Accounting for parameter uncertainty in the definition of parametric distributions used to describe individual patient variation in health economic models

• Published in: BMC medical research methodology Vol. 17; no. 1; p. 170
• Main Authors: Degeling, Koen, IJzerman, Maarten J, Koopman, Miriam, Koffijberg, Hendrik
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 15-12-2017; BioMed Central; BMC
• Subjects: Analysis; Case studies; Discrete event simulation; Health aspects; Medical research; Parameter uncertainty; Patient-level variation; Personalized medicine; State-transition modeling; Stochastic uncertainty; Parameter uncertainty; Individual patient data; Discrete event simulation; Patient-level variation; Personalized medicine; State-transition modeling; Stochastic uncertainty
• ISSN: 1471-2288; 1471-2288
• DOI: 10.1186/s12874-017-0437-y

Parametric distributions based on individual patient data can be used to represent both stochastic and parameter uncertainty. Although general guidance is available on how parameter uncertainty should be accounted for in probabilistic sensitivity analysis, there is no comprehensive guidance on reflecting parameter uncertainty in the (correlated) parameters of distributions used to represent stochastic uncertainty in patient-level models. This study aims to provide this guidance by proposing appropriate methods and illustrating the impact of this uncertainty on modeling outcomes.
Two approaches, 1) using non-parametric bootstrapping and 2) using multivariate Normal distributions, were applied in a simulation and case study. The approaches were compared based on point-estimates and distributions of time-to-event and health economic outcomes. To assess sample size impact on the uncertainty in these outcomes, sample size was varied in the simulation study and subgroup analyses were performed for the case-study.
Accounting for parameter uncertainty in distributions that reflect stochastic uncertainty substantially increased the uncertainty surrounding health economic outcomes, illustrated by larger confidence ellipses surrounding the cost-effectiveness point-estimates and different cost-effectiveness acceptability curves. Although both approaches performed similar for larger sample sizes (i.e. n = 500), the second approach was more sensitive to extreme values for small sample sizes (i.e. n = 25), yielding infeasible modeling outcomes.
Modelers should be aware that parameter uncertainty in distributions used to describe stochastic uncertainty needs to be reflected in probabilistic sensitivity analysis, as it could substantially impact the total amount of uncertainty surrounding health economic outcomes. If feasible, the bootstrap approach is recommended to account for this uncertainty.