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Semiparametric analysis of recurrent events: artificial censoring, truncation, pairwise estimation and inference

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Abstract

The analysis of recurrent failure time data from longitudinal studies can be complicated by the presence of dependent censoring. There has been a substantive literature that has developed based on an artificial censoring device. We explore in this article the connection between this class of methods with truncated data structures. In addition, a new procedure is developed for estimation and inference in a joint model for recurrent events and dependent censoring. Estimation proceeds using a mixed U-statistic based estimating function approach. New resampling-based methods for variance estimation and model checking are also described. The methods are illustrated by application to data from an HIV clinical trial as with a limited simulation study.

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Authors and Affiliations

  1. Department of Statistics and Public Health Sciences, Penn State University, 514A Wartik Lab, University Park, PA, 16802, USA

    Debashis Ghosh

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  1. Debashis Ghosh
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Correspondence to Debashis Ghosh.

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Ghosh, D. Semiparametric analysis of recurrent events: artificial censoring, truncation, pairwise estimation and inference. Lifetime Data Anal 16, 509–524 (2010). https://doi.org/10.1007/s10985-009-9150-4

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  • DOI: https://doi.org/10.1007/s10985-009-9150-4

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