EconPapers    
Economics at your fingertips  
 

Effects of Categorizing Continuous Variables in Decision-Analytic Models

Tanya G. K. Bentley, Milton C. Weinstein and Karen M. Kuntz
Additional contact information
Tanya G. K. Bentley: Faculty of Arts and Sciences, Harvard University, Cambridge, Massachusetts
Milton C. Weinstein: Department of Health Policy and Management, Harvard School of Public Health, Boston, Massachusetts
Karen M. Kuntz: Department of Health Policy and Management, Harvard School of Public Health, Boston, Massachusetts, kmkuntz@umn.edu

Medical Decision Making, 2009, vol. 29, issue 5, 549-556

Abstract: Purpose. When using continuous predictor variables in discrete-state Markov modeling, it is necessary to create categories of risk and assume homogeneous disease risk within categories, which may bias model outcomes. This analysis assessed the tradeoffs between model bias and complexity and/or data limitations when categorizing continuous risk factors in Markov models. Methods. The authors developed a generic Markov cohort model of disease, defining bias as the percentage change in life expectancy gain from a hypothetical intervention when using 2 to 15 risk factor categories as compared with modeling the risk factor as a continuous variable. They evaluated the magnitude and sign of bias as a function of disease incidence, disease-specific mortality, and relative difference in risk among categories. Results. Bias was positive in the base case, indicating that categorization overestimated life expectancy gains. The bias approached zero as the number of risk factor categories increased and did not exceed 4% for any parameter combinations or numbers of categories considered. For any given disease-specific mortality and disease incidence, bias increased with relative risk of disease. For any given relative risk, the relationship between bias and parameters such as disease-specific mortality or disease incidence was not always monotonic. Conclusions. Under the assumption of a normally distributed risk factor and reasonable assumption regarding disease risk and moderate values for the relative risk of disease given risk factor category, categorizing continuously valued risk factors in Markov models is associated with less than 4% absolute bias when at least 2 categories are used.

Keywords: Markov models; Monte Carlo models; bias. (search for similar items in EconPapers)
Date: 2009
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://journals.sagepub.com/doi/10.1177/0272989X09340238 (text/html)

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:sae:medema:v:29:y:2009:i:5:p:549-556

DOI: 10.1177/0272989X09340238

Access Statistics for this article

More articles in Medical Decision Making
Bibliographic data for series maintained by SAGE Publications ().

 
Page updated 2025-03-19
Handle: RePEc:sae:medema:v:29:y:2009:i:5:p:549-556