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Estimating the expected value of partial perfect information in health economic evaluations using integrated nested Laplace approximation.

ABSTRACT: The Expected Value of Perfect Partial Information (EVPPI) is a decision-theoretic measure of the 'cost' of parametric uncertainty in decision making used principally in health economic decision making. Despite this decision-theoretic grounding, the uptake of EVPPI calculations in practice has been slow. This is in part due to the prohibitive computational time required to estimate the EVPPI via Monte Carlo simulations. However, recent developments have demonstrated that the EVPPI can be estimated by non-parametric regression methods, which have significantly decreased the computation time required to approximate the EVPPI. Under certain circumstances, high-dimensional Gaussian Process (GP) regression is suggested, but this can still be prohibitively expensive. Applying fast computation methods developed in spatial statistics using Integrated Nested Laplace Approximations (INLA) and projecting from a high-dimensional into a low-dimensional input space allows us to decrease the computation time for fitting these high-dimensional GP, often substantially. We demonstrate that the EVPPI calculated using our method for GP regression is in line with the standard GP regression method and that despite the apparent methodological complexity of this new method, R functions are available in the package BCEA to implement it simply and efficiently. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

SUBMITTER: Heath A

PROVIDER: S-EPMC5031203 | biostudies-literature | 2016 Oct

REPOSITORIES: biostudies-literature

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Estimating the expected value of partial perfect information in health economic evaluations using integrated nested Laplace approximation.

Heath Anna A Manolopoulou Ioanna I Baio Gianluca G

Statistics in medicine 20160518 23

The Expected Value of Perfect Partial Information (EVPPI) is a decision-theoretic measure of the 'cost' of parametric uncertainty in decision making used principally in health economic decision making. Despite this decision-theoretic grounding, the uptake of EVPPI calculations in practice has been slow. This is in part due to the prohibitive computational time required to estimate the EVPPI via Monte Carlo simulations. However, recent developments have demonstrated that the EVPPI can be estimate ...[more]

PMID: 27189534

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Project description:BackgroundAntibiotics designed to decolonize carriers of drug-resistant organisms could offer substantial population health benefits, particularly if they can help avert outbreaks by interrupting person-to-person transmission chains. However, cost effectiveness of an antibiotic is typically evaluated only according to its benefits to recipients, which can be difficult to demonstrate for carriers of an organism that may not pose an immediate health threat to the carrier.MethodsWe developed a mathematical transmission model to quantify the effects of 2 hypothetical antibiotics targeting carbapenem-resistant Enterobacteriaceae (CRE) among long-term acute care hospital inpatients: one assumed to decrease the death rate of patients with CRE bloodstream infections (BSIs) and the other assumed to decolonize CRE carriers after clinical detection. We quantified the effect of each antibiotic on the number of BSIs and deaths among patients receiving the drug (direct effect) and among all patients (direct and indirect effect) compared to usual care. We applied these results to a cost-effectiveness analysis with effectiveness outcome of life-years gained and assumed costs for antibiotic doses and for CRE BSI.ResultsThe decolonizing antibiotic, once indirect effects were included, produced increased relative effectiveness and decreased relative costs compared to both usual care and the BSI treatment antibiotic. In fact, in most scenarios, the decolonizing drug was the dominant treatment strategy (ie, less costly and more effective).ConclusionsAntibiotics that decolonize carriers of drug-resistant organisms can be highly cost-effective when considering indirect benefits within populations vulnerable to outbreaks. Public health could benefit from finding ways to incentivize development of decolonizing antibiotics in the US, where drugs with unclear direct benefits to recipients would pose difficulties in achieving FDA approval and financial benefit to the developer.

Dataset Information

Estimating the expected value of partial perfect information in health economic evaluations using integrated nested Laplace approximation.

Publications

Estimating the expected value of partial perfect information in health economic evaluations using integrated nested Laplace approximation.

Similar Datasets

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