A Bayesian approach to aid in formulary decision making: incorporating institution-specific cost-effectiveness data with clinical trial results.
Pharmacy and therapeutics committees commonly cite a lack of generalizability as a reason for not incorporating cost-effectiveness information into decision making. To address this concern, many committees undertake site-specific economic evaluations, which are often limited by small sample sizes and nonrandomized designs. We show how 2 complementary approaches were used to minimize these limitations in an economic evaluation of abciximab at 1 institution. Using a propensity score methodology, we selected patients who did not receive abciximab for the comparison cohort. Then, we adopted a Bayesian, hierarchical, random-effects model to integrate site-specific and clinical trial data. We applied the posterior distributions of effectiveness with local cost data in a traditional decision-analytic model. In 74% of the simulations, abciximab was cost-effective at 1 institution at the $50,000 per life year saved threshold, assuming a 50:50 split of patients undergoing coronary stenting and angioplasty. Among patients undergoing coronary stenting, the cost-effectiveness ratio of the addition of abciximab was at or below the $50,000 per life year saved threshold in 66.0% of the simulations.
Duke Scholars
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Related Subject Headings
- Randomized Controlled Trials as Topic
- Middle Aged
- Male
- Immunoglobulin Fab Fragments
- Humans
- Health Policy & Services
- Female
- Decision Making
- Cost-Benefit Analysis
- Coronary Disease
Citation
Published In
DOI
ISSN
Publication Date
Volume
Issue
Start / End Page
Location
Related Subject Headings
- Randomized Controlled Trials as Topic
- Middle Aged
- Male
- Immunoglobulin Fab Fragments
- Humans
- Health Policy & Services
- Female
- Decision Making
- Cost-Benefit Analysis
- Coronary Disease