The impact of using informative priors in a Bayesian cost-effectiveness analysis: an application of endovascular versus open surgical repair for abdominal aortic aneurysms in high-risk patients.
Bayesian methods have been proposed as a way of synthesizing all available evidence to inform decision making. However, few practical applications of the use of Bayesian methods for combining patient-level data (i.e., trial) with additional evidence (e.g., literature) exist in the cost-effectiveness literature. The objective of this study was to compare a Bayesian cost-effectiveness analysis using informative priors to a standard non-Bayesian nonparametric method to assess the impact of incorporating additional information into a cost-effectiveness analysis.



Patient-level data from a previously published nonrandomized study were analyzed using traditional nonparametric bootstrap techniques and bivariate normal Bayesian models with vague and informative priors. Two different types of informative priors were considered to reflect different valuations of the additional evidence relative to the patient-level data (i.e., "face value" and "skeptical"). The impact of using different distributions and valuations was assessed in a sensitivity analysis. Models were compared in terms of incremental net monetary benefit (INMB) and cost-effectiveness acceptability frontiers (CEAFs).



The bootstrapping and Bayesian analyses using vague priors provided similar results. The most pronounced impact of incorporating the informative priors was the increase in estimated life years in the control arm relative to what was observed in the patient-level data alone. Consequently, the incremental difference in life years originally observed in the patient-level data was reduced, and the INMB and CEAF changed accordingly.



The results of this study demonstrate the potential impact and importance of incorporating additional information into an analysis of patient-level data, suggesting this could alter decisions as to whether a treatment should be adopted and whether more information should be acquired.
Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Ontario, Canada (CEM, EMP, LT, RG, J-ET)|Programs for Assessment of Technology in Health (PATH) Research Institute, St. Joseph’s Healthcare–Hamilton,
Hamilton, Ontario, Canada (CEM, RG, J-ET)
C Elizabeth
CE
McCarron
Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Ontario, Canada (CEM, EMP, LT, RG, J-ET)|Biostatistics Unit, St. Joseph’s Healthcare–Hamilton, Hamilton, Ontario, Canada (EMP, LT)
Eleanor M
EM
Pullenayegum
Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Ontario, Canada (CEM, EMP, LT, RG, J-ET)|Biostatistics Unit, St. Joseph’s Healthcare–Hamilton, Hamilton, Ontario, Canada (EMP, LT)
Lehana
L
Thabane
Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Ontario, Canada (CEM, EMP, LT, RG, J-ET)|Programs for Assessment of Technology in Health (PATH) Research Institute, St. Joseph’s Healthcare–Hamilton,
Hamilton, Ontario, Canada (CEM, RG, J-ET)
Ron
R
Goeree
Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Ontario, Canada (CEM, EMP, LT, RG, J-ET)|Programs for Assessment of Technology in Health (PATH) Research Institute, St. Joseph’s Healthcare–Hamilton,
Hamilton, Ontario, Canada (CEM, RG, J-ET)
Jean-Eric
JE
Tarride
D017544:Aortic Aneurysm, Abdominal / Q000191:economics / Q000601:surgery*; D001499:Bayes Theorem; D003362:Cost-Benefit Analysis; D006801:Humans; D014656:Vascular Surgical Procedures / Q000379:methods*