Choice of Modelling Technique for Evaluating Health Care Interventions
Choice of Modelling Technique for Evaluating Health Care Interventions
Economic evaluation, such as cost effectiveness analysis, provides a method for comparing healthcare interven- tions. These evaluations often use modelling techniques such as decision trees, Markov processes and discrete event simulations (DES). With the aid of examples from coronary heart disease, the use of these techniques in different health care situations is discussed. Guidelines for the choice of modelling technique are developed according to the characteristics of the health care intervention. The choice of modelling technique is shown to depend on the acceptance of the modelling technique, model 'error', model appropriateness, dimensionality and ease and speed of model development. Generally decision trees are suitable for acute interventions but they cannot model recursion and Markov models are suitable for simple chronic interventions. It is further recommended that population based models be used in order to provide health care outcomes for the likely cost, health benefits and cost effectiveness of the intervention. The population approach will complicate the construction of the model. DES will allow the modeller to construct more complex, dynamic and accurate systems but these may involve a corresponding increase in development time and expense. The modeller will need to make a judgement on the necessary complexity of the model in terms of interaction of individuals and model size and whether queuing for resources, resource constraints or the interactions between individuals are significant issues in the health care system
168-176
Cooper, K.
6b37f95e-daae-4b87-9f79-0fabd4eb8f6b
Brailsford, S.C.
634585ff-c828-46ca-b33d-7ac017dda04f
Davies, R.
b3bce148-1213-4c2e-a3bf-a79fe8360f03
1 February 2007
Cooper, K.
6b37f95e-daae-4b87-9f79-0fabd4eb8f6b
Brailsford, S.C.
634585ff-c828-46ca-b33d-7ac017dda04f
Davies, R.
b3bce148-1213-4c2e-a3bf-a79fe8360f03
Cooper, K., Brailsford, S.C. and Davies, R.
(2007)
Choice of Modelling Technique for Evaluating Health Care Interventions.
Journal of the Operational Research Society, 58 (2), .
(doi:10.1057/palgrave.jors.26).
Abstract
Economic evaluation, such as cost effectiveness analysis, provides a method for comparing healthcare interven- tions. These evaluations often use modelling techniques such as decision trees, Markov processes and discrete event simulations (DES). With the aid of examples from coronary heart disease, the use of these techniques in different health care situations is discussed. Guidelines for the choice of modelling technique are developed according to the characteristics of the health care intervention. The choice of modelling technique is shown to depend on the acceptance of the modelling technique, model 'error', model appropriateness, dimensionality and ease and speed of model development. Generally decision trees are suitable for acute interventions but they cannot model recursion and Markov models are suitable for simple chronic interventions. It is further recommended that population based models be used in order to provide health care outcomes for the likely cost, health benefits and cost effectiveness of the intervention. The population approach will complicate the construction of the model. DES will allow the modeller to construct more complex, dynamic and accurate systems but these may involve a corresponding increase in development time and expense. The modeller will need to make a judgement on the necessary complexity of the model in terms of interaction of individuals and model size and whether queuing for resources, resource constraints or the interactions between individuals are significant issues in the health care system
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Published date: 1 February 2007
Additional Information:
Special Issue: Operational Research in Health (Feb., 2007)
Identifiers
Local EPrints ID: 35783
URI: http://eprints.soton.ac.uk/id/eprint/35783
ISSN: 0160-5682
PURE UUID: c009d53c-be97-48c0-8239-dd50cf2600f3
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Date deposited: 10 Jul 2006
Last modified: 12 Nov 2024 02:33
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Author:
K. Cooper
Author:
R. Davies
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