Semiparametric Regression Analysis of Panel Count Data with Multiple Modes of Recurrence

Mathew P. M. Ashlin (), P. G. Sankaran () and E. P. Sreedevi ()
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Mathew P. M. Ashlin: St.Thomas College (Autonomous)
P. G. Sankaran: Cochin Univerisity of Science and Technology
E. P. Sreedevi: Cochin Univerisity of Science and Technology

Annals of Data Science, 2025, vol. 12, issue 2, No 7, 590 pages

Abstract: Abstract Panel count data refers to the information collected in studies focusing on recurrent events, where subjects are observed only at specific time points. If these study subjects are exposed to recurrent events of several types, we obtain panel count data with multiple modes of recurrence. In this article, we present a novel method based on generalized estimating equations for the regression analysis of panel count data exposed to multiple modes of recurrence. A cause specific proportional mean model is developed to analyze the effect of covariates on the underlying counting process due to multiple modes of recurrence. We conduct a detailed investigation on the joint estimation of baseline cumulative mean functions and regression parameters. Simulation studies are carried out to evaluate the finite sample performance of the proposed estimators. The procedures are applied to two real data sets, to demonstrate the practical utility.

Keywords: Cause specific mean functions; Counting process; Panel count data; Proportional mean model; Recurrent events; 62N01; 62N03 (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s40745-024-00522-7

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