IDEAS home Printed from https://ideas.repec.org/a/sae/medema/v18y1998i1p52-60.html
   My bibliography  Save this article

Fitting Multistate Transition Models with Autoregressive Logistic Regression

Author

Listed:
  • Sybolt O. de Vries
  • Vaclav Fidler
  • Wietze D. Kuipers
  • Maria G.M. Hunink

Abstract

The purpose of this study was to develop a model that predicts the outcome of su pervised exercise for intermittent claudication. The authors present an example of the use of autoregressive logistic regression for modeling observed longitudinal data. Data were collected from 329 participants in a six-month exercise program. The levels of the polytomous outcome variable correspond to states they defined in a Markov de cision model comparing treatment strategies for intermittent claudication. Autoregres sive logistic regression can be used to fit multistate transition models to observed longitudinal data with standard statistical software. The technique allows exploration of alternative assumptions about the dependence in the outcome series and provides transition probabilities for different covariate patterns. Of the alternatives examined, a Markov model including two preceding responses, time, age, ankle brachial index, and duration of disease best described the data. Key words: longitudinal data analysis; autoregressive models; logistic regression; Markov models; peripheral arterial occlu sive disease; intermittent claudication; exercise. (Med Decis Making 1998;18:52- 60)

Suggested Citation

  • Sybolt O. de Vries & Vaclav Fidler & Wietze D. Kuipers & Maria G.M. Hunink, 1998. "Fitting Multistate Transition Models with Autoregressive Logistic Regression," Medical Decision Making, , vol. 18(1), pages 52-60, January.
    • Handle: RePEc:sae:medema:v:18:y:1998:i:1:p:52-60
    DOI: 10.1177/0272989X9801800112
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/0272989X9801800112
    Download Restriction: no
    File URL: https://libkey.io/10.1177/0272989X9801800112?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
    ---><---

    Citations

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


    Cited by:

    1. Zhang, Xinyu & Lu, Zudi & Zou, Guohua, 2013. "Adaptively combined forecasting for discrete response time series," Journal of Econometrics, Elsevier, vol. 176(1), pages 80-91.
    2. Michael Wiberg & Staffan Marklund & Kristina Alexanderson, 2017. "Transitions Between Compensated Work Disability, Joblessness, and Self-Sufficiency: A Cohort Study 1997–2010 of Those Jobless in 1995," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 36(1), pages 85-107, February.

    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:sae:medema:v:18:y:1998:i:1:p:52-60. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: SAGE Publications (email available below). General contact details of provider: .

    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.