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Abstract
Background
Most decisions are associated with uncertainty. Value of information (VOI) analysis quantifies the opportunity loss associated with choosing a suboptimal intervention based on current imperfect information. VOI can inform the value of collecting additional information, resource allocation, research prioritization, and future research designs. However, in practice, VOI remains underused due to many conceptual and computational challenges associated with its application. Expected value of sample information (EVSI) is rooted in Bayesian statistical decision theory and measures the value of information from a finite sample. The past few years have witnessed a dramatic growth in computationally efficient methods to calculate EVSI, including metamodeling. However, little research has been done to simplify the experimental data collection step inherent to all EVSI computations, especially for correlated model parameters.
Methods
We propose a general approach to compute EVSI that consists of 2 components: 1) a linear metamodel between θ I and the opportunity loss L and 2) a Gaussian approximation of the posterior mean of the data collection experiments involving a set of parameters of interest θ I. In a first case study, we illustrated the GA performance in terms of accuracy in 4 numerical exercises involving conjugate, nonconjugate, univariate, and multivariate priors. The second case study compared the accuracy and efficiency of the GA to the traditional Bayesian updating via the 2MCS approach in estimating EVPPI and EVSI of an economic evaluation using a Markov model with nonlinear relations between the net benefits and the non-Gaussian prior parameters.
Results
The proposed approach is efficient and can be applied for a wide range of data collection designs involving multiple non-Gaussian parameters and unbalanced study designs. Our approach is particularly useful when the parameters of an economic evaluation are correlated or interact.