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Cause specific rate functions for panel count data with multiple modes of recurrence

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dc.contributor.author Paduthol, Sankaran
dc.contributor.author Mathew, Ashlin P M
dc.contributor.author Sreedevi, E P
dc.date.accessioned 2025-01-17T05:23:54Z
dc.date.available 2025-01-17T05:23:54Z
dc.date.issued 2021-06-03
dc.identifier.citation Semantic Scholar en_US
dc.identifier.uri https://doi.org/10.48550/arXiv.2106.01636 Focus to learn more
dc.identifier.uri http://starc.stthomas.ac.in:8080/xmlui/xmlui/handle/123456789/383
dc.description.abstract Panel count data arise from longitudinal studies on recurrent events where each subject is observed only at discrete time points. If recurrent events of several types are possible, we obtain panel count data with multiple modes of recurrence. Such data is commonly encountered in medical studies, reliability experiments as well as in sociological studies. In this article, we present cause specific rate functions for the analysis of panel count data with multiple modes of recurrence and develop nonparametric estimation procedures for the same. We derive empirical estimators for the cause specific rate functions and also propose a smoothed version of the same estimators using kernel estimation method. Asymptotic properties of the proposed estimators are studied. A simulation study is conducted to assess the performance of the proposed estimators in finite samples. The practical utility of the proposed method is demonstrated using a real life data arising from skin cancer chemo prevention trial given in Sun and Zhao (2013). en_US
dc.language.iso en en_US
dc.publisher Cornell University en_US
dc.subject Recurrent events en_US
dc.subject Panel count data en_US
dc.subject Nonpara- metric estimation en_US
dc.subject Competing risks en_US
dc.subject Cause specific rate functions en_US
dc.subject Kernel estimation en_US
dc.title Cause specific rate functions for panel count data with multiple modes of recurrence en_US
dc.type Article en_US


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