DESCRIPTION (provided by applicant): Abstract This application addresses the development and application of new analytic strategies for mixed recurrent-event and panel-count data. While recurrent-event data and panel-count data are both generated from recurrent-event processes, they have different observation systems. In the former, subjects are observed continuously and in the latter, subjects are observed only at discrete time points. Consequently, recurrent-event data record all occurrence times of recurrent events, while panel-count data record only the events between observation time points. It is possible that in a single cohort, some subjects have recurrent-event data while others have panel-count data, or every subject has recurrent-event data during some periods and has panel-count data during other periods. It is not unusual to have these mixed data in long-term follow-up studies. The current common practice is to approximate or simplify these complex data, resulting in potentially misleading conclusions. There is an urgent need to develop intuitive, efficient, and computationally feasible methods for analyzing complex data in event history studies. This project proposes to use data from the renown longitudinal Childhood Cancer Survivor Study (CCSS) to: 1) develop both a nonparametric estimation of the mean function and a procedure of nonparametric two-sample comparison for these mixed data; 2) Develop a semiparametric estimating equation-based method for a proportional mean model and a semiparametric estimating equation-based method for an additive rate model for regression analysis; and, 3) Extend the methods developed in Aim 2 for multivariate mixed recurrent-event and panel-count data. These approaches potentially have strong statistical and clinical relevance for the study of complex event history data.
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