Simulating Study Data to Support Expected Value of Sample Information Calculations: A Tutorial

TY - JOUR

T1 - Simulating Study Data to Support Expected Value of Sample Information Calculations

T2 - A Tutorial

AU - Heath, Anna

AU - Strong, Mark

AU - Glynn, David

AU - Kunst, Natalia

AU - Welton, Nicky J

AU - Goldhaber-Fiebert, Jeremy D

N1 - © The Author(s) 2021

PY - 2022/2

Y1 - 2022/2

N2 - The expected value of sample information (EVSI) can be used to prioritize avenues for future research and design studies that support medical decision making and offer value for money spent. EVSI is calculated based on 3 key elements. Two of these, a probabilistic model-based economic evaluation and updating model uncertainty based on simulated data, have been frequently discussed in the literature. By contrast, the third element, simulating data from the proposed studies, has received little attention. This tutorial contributes to bridging this gap by providing a step-by-step guide to simulating study data for EVSI calculations. We discuss a general-purpose algorithm for simulating data and demonstrate its use to simulate 3 different outcome types. We then discuss how to induce correlations in the generated data, how to adjust for common issues in study implementation such as missingness and censoring, and how individual patient data from previous studies can be leveraged to undertake EVSI calculations. For all examples, we provide comprehensive code written in the R language and, where possible, Excel spreadsheets in the supplementary materials. This tutorial facilitates practical EVSI calculations and allows EVSI to be used to prioritize research and design studies.

AB - The expected value of sample information (EVSI) can be used to prioritize avenues for future research and design studies that support medical decision making and offer value for money spent. EVSI is calculated based on 3 key elements. Two of these, a probabilistic model-based economic evaluation and updating model uncertainty based on simulated data, have been frequently discussed in the literature. By contrast, the third element, simulating data from the proposed studies, has received little attention. This tutorial contributes to bridging this gap by providing a step-by-step guide to simulating study data for EVSI calculations. We discuss a general-purpose algorithm for simulating data and demonstrate its use to simulate 3 different outcome types. We then discuss how to induce correlations in the generated data, how to adjust for common issues in study implementation such as missingness and censoring, and how individual patient data from previous studies can be leveraged to undertake EVSI calculations. For all examples, we provide comprehensive code written in the R language and, where possible, Excel spreadsheets in the supplementary materials. This tutorial facilitates practical EVSI calculations and allows EVSI to be used to prioritize research and design studies.

KW - Algorithms

KW - Cost-Benefit Analysis

KW - Humans

KW - Models, Statistical

KW - Uncertainty

U2 - 10.1177/0272989X211026292

DO - 10.1177/0272989X211026292

M3 - Article

C2 - 34388954

SN - 0272-989X

VL - 42

SP - 143

EP - 155

JO - Medical Decision Making

JF - Medical Decision Making

IS - 2

ER -