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Joint modeling of failure time data with transformation model and longitudinal data when covariates are measured with errors

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Abstract

Semiparametric transformation models provide a class of flexible models for regression analysis of failure time data. Several authors have discussed them under different situations when covariates are timeindependent (Chen et al., 2002; Cheng et al., 1995; Fine et al., 1998). In this paper, we consider fitting these models to right-censored data when covariates are time-dependent longitudinal variables and, furthermore, may suffer measurement errors. For estimation, we investigate the maximum likelihood approach, and an EM algorithm is developed. Simulation results show that the proposed method is appropriate for practical application, and an illustrative example is provided.

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Authors and Affiliations

  1. School of Applied Science, Beijing Information Science and Technology University, Beijing, 100192, China

    Xi-ming Cheng

  2. Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China

    Xi-ming Cheng

  3. Department of Statistics, University of Missouri, Columbia, MO, USA

    Qi Gong

Authors
  1. Xi-ming Cheng
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  2. Qi Gong
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Correspondence to Xi-ming Cheng.

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Cheng, Xm., Gong, Q. Joint modeling of failure time data with transformation model and longitudinal data when covariates are measured with errors. Acta Math. Appl. Sin. Engl. Ser. 28, 663–672 (2012). https://doi.org/10.1007/s10255-012-0192-0

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  • DOI: https://doi.org/10.1007/s10255-012-0192-0

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