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Estimating the Expected Value of Sample Information across Different Sample Sizes Using Moment Matching and Nonlinear Regression

Heath, A; Manolopoulou, I; Baio, G; (2019) Estimating the Expected Value of Sample Information across Different Sample Sizes Using Moment Matching and Nonlinear Regression. Medical Decision Making 10.1177/0272989X19837983. (In press). Green open access

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Abstract

BACKGROUND: The expected value of sample information (EVSI) determines the economic value of any future study with a specific design aimed at reducing uncertainty about the parameters underlying a health economic model. This has potential as a tool for trial design; the cost and value of different designs could be compared to find the trial with the greatest net benefit. However, despite recent developments, EVSI analysis can be slow, especially when optimizing over a large number of different designs. METHODS: This article develops a method to reduce the computation time required to calculate the EVSI across different sample sizes. Our method extends the moment-matching approach to EVSI estimation to optimize over different sample sizes for the underlying trial while retaining a similar computational cost to a single EVSI estimate. This extension calculates the posterior variance of the net monetary benefit across alternative sample sizes and then uses Bayesian nonlinear regression to estimate the EVSI across these sample sizes. RESULTS: A health economic model developed to assess the cost-effectiveness of interventions for chronic pain demonstrates that this EVSI calculation method is fast and accurate for realistic models. This example also highlights how different trial designs can be compared using the EVSI. CONCLUSION: The proposed estimation method is fast and accurate when calculating the EVSI across different sample sizes. This will allow researchers to realize the potential of using the EVSI to determine an economically optimal trial design for reducing uncertainty in health economic models. LIMITATIONS: Our method involves rerunning the health economic model, which can be more computationally expensive than some recent alternatives, especially in complex models.

Type: Article
Title: Estimating the Expected Value of Sample Information across Different Sample Sizes Using Moment Matching and Nonlinear Regression
Location: United States
Open access status: An open access version is available from UCL Discovery
DOI: 10.1177/0272989X19837983
Publisher version: https://doi.org/10.1177%2F0272989X19837983
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Expected value of sample information, health economic evaluation, nonlinear regression, trial design, value of information
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science
URI: https://discovery.ucl.ac.uk/id/eprint/10076043
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