Bayesian Inference for Hospital Quality in a Selection Model
选择模型中医院质量的贝叶斯推断AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要明确涉及离散结果、非随机医院选择及与常规 probit 模型的比较。
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选择模型中医院质量的贝叶斯推断
- 研究问题
- 患者进入医院存在非随机选择、未观测病情严重度又影响死亡率时，如何从离院记录推断医院质量并比较医院间差异？
- 方法与思路
- 作者构建离散结果与非随机选择模型，以居住地到备选医院的距离为关键外生变量，通过马尔可夫链蒙特卡洛获得质量比较的后验概率。
- 材料陈述的贡献
- 摘要称洛杉矶肺炎患者样本中，最小与最大医院质量最高；未观测病情较重者更常进入高质量医院，选择模型与常规 probit 的判断明显不同。
- 阅读关注点
- 重点核对距离变量外生性的依据、医院选择与死亡率方程的关联设定，以及先验、后验模拟和质量比较对模型假设的敏感性。
- 阅读边界
- 摘要未说明距离外生性的论证、先验与模拟诊断；结果来自特定时期的洛杉矶 Medicare 肺炎患者，外部适用性仍需核对。
原文摘要
This paper develops new econometric methods to infer hospital quality in a model with discrete dependent variables and nonrandom selection. Mortality rates in patient discharge records are widely used to infer hospital quality. However, hospital admission is not random and some hospitals may attract patients with greater unobserved severity of illness than others. In this situation the assumption of random admission leads to spurious inference about hospital quality. This study controls for hospital selection using a model in which distance between the patient's residence and alternative hospitals are key exogenous variables. Bayesian inference in this model is feasible using a Markov chain Monte Carlo posterior simulator, and attaches posterior probabilities to quality comparisons between individual hospitals and groups of hospitals. The study uses data on 74,848 Medicare patients admitted to 114 hospitals in Los Angeles County from 1989 through 1992 with a diagnosis of pneumonia. It finds the smallest and largest hospitals to be of the highest quality. There is strong evidence of dependence between the unobserved severity of illness and the assignment of patients to hospitals, whereby patients with a high unobserved severity of illness are disproportionately admitted to high quality hospitals. Consequently a conventional probit model leads to inferences about quality that are markedly different from those in this study's selection model.