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, Hawre Jalal

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


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). 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 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.

Original languageEnglish (US)
Pages (from-to)871-877
Number of pages7
Issue number7
StatePublished - Jul 1 2019

Bibliographical note

Funding Information:
Funding/support Financial support for this study was provided in part by a Doctoral Dissertation Fellowship from the Graduate School of the University of Minnesota as part of Dr. Alarid-Escudero’s doctoral program. Drs. Kuntz and Alarid-Escudero were supported by a Grant from the National Cancer Institute (U01-CA-199335) as part of the Cancer Intervention and Surveillance Modeling Network (CISNET). Dr. Jutkowitz was supported by a Grant from the National Institute on Aging (1R21AG059623-01) and a Grant from the Brown School of Public Health. The funding agencies had no role in the design of the study, interpretation of results, or writing of the manuscript. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The funding agreement ensured the authors’ independence in designing the study, interpreting the data, and writing and publishing the report.

PubMed: MeSH publication types

  • Journal Article
  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

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