### 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^{∗}). 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 language | English (US) |
---|---|

Pages (from-to) | 871-877 |

Number of pages | 7 |

Journal | PharmacoEconomics |

Volume | 37 |

Issue number | 7 |

DOIs | |

State | Published - Jul 1 2019 |

### Fingerprint

### PubMed: MeSH publication types

- Journal Article

### Cite this

*PharmacoEconomics*,

*37*(7), 871-877. https://doi.org/10.1007/s40273-019-00770-z

**The Curve of Optimal Sample Size (COSS) : A Graphical Representation of the Optimal Sample Size from a Value of Information Analysis.** / Jutkowitz, Eric; Alarid Escudero, Fernando; Kuntz, Karen M; Jalal, Hawre.

Research output: Contribution to journal › Article

*PharmacoEconomics*, vol. 37, no. 7, pp. 871-877. https://doi.org/10.1007/s40273-019-00770-z

}

TY - JOUR

T1 - The Curve of Optimal Sample Size (COSS)

T2 - A Graphical Representation of the Optimal Sample Size from a Value of Information Analysis

AU - Jutkowitz, Eric

AU - Alarid Escudero, Fernando

AU - Kuntz, Karen M

AU - Jalal, Hawre

PY - 2019/7/1

Y1 - 2019/7/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85061658788&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85061658788&partnerID=8YFLogxK

U2 - 10.1007/s40273-019-00770-z

DO - 10.1007/s40273-019-00770-z

M3 - Article

C2 - 30761461

AN - SCOPUS:85061658788

VL - 37

SP - 871

EP - 877

JO - PharmacoEconomics

JF - PharmacoEconomics

SN - 1170-7690

IS - 7

ER -