Joint Statistics/Biostatistics and
Medical Informatics Department Seminar
Characterizing, Modeling and Analyzing Recurrent Event Data
Mei-Cheng Wang, Ph.D., Professor, Department of Biostatistics
Johns Hopkins University
Wednesday, October 15, 2003, 4-5 p.m.
1221 Computer Science and Statistics Center
Recurrent events are frequently observed in many fields and serve as important measurements for evaluating disease progression, health deterioration, or insurance plans. The intensity function of a recurrent event process is known as the occurrence probability density conditional on event history. In contrast with the conditional interpretation of the intensity function, the rate function is defined as the occurrence probability density unconditional on the event history. In this talk the 'shape' and 'size' parameters of the rate function are introduced to characterize, model and analyze recurrent event data. Particular interests will focus on informative censoring models in two different situations: 1) informative censoring as a nuisance, 2) informative censoring generated mainly or partly by a failure event, and the joint relationship between the recurrent event process and the failure time is of interest. Nonparametric and semiparametric methods will be constructed via the estimation of the shape and size parameters in one-sample and regression models. The estimation results obtained here provide a robust approach as compared with the 'risk-set method' of counting process which is valid only for independent censoring models. Analyses based on two data examples will be presented to illustrate the proposed methods.
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