MOX - Modelling and Scientific Computing, Department of Mathematics Politecnico di Milano, Piazza Leonardo Da Vinci 32, Milano 20123, Italy.
MRC Biostatistics Unit, Cambridge Institute of Public Health, Forvie Site, Robinson Way, Cambridge Biomedical Campus, Cambridge CB2 0SR, UK.
Biostatistics. 2020 Jul 1;21(3):531-544. doi: 10.1093/biostatistics/kxy071.
We propose a novel model for hierarchical time-to-event data, for example, healthcare data in which patients are grouped by their healthcare provider. The most common model for this kind of data is the Cox proportional hazard model, with frailties that are common to patients in the same group and given a parametric distribution. We relax the parametric frailty assumption in this class of models by using a non-parametric discrete distribution. This improves the flexibility of the model by allowing very general frailty distributions and enables the data to be clustered into groups of healthcare providers with a similar frailty. A tailored Expectation-Maximization algorithm is proposed for estimating the model parameters, methods of model selection are compared, and the code is assessed in simulation studies. This model is particularly useful for administrative data in which there are a limited number of covariates available to explain the heterogeneity associated with the risk of the event. We apply the model to a clinical administrative database recording times to hospital readmission, and related covariates, for patients previously admitted once to hospital for heart failure, and we explore latent clustering structures among healthcare providers.
我们提出了一种新的层次时间事件数据模型，例如医疗保健数据，其中患者按其医疗保健提供者进行分组。此类数据最常见的模型是 Cox 比例风险模型，其脆弱性是同组患者共有的，并具有参数分布。我们通过使用非参数离散分布来放宽此类模型中的参数脆弱性假设。这通过允许非常一般的脆弱性分布来提高模型的灵活性，并使数据能够按具有相似脆弱性的医疗保健提供者进行聚类。提出了一种定制的期望最大化算法来估计模型参数，比较了模型选择方法，并在模拟研究中评估了代码。该模型对于行政数据特别有用，因为只有有限的协变量可用于解释与事件风险相关的异质性。我们将该模型应用于记录心力衰竭患者再次住院时间和相关协变量的临床行政数据库，并探索医疗保健提供者之间的潜在聚类结构。
