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Semiparametric Transformation Rate Model for Recurrent Event Data

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Abstract

In this article, we propose a class of semiparametric transformation rate models for recurrent event data subject to right censoring and potentially stopped by a terminating event (e.g., death). These transformation models include both additive rates model and proportional rates model as special cases. Respecting the property that no recurrent events can occur after the terminating event, we model the conditional recurrent event rate given survival. Weighted estimating equations are constructed to estimate the regression coefficients and baseline rate function. In particular, the baseline rate function is approximated by wavelet function. Asymptotic properties of the proposed estimators are derived and a data-dependent criterion is proposed for selecting the most suitable transformation. Simulation studies show that the proposed estimators perform well for practical sample sizes. The proposed methods are used in two real-data examples: a randomized trial of rhDNase and a community trial of vitamin A.

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Authors and Affiliations

  1. Department of Biostatistics, University of North Carolina, CB# 7420, Chapel Hill, NC, 27599-7420, USA

    Donglin Zeng & Jianwen Cai

  2. Department of Biostatistics, University of Michigan, Ann Arbor, MI, 48109-2029, USA

    Douglas E. Schaubel

Authors
  1. Donglin Zeng
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  2. Douglas E. Schaubel
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  3. Jianwen Cai
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Correspondence to Jianwen Cai.

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Zeng, D., Schaubel, D.E. & Cai, J. Semiparametric Transformation Rate Model for Recurrent Event Data. Stat Biosci 3, 187–207 (2011). https://doi.org/10.1007/s12561-011-9043-4

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  • DOI: https://doi.org/10.1007/s12561-011-9043-4

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