Seemingly unrelated Bayesian additive regression trees for cost-effectiveness analyses in healthcare
医疗成本效果分析中的似不相关贝叶斯加性回归树AI 解读 ↓
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医疗成本效果分析中的似不相关贝叶斯加性回归树
- 研究问题
- 如何联合建模医疗成本与健康生活质量等相关结果，同时允许不同结果拥有不同非线性回归结构并兼容连续或二元响应？
- 方法与思路
- 作者扩展多元 BART，为不同响应配置独立树集并保留结果间依赖，讨论先验与 MCMC，并在因果分析中纳入倾向得分。
- 材料陈述的贡献
- 摘要提出连续结果的非参数似不相关回归与二元结果的多元 probit 扩展，提供 R 包，并报告创伤护理干预的成本效果证据。
- 阅读关注点
- 应核对不同树集间的依赖参数化、先验与采样机制，以及倾向得分纳入方式、因果识别条件和成本效果判定标准。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未经核实；摘要未说明完整因果假设及效果数值，无法据此确认干预的普遍收益。
原文摘要
In recent years, theoretical results and simulation evidence have shown Bayesian additive regression trees to be a highly-effective method for nonparametric regression. Motivated by cost-effectiveness analyses in health economics, where interest lies in jointly modelling the costs of healthcare treatments and the associated health-related quality of life experienced by a patient, we propose a multivariate extension of BART which is applicable in regression analyses with several dependent outcome variables. Our framework allows for continuous or binary outcomes and overcomes some key limitations of existing multivariate BART models by allowing each individual response to be associated with different ensembles of trees, while still handling dependencies between the outcomes. In the case of continuous outcomes, our model is essentially a nonparametric version of seemingly unrelated regression. Likewise, our proposal for binary outcomes is a nonparametric generalisation of the multivariate probit model. We give suggestions for easily interpretable prior distributions, which allow specification of both informative and uninformative priors. We provide detailed discussions of MCMC sampling methods to conduct posterior inference. Our methods are implemented in the R package "subart". We showcase their performance through extensive simulation experiments and an application to an empirical case study from health economics. By also accommodating propensity scores in a manner befitting a causal analysis, we find substantial evidence for a novel trauma care intervention's cost-effectiveness.