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Bayesian cost-effectiveness analysis for censored data: an application to antiplatelet therapy
David Bin-Chia Wu1, Yi-Wen Tsai, Yu-Wen Wen
1Division of Biostatistics, Institute of Public Health, National Yang-Ming University, Taipei, Taiwan.
Journal of Medical Economics
|December 27, 2011
Summary
This study introduces a regression model to address censoring in cost-effectiveness analysis (CEA), improving accuracy for time-to-event data in pharmacoeconomics. The findings suggest aspirin plus PPIs are more cost-effective for GI bleeding patients when censoring is considered.
Area of Science:
- Pharmacoeconomics
- Health Economics
- Biostatistics
Background:
- Cost-effectiveness analysis (CEA) is crucial in pharmacoeconomics for evaluating healthcare interventions.
- Traditional regression models in CEA may yield biased estimates if patient-level censoring in effectiveness and costs is ignored.
- Accounting for patient heterogeneity via covariates is standard, but time-to-event data requires specialized handling.
Purpose of the Study:
- To propose and validate a regression model that simultaneously accounts for time-to-event effectiveness and costs in CEA.
- To address the bias introduced by censoring in cost and effectiveness data.
- To provide a more accurate estimation method for regression-based CEA.
Main Methods:
- A bivariate regression model was developed to analyze effectiveness and cost concurrently, incorporating censored observations.
- Bayesian estimation of regression coefficients was performed using Markov chain Monte Carlo (MCMC) simulations.
- The model was applied to empirical data on anti-platelet therapies for cardiovascular disease patients at high risk of gastrointestinal (GI) bleeding.
Main Results:
- Under censored conditions, aspirin plus proton-pump inhibitors (PPIs) demonstrated greater cost-effectiveness compared to clopidogrel (with or without PPIs).
- The cost-effectiveness acceptability curve indicated a willingness-to-pay of 89 NTD for delaying hospitalization due to GI complications by one day, favoring clopidogrel over aspirin.
- Censoring significantly impacted the cost-effectiveness outcomes.
Conclusions:
- Ignoring censoring in CEA can lead to biased results and inaccurate conclusions.
- The proposed bivariate regression method offers an appropriate approach to conduct regression-based CEA, enhancing estimation accuracy.
- While the bivariate normal assumption for cost and effectiveness is important, large sample sizes can mitigate minor deviations.
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