IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v57y2013i1p570-588.html
   My bibliography  Save this article

Stochastic models for multiple pathways of temporal natural history on co-morbidity of chronic disease

Author

Listed:
  • Yen, Amy Ming-Fang
  • Chen, Hsiu-Hsi

Abstract

Chronic diseases frequently co-occur in individuals. Susceptibility to co-morbidity, the temporal sequence and the transition rates governing the development of co-morbid diseases are often hidden or partially observable. To tackle these thorny issues we developed a series of co-morbidity stochastic models with latent variables to estimate the true proportions of susceptibility, temporal sequence, and transition rates. We begin with a bivariate co-morbidity model for two chronic diseases, then extend to a trivariate co-morbidity model for three chronic diseases, and to a generalized high-order co-morbidity model to accommodate more than three chronic diseases. To illustrate our approach we fitted the proposed model with data from a population-based health check-up for hypertension, diabetes mellitus (DM), and overweight in Matsu.

Suggested Citation

  • Yen, Amy Ming-Fang & Chen, Hsiu-Hsi, 2013. "Stochastic models for multiple pathways of temporal natural history on co-morbidity of chronic disease," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 570-588.
  • Handle: RePEc:eee:csdana:v:57:y:2013:i:1:p:570-588
    DOI: 10.1016/j.csda.2012.07.009
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167947312002836
    Download Restriction: Full text for ScienceDirect subscribers only.

    As the access to this document is restricted, you may want to search for a different version of it.

    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:eee:csdana:v:57:y:2013:i:1:p:570-588. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Dana Niculescu). General contact details of provider: http://www.elsevier.com/locate/csda .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.