Title: Inverse probability weighting methods for the analysis of panel count data with informative observation times
Authors: Ni Li - School of Mathematics and Statistics, Hainan Normal University (China) [presenting]
Abstract: Recurrent event data usually occur in long-term studies which concern recurrence rates of certain events. In some circumstances of these studies, subjects can only be observed at discrete time points rather than continuously and thus only the numbers of the events that occur between the observation times, not their occurrence times, are observed. This type of data can also be referred to as interval-censored recurrent event data, or panel count data. In panel count data, the observation times or process may differ from subject to subject and more importantly, may contain relevant information about the underlying recurrent event process, therefore can be viewed as dependent observation process. Methods have been proposed for regression analysis of panel count data, but most of the existing research focuses on situations where observation times are independent of longitudinal response variables given covariates. However, the independence assumption may not hold. We propose semiparametric analysis of panel count data with adjusting for confounding effects caused by dependent observation process. In our approach, the observation filtration will be adjusted by parametric estimates of propensity scores using the idea of inverse probability weighting, to avoid confounding bias produced. The results of this research could serve as new methodologies for analyzing panel count data with informative observation times.