A comparison of non-homogeneous Markov regression models with application to Alzheimer's disease progression

R. A. Hubbard and X. H. Zhou

Journal of Applied Statistics, 2011, vol. 38, issue 10, 2313-2326

Abstract: Markov regression models are useful tools for estimating risk factor effects on transition rates between multiple disease states. Alzheimer's disease (AD) is an example of a multi-state disease process where great interest lies in identifying risk factors for transition. In this context, non-homogeneous models are required because transition rates change as subjects age. In this report we propose a non-homogeneous Markov regression model that allows for reversible and recurrent states, transitions among multiple states between observations, and unequally spaced observation times. We conducted simulation studies to compare performance of estimators for covariate effects from this model and alternative models when the underlying non-homogeneous process was correctly specified and under model misspecification. In simulation studies, we found that covariate effects were biased if non-homogeneity of the disease process was not accounted for. However, estimates from non-homogeneous models were robust to misspecification of the form of the non-homogeneity. We used our model to estimate risk factors for transition to mild cognitive impairment (MCI) and AD in a longitudinal study of subjects included in the National Alzheimer's Coordinating Center's Uniform Data Set. We found that subjects with MCI affecting multiple cognitive domains were significantly less likely to revert to normal cognition.

Keywords: Alzheimer's disease; interval censoring; Markov process; mild cognitive impairment; non-homogeneous; panel data (search for similar items in EconPapers)
Date: 2011
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.tandfonline.com/doi/abs/10.1080/02664763.2010.547567 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:japsta:v:38:y:2011:i:10:p:2313-2326

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/CJAS20

DOI: 10.1080/02664763.2010.547567

Access Statistics for this article

Journal of Applied Statistics is currently edited by Robert Aykroyd

More articles in Journal of Applied Statistics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().