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Semiparametric Transformation Models for Panel Count Data with Dependent Observation Process

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Abstract

This paper presents a class of semiparametric transformation models for regression analysis of panel count data when the observation times or process may differ from subject to subject and more importantly, may contain relevant information about the underlying recurrent event. The models are much more flexible than the existing ones and include many commonly used models as special cases. For estimation of regression parameters, some estimating equations are developed and the resulting estimators are shown to be consistent and asymptotically normal. An extensive simulation study was conducted and indicates that the proposed approach works well for practical situations. An illustrative example is provided.

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Authors and Affiliations

  1. Institute of Applied Mathematics, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, P.R. China

    Liuquan Sun

  2. Department of Statistics, University of Missouri, 146 Middlebush Hall, Columbia, MO, 65211, USA

    Ni Li & Jianguo Sun

Authors
  1. Ni Li
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  2. Liuquan Sun
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  3. Jianguo Sun
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Correspondence to Jianguo Sun.

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Li, N., Sun, L. & Sun, J. Semiparametric Transformation Models for Panel Count Data with Dependent Observation Process. Stat Biosci 2, 191–210 (2010). https://doi.org/10.1007/s12561-010-9029-7

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  • DOI: https://doi.org/10.1007/s12561-010-9029-7

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