|
Activity Number:
|
77
- Contributed Poster Presentations: Biopharmaceutical Section
|
|
Type:
|
Contributed
|
|
Date/Time:
|
Monday, August 3, 2020 : 10:00 AM to 2:00 PM
|
|
Sponsor:
|
Biopharmaceutical Section
|
|
Abstract #312263
|
|
|
Title:
|
Bayesian Data Envelopment Analysis for Assessing Drug Benefit-Risk
|
|
Author(s):
|
Guangyi Gao* and Jo A. Wick and Byron J Gajewski
|
|
Companies:
|
and University of Kansas Medical Center and KUMC
|
|
Keywords:
|
DEA;
Bayesian;
Benefit-Risk
|
|
Abstract:
|
Current approaches to drug benefit-risk assessment do not combine all benefit and risk data into a straightforward quantitative comparison of treatment that recognizes the uncertainty in estimation. NNT and NNH are well-established measures in health care literature, but they only accommodate a single measure of benefit and risk and ignore uncertainty. MCDA has been used to study trade-offs between benefit and risk, and while it can combine all available efficacy and safety data, it does not allow statistical comparisons. We propose a Bayesian DEA framework to assess benefit-risk. DEA is a nonparametric mathematical programming method for evaluating the relative efficiency of entities that use resources to achieve desired outcomes. It is widely used in healthcare to compare hospital quality and efficiency but has yet to be applied to benefit-risk. The proposed model produces posterior distributions of benefit-risk that characterize the value of competing treatment options to improve skin appearance in patients with mild to moderate acne. The approach also identifies specific sources of that physicians can address. We investigate and compare model performance via a simulation study.
|
Authors who are presenting talks have a * after their name.
