A flexible semiparametric model for analyzing longitudinal panel count data is presented. Panel count data refers here to count data on recurrent events collected as the number of events which have occurred within specific followup periods. The model assumes that the counts for each subject are generated by a nonhomogeneous Poisson process with a smooth intensity function. Such smooth intensities are modeled with adaptive splines. Both random and discrete mixtures of intensities are considered to account for complex correlation structures, heterogeneity and hidden subpopulations common to this type of data. An estimating equation approach to inference requiring only low moment assumptions is developed and the method is illustrated on several data sets.
Group for Research in Decision Analysis