TY - JOUR

T1 - Computing Expected Value of Partial Sample Information from Probabilistic Sensitivity Analysis Using Linear Regression Metamodeling

AU - Jalal, Hawre

AU - Goldhaber-Fiebert, Jeremy D.

AU - Kuntz, Karen M.

PY - 2015/7/19

Y1 - 2015/7/19

N2 - Decision makers often desire both guidance on the most cost-effective interventions given current knowledge and also the value of collecting additional information to improve the decisions made (i.e., from value of information [VOI] analysis). Unfortunately, VOI analysis remains underused due to the conceptual, mathematical, and computational challenges of implementing Bayesian decision-theoretic approaches in models of sufficient complexity for real-world decision making. In this study, we propose a novel practical approach for conducting VOI analysis using a combination of probabilistic sensitivity analysis, linear regression metamodeling, and unit normal loss integral function - a parametric approach to VOI analysis. We adopt a linear approximation and leverage a fundamental assumption of VOI analysis, which requires that all sources of prior uncertainties be accurately specified. We provide examples of the approach and show that the assumptions we make do not induce substantial bias but greatly reduce the computational time needed to perform VOI analysis. Our approach avoids the need to analytically solve or approximate joint Bayesian updating, requires only one set of probabilistic sensitivity analysis simulations, and can be applied in models with correlated input parameters.

AB - Decision makers often desire both guidance on the most cost-effective interventions given current knowledge and also the value of collecting additional information to improve the decisions made (i.e., from value of information [VOI] analysis). Unfortunately, VOI analysis remains underused due to the conceptual, mathematical, and computational challenges of implementing Bayesian decision-theoretic approaches in models of sufficient complexity for real-world decision making. In this study, we propose a novel practical approach for conducting VOI analysis using a combination of probabilistic sensitivity analysis, linear regression metamodeling, and unit normal loss integral function - a parametric approach to VOI analysis. We adopt a linear approximation and leverage a fundamental assumption of VOI analysis, which requires that all sources of prior uncertainties be accurately specified. We provide examples of the approach and show that the assumptions we make do not induce substantial bias but greatly reduce the computational time needed to perform VOI analysis. Our approach avoids the need to analytically solve or approximate joint Bayesian updating, requires only one set of probabilistic sensitivity analysis simulations, and can be applied in models with correlated input parameters.

KW - Bayesian statistical methods

KW - cost-benefit analysis

KW - probabilistic sensitivity analysis

KW - simulation methods

KW - value of information

UR - http://www.scopus.com/inward/record.url?scp=84931461022&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84931461022&partnerID=8YFLogxK

U2 - 10.1177/0272989X15578125

DO - 10.1177/0272989X15578125

M3 - Article

C2 - 25840900

AN - SCOPUS:84931461022

VL - 35

SP - 584

EP - 595

JO - Medical Decision Making

JF - Medical Decision Making

SN - 0272-989X

IS - 5

ER -