Markov Chains with Measurement Error: Estimating the ‘True’ Course of a Marker of the Progression of Human Immunodeficiency Virus Disease

Glen A. Satten and Ira M. Longini

Journal of the Royal Statistical Society Series C, 1996, vol. 45, issue 3, 275-295

Abstract: A Markov chain is a useful way of describing cohort data Longitudinal observations of a marker of the progression of the human immunodeficiency virus (HIV), such as CD4 cell count, measured on members of a cohort study, can be analysed as a continuous time Markov chain by categorizing the CD4 cell counts into stages. Unfortunately, CD4 cell counts are subject to substantial measurement error and short timescale variability. Thus, fitting a Markov chain to raw CD4 cell count measurements does not determine the transition probabilities for the true or underlying CD4 cell counts; the measurement error results in a process that is too rough. Assuming independent measurement errors, we propose a likelihood‐based method for estimating the 'true'or underlying transition probabilities. The Markov structure allows efficient calculation of the likelihood by using hidden Markov model methodology. As an example, we consider CD4 cell count data from 430 HIV‐infected participants in the San Francisco Men's Health Study by categorizing the marker data into seven stages; up to 17 observations are available for each individual. We find that including measurement error both produces a significantly better fit and provides a model for CD4 progression that is more biologically reasonable.

Date: 1996
References: Add references at CitEc
Citations: View citations in EconPapers (10)

Downloads: (external link)
https://doi.org/10.2307/2986089

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:bla:jorssc:v:45:y:1996:i:3:p:275-295

Ordering information: This journal article can be ordered from
http://ordering.onli ... 1111/(ISSN)1467-9876

Access Statistics for this article

Journal of the Royal Statistical Society Series C is currently edited by R. Chandler and P. W. F. Smith

More articles in Journal of the Royal Statistical Society Series C from Royal Statistical Society Contact information at EDIRC.
Bibliographic data for series maintained by Wiley Content Delivery ().