Abstract
BackgroundThis article addresses the choice of state structure in a cost-effectiveness multi-state model. Key model outputs, such as treatment recommendations and prioritisation of future research, may be sensitive to state structure choice. For example, it may be uncertain whether to consider similar disease severities or similar clinical events as the same state or as separate states. Standard statistical methods for comparing models require a common reference dataset but merging states in a model aggregates the data, rendering these methods invalid.MethodsWe propose a method that involves re-expressing a model with merged states as a model on the larger state space in which particular transition probabilities, costs and utilities are constrained to be equal between states. This produces a model that gives identical estimates of cost effectiveness to the model with merged states, while leaving the data unchanged. The comparison of state structures can be achieved by comparing maximised likelihoods or information criteria between constrained and unconstrained models. We can thus test whether the costs and/or health consequences for a patient in two states are the same, and hence if the states can be merged. We note that different structures can be used for rates, costs and utilities, as appropriate.ApplicationWe illustrate our method with applications to two recent models evaluating the cost effectiveness of prescribing anti-depressant medications by depression severity and the cost effectiveness of diagnostic tests for coronary artery disease.ConclusionsState structures in cost-effectiveness models can be compared using standard methods to compare constrained and unconstrained models.Electronic supplementary materialThe online version of this article (doi:10.1007/s40273-017-0501-9) contains supplementary material, which is available to authorized users.
Highlights
Health economic evaluations rely on cost-effectiveness models, such as Markov multi-state models [1], to produce accurate comparative assessments of the costs and health effects of different interventions for the management of disease
Research recommendations can be guided by the expected value of perfect information (EVPI) and expected value of partial perfect information, comparing the benefits in terms of costs and monetised health effects gained from a decision based on evidence, where parameter uncertainty is removed or reduced, with that based on current evidence [4]
The costs and utilities used for the medium- and high-risk states in the economic model were estimated from the subset of patients in the Cost-Effectiveness of non-invasive Cardiac Testing (CECaT) trial whose coronary artery disease (CAD) severity was known
Summary
We propose a method that involves re-expressing a model with merged states as a model on the larger state space in which particular transition probabilities, costs and utilities are constrained to be equal between states. This produces a model that gives identical estimates of cost effectiveness to the model with merged states, while leaving the data unchanged. The original version of this article was revised due to a retrospective Open Access. Electronic supplementary material The online version of this article (doi:10.1007/s40273-017-0501-9) contains supplementary material, which is available to authorized users
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