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Annals of Data Science

19.03.2024

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

verfasst von: Mathew P. M. Ashlin, P. G. Sankaran, E. P. Sreedevi

Erschienen in: Annals of Data Science

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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.

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Titel: Semiparametric Regression Analysis of Panel Count Data with Multiple Modes of Recurrence
verfasst von: Mathew P. M. Ashlin
P. G. Sankaran
E. P. Sreedevi
Publikationsdatum: 19.03.2024
Verlag: Springer Berlin Heidelberg
Erschienen in: Annals of Data Science
Print ISSN: 2198-5804
Elektronische ISSN: 2198-5812
DOI: https://doi.org/10.1007/s40745-024-00522-7

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