7-STA (01))
Program Officer
Harris, Maureen I
Project Start
1998-07-01
Project End
2000-08-31
Budget Start
1999-07-01
Budget End
2000-08-31
Support Year
2
Fiscal Year
1999
Total Cost
Indirect Cost

  Institution

Name
Bowling Green State University
Department
Biostatistics & Other Math Sci
Type
Schools of Arts and Sciences
DUNS #
617407325
City
Bowling Green
State
OH
Country
United States
Zip Code
43403

  Publications

Adekpedjou, Akim; Peña, Edsel A; Quiton, Jonathan (2010) Estimation and Efficiency with Recurrent Event Data under Informative Monitoring. J Stat Plan Inference 140:597-615
Clement, David Y; Strawderman, Robert L (2009) Conditional GEE for recurrent event gap times. Biostatistics 10:451-67
Pena, Edsel A; Slate, Elizabeth H; Gonzalez, Juan R (2007) Semiparametric Inference for a General Class of Models for Recurrent Events. J Stat Plan Inference 137:1727-1747
Han, Jun; Slate, Elizabeth H; Pena, Edsel A (2007) Parametric latent class joint model for a longitudinal biomarker and recurrent events. Stat Med 26:5285-302
Pena, Edsel A (2006) Dynamic Modelling and Statistical Analysis of Event Times. Stat Sci 21:1-26
Agustin, Ma Zenia N; Pena, Edsel A (2005) A basis approach to goodness-of-fit testing in recurrent event models. J Stat Plan Inference 133:285-303
Hollander, Myles; Pena, Edsel A (2004) Nonparametric Methods in Reliability. Stat Sci 19:644-651
Agustin, M Z; Pena, E A (2001) Goodness-of-fit of the distribution of time-to-first-occurrence in recurrent event models. Lifetime Data Anal 7:289-306