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Non-Markov Multistate Modeling Using Time-Varying Covariates, with Application to Progression of Liver Fibrosis due to Hepatitis C Following Liver Transplant

Author

Listed:
  • Bacchetti Peter

    (University of California, San Francisco)

  • Boylan Ross D

    (University of California, San Francisco)

  • Terrault Norah A

    (University of California, San Francisco)

  • Monto Alexander

    (University of California, San Francisco)

  • Berenguer Marina

    (Hospital Universitario La Fe)

Abstract

Multistate modeling methods are well-suited for analysis of some chronic diseases that move through distinct stages. The memoryless or Markov assumptions typically made, however, may be suspect for some diseases, such as hepatitis C, where there is interest in whether prognosis depends on history. This paper describes methods for multistate modeling where transition risk can depend on any property of past progression history, including time spent in the current stage and the time taken to reach the current stage. Analysis of 901 measurements of fibrosis in 401 patients following liver transplantation found decreasing risk of progression as time in the current stage increased, even when controlled for several fixed covariates. Longer time to reach the current stage did not appear associated with lower progression risk. Analysis of simulation scenarios based on the transplant study showed that greater misclassification of fibrosis produced more technical difficulties in fitting the models and poorer estimation of covariate effects than did less misclassification or error-free fibrosis measurement. The higher risk of progression when less time has been spent in the current stage could be due to varying disease activity over time, with recent progression indicating an "active" period and consequent higher risk of further progression.

Suggested Citation

  • Bacchetti Peter & Boylan Ross D & Terrault Norah A & Monto Alexander & Berenguer Marina, 2010. "Non-Markov Multistate Modeling Using Time-Varying Covariates, with Application to Progression of Liver Fibrosis due to Hepatitis C Following Liver Transplant," The International Journal of Biostatistics, De Gruyter, vol. 6(1), pages 1-16, February.
    • Handle: RePEc:bpj:ijbist:v:6:y:2010:i:1:n:7
    DOI: 10.2202/1557-4679.1213
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    References listed on IDEAS

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    1. Haiqun Lin & Zhenchao Guo & Peter N. Peduzzi & Thomas M. Gill & Heather G. Allore, 2008. "A Semiparametric Transition Model with Latent Traits for Longitudinal Multistate Data," Biometrics, The International Biometric Society, vol. 64(4), pages 1032-1042, December.
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    Cited by:

    1. Juan Eloy Ruiz-Castro & Mariangela Zenga, 2020. "A general piecewise multi-state survival model: application to breast cancer," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 29(4), pages 813-843, December.
    2. Andrew C. Titman, 2011. "Flexible Nonhomogeneous Markov Models for Panel Observed Data," Biometrics, The International Biometric Society, vol. 67(3), pages 780-787, September.

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