Advanced Search

Journal Navigation

Journal Home

Subscriptions

Archive

Contact Us

Table of Contents

SAGETRACK

Sign In to gain access to subscriptions and/or personal tools.
Medical Decision Making
This Article
Right arrow Full Text (PDF)
Right arrow References
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Right arrow Citation Map
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to Saved Citations
Right arrow Download to citation manager
Right arrowRequest Permissions
Right arrow Request Reprints
Right arrow Add to My Marked Citations
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via Google Scholar
Right arrow Citing Articles via Scopus
Google Scholar
Right arrow Articles by Zaric, G. S.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Zaric, G. S.
Social Bookmarking
Add to CiteULike   Add to Complore   Add to Connotea   Add to Del.icio.us   Add to Digg   Add to Reddit   Add to Technorati   Add to Twitter  
What's this?

The Impact of Ignoring Population Heterogeneity when Markov Models are Used in Cost-Effectiveness Analysis

Gregory S. Zaric, PhD

Richard Ivey School of Business, University of Western Ontario, London, Ontario, N6A 3K7 Canada, gzaric{at}ivey.uwo.ca

Many factors related to the spread and progression of diseases vary throughout a population. This heterogeneity is frequently ignored in cost-effectiveness analyses by using aver-age or representative values or by considering multiple risk groups. The author explores the impact that such simplifying assumptions may have on the results and interpretation of cost-effectiveness analyses when Markov models are used to calculate the costs and health impact of interventions. A discrete-time Markov model for a disease is defined, and 5 potential interventions are considered. Health benefits, costs, and incremental cost-effectiveness ratios are calculated for each intervention. It is assumed that the population is heterogeneous with respect to the probability of becoming sick. Ignoring this heterogeneity may lead to optimistic or pessimistic estimates of cost-effectiveness ratios, depending on the intervention and, in some cases, the parameter values. Implications are discussed of this finding on the use of league tables and on comparisons of cost-effectiveness ratios versus commonly accepted threshold values.

Key Words: technology assessment • program evaluation • cost-benefit analysis • pharmaceutical economics

Medical Decision Making, Vol. 23, No. 5, 379-386 (2003)
DOI: 10.1177/0272989X03256883
Add to CiteULike CiteULike   Add to Complore Complore   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us   Add to Digg Digg   Add to Reddit Reddit   Add to Technorati Technorati   Add to Twitter Twitter    What's this?


This article has been cited by other articles:


Home page
 Med Decis MakingHome page
T. G. K. Bentley, M. C. Weinstein, and K. M. Kuntz
Effects of Categorizing Continuous Variables in Decision-Analytic Models
Med Decis Making, September 1, 2009; 29(5): 549 - 556.
[Abstract] [PDF]

Home page
 Med Decis MakingHome page
H. Koffijberg, G. Rinkel, and E. Buskens
Do Intraindividual Variation in Disease Progression and the Ensuing Tight Window of Opportunity Affect Estimation of Screening Benefits?
Med Decis Making, January 1, 2009; 29(1): 82 - 90.
[Abstract] [PDF]