In health care, multilevel models are typically used to evaluate hospitals' performance and to rank hospitals accordingly. While multilevel models capture the hierarchical structure in the data, such as the grouping of patients into hospitals, these models do not account for additional latent structures. In this paper, we develop a novel multilevel logistic cluster‐weighted model which can predict a binary outcome, such as mortality within 30 days of discharge, while accounting both for known and latent structures of the data. We develop an Expectation‐Maximization algorithm for parameter estimation and a parametric bootstrap approach for assessing the variability of the estimators. Using a rich data set of the Lombardy (Italy) health care system and focussing on the two wards of cardiosurgery and medicine, we show how the proposed model detects, in both cases, two well‐defined clusters within the patient to hospital hierarchical structure of the data. A comparison with standard multilevel and cluster‐weighted approaches reveals a better fit of the proposed model and a greater insight into the structure of the data. We show how this can have implications in the resulting league tables and thus how the proposed model can be a useful tool for policy‐makers and healthcare managers to conduct hospital evaluations.

中文翻译：

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用于卫生保健结果评估的多级逻辑聚类加权模型*

在医疗保健中，通常使用多级模型来评估医院的绩效并据此对医院进行排名。尽管多级模型捕获了数据中的层次结构，例如将患者分组到医院，但这些模型并未考虑其他潜在结构。在本文中，我们开发了一种新颖的多级Logistic聚类加权模型，该模型可以预测二进制结果，例如出院后30天内的死亡率，同时考虑数据的已知结构和潜在结构。我们开发了用于参数估计的期望最大化算法和用于评估估计器变异性的参数自举方法。利用伦巴第（意大利）医疗保健系统的丰富数据集，并专注于心脏外科和药物治疗两个病区，我们展示了在两种情况下提出的模型如何检测到患者到医院数据层次结构中两个定义明确的群集。与标准多级方法和聚类加权方法的比较表明，该模型更适合该模型，并且对数据结构有更深入的了解。我们将展示这如何在最终的排行榜中产生影响，以及由此提出的模型如何成为决策者和医疗保健经理进行医院评估的有用工具。