Modelling volume-outcome relationships in health care
医疗服务量与结局关系的建模AI 解读 ↓
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医疗服务量与结局关系的建模
- 研究问题
- 如何灵活估计医疗机构服务量与患者结局的关系，区分服务量效应和机构自身差异，并判断何时可以作因果解释？
- 方法与思路
- 作者构建加性混合模型，以平滑函数描述连续服务量效应，纳入患者风险因素和机构随机截距，并借助因果图讨论解释条件。
- 材料陈述的贡献
- 摘要称，方法可同时提取平滑服务量效应与独立于服务量的机构效应，比较其大小，并通过联合估计构造置信集合。
- 阅读关注点
- 应核对因果图导出的解释条件、随机截距与服务量的关系，以及平滑项选择、聚类处理和置信集合的具体形式。
- 阅读边界
- 这是 arXiv 预印本，不能据此认定已经同行评审或期刊接收；摘要未列出因果条件，调整患者风险与机构聚类并不自动消除混杂。
原文摘要
Despite the ongoing strong interest in associations between quality of care and the volume of health care providers, a unified statistical framework for analyzing them is missing, and many studies suffer from poor statistical modelling choices. We propose a flexible, additive mixed model for studying volume-outcome associations in health care that takes into account individual patient characteristics as well as provider-specific effects through a multi-level approach. More specifically, we treat volume as a continuous variable, and its effect on the considered outcome is modelled as a smooth function. We take account of different case-mixes by including patient-specific risk factors and of clustering on the provider level through random intercepts. This strategy enables us to extract a smooth volume effect as well as volume-independent provider effects. These two quantities can be compared directly in terms of their magnitude, which gives insight into the sources of variability of quality of care. Based on a causal DAG, we derive conditions under which the volume-effect can be interpreted as a causal effect. The paper provides confidence sets for each of the estimated quantities relying on joint estimation of all effects and parameters. Our approach is illustrated through simulation studies and an application to German health care data. Keywords: health care quality measurement, volume-outcome analysis, minimum provider volume, additive regression models, random intercept