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MARKOV MODELING AND DISCRETE EVENT SIMULATION IN HEALTH CARE: A SYSTEMATIC COMPARISON

Part of: Special Collection - Methods

Published online by Cambridge University Press:  28 April 2014

Lachlan Standfield ,
Tracy Comans  and
Paul Scuffham
Lachlan Standfield
Affiliation:
Centre for Applied Health Economics, School of Medicine & Griffith Health Institute, Griffith University
Tracy Comans
Affiliation:
Centre for Applied Health Economics, School of Medicine & Griffith Health Institute, Griffith University
Paul Scuffham
Affiliation:
Centre for Applied Health Economics, School of Medicine & Griffith Health Institute, Griffith University
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Abstract

Objectives: The aim of this study was to assess if the use of Markov modeling (MM) or discrete event simulation (DES) for cost-effectiveness analysis (CEA) may alter healthcare resource allocation decisions.

Methods: A systematic literature search and review of empirical and non-empirical studies comparing MM and DES techniques used in the CEA of healthcare technologies was conducted.

Results: Twenty-two pertinent publications were identified. Two publications compared MM and DES models empirically, one presented a conceptual DES and MM, two described a DES consensus guideline, and seventeen drew comparisons between MM and DES through the authors’ experience. The primary advantages described for DES over MM were the ability to model queuing for limited resources, capture individual patient histories, accommodate complexity and uncertainty, represent time flexibly, model competing risks, and accommodate multiple events simultaneously. The disadvantages of DES over MM were the potential for model overspecification, increased data requirements, specialized expensive software, and increased model development, validation, and computational time.

Conclusions: Where individual patient history is an important driver of future events an individual patient simulation technique like DES may be preferred over MM. Where supply shortages, subsequent queuing, and diversion of patients through other pathways in the healthcare system are likely to be drivers of cost-effectiveness, DES modeling methods may provide decision makers with more accurate information on which to base resource allocation decisions. Where these are not major features of the cost-effectiveness question, MM remains an efficient, easily validated, parsimonious, and accurate method of determining the cost-effectiveness of new healthcare interventions.

Keywords

systematic revieweconomic evaluationMarkovdiscrete event simulationDES
Type
Methods
Information
International Journal of Technology Assessment in Health Care , Volume 30 , Issue 2 , April 2014 , pp. 165 - 172
Copyright
Copyright © Cambridge University Press 2014 

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