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Analysis of interval-censored data from clustered multistate processes: application to joint damage in psoriatic arthritis


  • Rinku Sutradhar
  • Richard J. Cook


A conditionally Markov multiplicative intensity model is described for the analysis of clustered progressive multistate processes under intermittent observation. The model is motivated by a long-term prospective study of patients with psoriatic arthritis with the aim of characterizing progression of joint damage via an irreversible four-state model. The model accommodates heterogeneity in transition rates between different individuals and correlation in transition rates within patients. To do this we introduce subject-specific multivariate random effects in which each component acts multiplicatively on a specific transition intensity. Through the association between the components of the random effect, correlations in transition intensities are accommodated. A Monte Carlo EM algorithm is developed for estimation, which features closed form expressions for estimators at each M-step. Copyright (c) 2008 Royal Statistical Society.

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  • Rinku Sutradhar & Richard J. Cook, 2008. "Analysis of interval-censored data from clustered multistate processes: application to joint damage in psoriatic arthritis," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 57(5), pages 553-566.
  • Handle: RePEc:bla:jorssc:v:57:y:2008:i:5:p:553-566

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    References listed on IDEAS

    1. Richard J. Cook, 1999. "A Mixed Model for Two-State Markov Processes Under Panel Observation," Biometrics, The International Biometric Society, vol. 55(3), pages 915-920, September.
    2. Glen A. Satten, 1999. "Estimating the Extent of Tracking in Interval-Censored Chain-Of-Events Data," Biometrics, The International Biometric Society, vol. 55(4), pages 1228-1231, December.
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    1. repec:bla:jorssc:v:66:y:2017:i:2:p:413-423 is not listed on IDEAS
    2. Jialiang Li & Shuangge Ma, 2010. "Interval-censored data with repeated measurements and a cured subgroup," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 59(4), pages 693-705.

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