SEMIPARAMETRIC REGRESSION-ANALYSIS FOR RECURRENT EVENT INTERVAL COUNTS

This paper deals with analysis of data from longitudinal studies where the rate of a recurrent event characterizing morbidity is the primary criterion for treatment-evaluation. We consider clinical trials which require patients to visit their clinical center at successive schedul ed times as part of follow-up. At each visit, the patient reports the number of events that occurred since the previous visit, or an examina tion reveals the number of accumulated events, such as skin cancers. T he exact occurrence times of the events are unavailable and the actual patient visit times typically vary randomly about the scheduled follo w-up times. Each patient's record thus consists of a sequence of clini c visit dates, event counts corresponding to the successive time inter vals between clinic visits, and baseline covariates. We propose a semi parametric regression model, extending the fully parametric model of T hall (1988, Biometrics 44, 197-209), to estimate and test for covariat e effects on the rate of events over time while also accounting for th e possibly time-varying nature of the underlying event rate. Covariate effects enter the model parametrically, while the underlying time-var ying event rate is modelled nonparametrically. The method of Severini and Wong (1992, Annals of Statistics 20, 1768-1802) is used to constru ct asymptotically efficient estimators of the parametric component and to specify their asymptotic distribution. A simulation study and appl ication to a data set are provided.