医疗保健中容量—结局关系的建模
Modelling volume-outcome relationships in health care
AI总结：
本文提出多层可加混合模型，将容量效应建模为平滑函数并分离提供者效应，基于因果 DAG 给出因果解释条件与联合置信集，且经模拟和德国数据验证。
摘要：
尽管人们对医疗质量与医疗服务提供者容量之间的关联持续抱有浓厚兴趣，但目前仍缺乏用于分析这些关联的统一统计框架，而且许多研究存在统计建模选择不当的问题。我们提出一种灵活的可加混合模型，用于研究医疗保健中的容量—结局关联；该模型通过多层方法同时考虑患者个体特征和服务提供者特异性效应。更具体地说，我们将容量视为连续变量，并将其对所考察结局的效应建模为平滑函数。我们通过纳入患者特异性风险因素来考虑不同病例组合，并通过随机截距来处理服务提供者层面的聚集性。这一策略使我们能够提取平滑的容量效应以及与容量无关的服务提供者效应。这两个量可以在量级上直接比较，从而有助于洞察医疗质量变异的来源。基于因果 DAG，我们推导了容量效应可被解释为因果效应所需满足的条件。本文依靠对所有效应和参数的联合估计，为每个被估计量提供置信集。我们的方法通过模拟研究以及对德国医疗保健数据的应用得到了说明。
英文摘要：
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
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