Abstract
Cost effectiveness analysis compares the differential costs and outcomes of policy strategies competing for the implementation of a program. Outcomes are measured by arithmetical or physical units instead of by an equivalent money value (Sect. 10.1). Indicators of cost effectiveness are the incremental cost effectiveness ratio and its generalization as a function of collective willingness to pay, i.e. the incremental net benefit (Sect. 10.2). Beyond the pairwise comparison of strategies through those indicators, the efficiency frontier identifies policy options subject to simple and extended dominance, and selects the efficient ones (Sect. 10.3). Cost and outcome data is usually obtained from decision analytic modeling, here Markov models (Sect. 10.4). Section 10.5 provides a numerical example in R-CRAN. Cost effectiveness analysis is notably used in public health economics where the health outcome of a medical intervention is assessed by quality-adjusted life years (Sect. 10.6). Section 10.7 debates the uncertainty surrounding decision analytic models and uses Monte Carlo simulations to address parameter uncertainty. Section 10.8 shows how to analyze simulation outputs on the differential cost-effectiveness plane and with cost effectiveness acceptability curves, with self-contained R-CRAN programs.
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Josselin, JM., Le Maux, B. (2017). Cost Effectiveness Analysis. In: Statistical Tools for Program Evaluation . Springer, Cham. https://doi.org/10.1007/978-3-319-52827-4_10
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