| Summary: | Value of Information measures quantify the economic benefit of obtaining additional information about the underlying model parameters of a health economic model. Theoretically, these measures can be used to understand the impact of model uncertainty on health economic decision making. Specifically, the Expected Value of Partial Perfect Information (EVPPI) can be used to determine which model parameters are driving decision uncertainty. This is useful as a tool to perform sensitivity analysis to model assumptions and to determine where future research should be targeted to reduce model uncertainty. Even more importantly, the Value of Information measure known as the Expected Value of Sample Information (EVSI) quantifies the economic value of undertaking a proposed scheme of research. This has clear applications in research prioritisation and trial design, where economically valuable studies should be funded. Despite these useful properties, these two measures have rarely been used in practice due to the large computational burden associated with estimating them in practical scenarios. Therefore, this thesis develops novel methodology to allow these two measures to be calculated in practice. For the EVPPI, the method is based on non-parametric regression using the fast Bayesian computation method INLA (Integrated Nested Laplace Approximations). This novel calculation method is fast, especially for high dimensional problems, greatly reducing the computational time for calculating the EVPPI in many practical settings. For the EVSI, the approximation is based on Moment Matching and using properties of the distribution of the preposterior mean. An extension to this method also uses Bayesian non-linear regression to calculate the EVSI quickly across different trial designs. All these methods have been developed and implemented in R packages to aid implementation by practitioners and allow Value of Information measures to inform both health economic evaluations and trial design.
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