Inference of a time-varying coefficient regression model for multivariate panel count data

Abstract

Panel count data consist of the numbers of event occurrences between two consecutive observation times and are prevalent in many areas. Correspondingly a bulk of literature has been developed for the analysis of panel count data with time-independent or time-dependent covariates but assuming time-invariant covariate effects. However, the time-invariant coefficient assumption is too restrictive in reality and fails to represent the time-dynamic association between the covariates and event occurrence rates. In this paper, we discuss regression analysis of multivariate panel count data where both the covariates and their effects may be time-varying. We propose a marginal estimating equation approach combined with the B-splines that approximate the functional forms of the regression coefficients. The asymptotic properties of the proposed estimators are rigorously established. A simulation study is conducted to assess the performance of the proposed estimation procedure and suggests that it works well for practical situations. The proposed methodology is applied to a real dataset that motivated this study.