Abstract
Panel count data arise when recurrent events experienced by the study subjects are recorded only at discrete observation times. In this article, we focus on the regression analysis of panel count data with multiple modes of recurrence. We introduce a proportional mean model to assess the effects of covariates on the underlying counting processes corresponding to different recurrence modes. The methodology includes the joint estimation of baseline cumulative cause-specific mean functions and regression coefficients. We also establish the asymptotic properties of the proposed estimators. A Monte Carlo simulation study is conducted to examine the performance of the proposed estimators in finite samples. We illustrate the practical applicability of the procedures using two real-life data sets.
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