IDEAS home Printed from https://ideas.repec.org/a/bla/jorssc/v57y2008i5p553-566.html
   My bibliography  Save this article

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

Author

Listed:
  • Rinku Sutradhar
  • Richard J. Cook

Abstract

Summary. 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.

Suggested Citation

  • 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, December.
    • Handle: RePEc:bla:jorssc:v:57:y:2008:i:5:p:553-566
    DOI: 10.1111/j.1467-9876.2008.00630.x
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/j.1467-9876.2008.00630.x
    Download Restriction: no
    File URL: https://libkey.io/10.1111/j.1467-9876.2008.00630.x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Daewoo Pak & Chenxi Li & David Todem & Woosung Sohn, 2017. "A multistate model for correlated interval-censored life history data in caries research," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(2), pages 413-423, February.
    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, August.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Richard J. Cook & Grace Y. Yi & Ker-Ai Lee & Dafna D. Gladman, 2004. "A Conditional Markov Model for Clustered Progressive Multistate Processes under Incomplete Observation," Biometrics, The International Biometric Society, vol. 60(2), pages 436-443, June.
    2. Enrico Fabrizi, 2009. "The Determinants of Labour Market Transitions," Giornale degli Economisti, GDE (Giornale degli Economisti e Annali di Economia), Bocconi University, vol. 68(2), pages 233-265, July.
    3. Jacobo de Uña-Álvarez & Luís Meira-Machado, 2015. "Nonparametric estimation of transition probabilities in the non-Markov illness-death model: A comparative study," Biometrics, The International Biometric Society, vol. 71(2), pages 364-375, June.
    4. Na Cui & Yuguo Chen & Dylan S. Small, 2013. "Modeling Parasite Infection Dynamics when there Is Heterogeneity and Imperfect Detectability," Biometrics, The International Biometric Society, vol. 69(3), pages 683-692, September.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bla:jorssc:v:57:y:2008:i:5:p:553-566. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/rssssea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.