The Curve of Optimal Sample Size (COSS): A Graphical Representation of the Optimal Sample Size from a Value of Information Analysis

Eric Jutkowitz, Fernando Alarid-Escudero (), Karen M. Kuntz and Hawre Jalal
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Eric Jutkowitz: Brown University School of Public Health
Fernando Alarid-Escudero: Center for Research and Teaching in Economics (CIDE)-CONACyT
Karen M. Kuntz: University of Minnesota School of Public Health
Hawre Jalal: University of Pittsburgh

PharmacoEconomics, 2019, vol. 37, issue 7, No 1, 877 pages

Abstract: Abstract Value of information (VOI) analysis quantifies the opportunity cost associated with decision uncertainty, and thus informs the value of collecting further information to avoid this cost. VOI can inform study design, optimal sample size selection, and research prioritization. Recent methodological advances have reduced the computational burden of conducting VOI analysis and have made it easier to evaluate the expected value of sample information, the expected net benefit of sampling, and the optimal sample size of a study design ( $$n^{*}$$ n ∗ ). The volume of VOI analyses being published is increasing, and there is now a need for VOI studies to conduct sensitivity analyses on VOI-specific parameters. In this practical application, we introduce the curve of optimal sample size (COSS), which is a graphical representation of $$n^{*}$$ n ∗ over a range of willingness-to-pay thresholds and VOI parameters (example data and R code are provided). In a single figure, the COSS presents summary data for decision makers to determine the sample size that optimizes research funding given their operating characteristics. The COSS also presents variation in the optimal sample size given variability or uncertainty in VOI parameters. The COSS represents an efficient and additional approach for summarizing results from a VOI analysis.

Date: 2019
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DOI: 10.1007/s40273-019-00770-z

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