Lee Kyu Ha, Dominici Francesca, Schrag Deborah, Haneuse Sebastien
Epidemiology and Biostatistics Core, The Forsyth Institute, Department of Oral Health Policy and Epidemiology, Harvard School of Dental Medicine.
Department of Biostatistics, Harvard T.H. Chan School of Public Health.
J Am Stat Assoc. 2016;111(515):1075-1095. doi: 10.1080/01621459.2016.1164052. Epub 2016 Oct 18.
Readmission following discharge from an initial hospitalization is a key marker of quality of health care in the United States. For the most part, readmission has been studied among patients with 'acute' health conditions, such as pneumonia and heart failure, with analyses based on a logistic-Normal generalized linear mixed model (Normand et al., 1997). Naïve application of this model to the study of readmission among patients with 'advanced' health conditions such as pancreatic cancer, however, is problematic because it ignores death as a competing risk. A more appropriate analysis is to imbed such a study within the semi-competing risks framework. To our knowledge, however, no comprehensive statistical methods have been developed for cluster-correlated semi-competing risks data. To resolve this gap in the literature we propose a novel hierarchical modeling framework for the analysis of cluster-correlated semi-competing risks data that permits parametric or non-parametric specifications for a range of components giving analysts substantial flexibility as they consider their own analyses. Estimation and inference is performed within the Bayesian paradigm since it facilitates the straightforward characterization of (posterior) uncertainty for all model parameters, including hospital-specific random effects. Model comparison and choice is performed via the deviance information criterion and the log-pseudo marginal likelihood statistic, both of which are based on a partially marginalized likelihood. An efficient computational scheme, based on the Metropolis-Hastings-Green algorithm, is developed and had been implemented in the SemiCompRisks R package. A comprehensive simulation study shows that the proposed framework performs very well in a range of data scenarios, and outperforms competitor analysis strategies. The proposed framework is motivated by and illustrated with an on-going study of the risk of readmission among Medicare beneficiaries diagnosed with pancreatic cancer. Using data on n=5,298 patients at =112 hospitals in the six New England states between 2000-2009, key scientific questions we consider include the role of patient-level risk factors on the risk of readmission and the extent of variation in risk across hospitals not explained by differences in patient case-mix.
首次住院出院后的再入院是美国医疗保健质量的一个关键指标。在很大程度上，再入院情况主要是在患有“急性”健康状况（如肺炎和心力衰竭）的患者中进行研究的，分析基于逻辑正态广义线性混合模型（诺曼德等人，1997年）。然而，将此模型简单应用于研究患有“晚期”健康状况（如胰腺癌）的患者的再入院情况存在问题，因为它忽略了死亡这一竞争风险。更合适的分析方法是将此类研究纳入半竞争风险框架内。然而，据我们所知，尚未开发出针对聚类相关半竞争风险数据的全面统计方法。为了弥补文献中的这一空白，我们提出了一种新颖的层次建模框架，用于分析聚类相关半竞争风险数据，该框架允许对一系列组件进行参数化或非参数化设定，使分析师在考虑自身分析时具有很大的灵活性。估计和推断是在贝叶斯范式内进行的，因为它有助于直接刻画所有模型参数（包括特定医院随机效应）的（后验）不确定性。模型比较和选择通过偏差信息准则和对数伪边际似然统计量进行，这两者均基于部分边际似然。基于Metropolis-Hastings-Green算法开发了一种高效计算方案，并已在SemiCompRisks R包中实现。一项全面的模拟研究表明，所提出的框架在一系列数据场景中表现非常出色，并且优于竞争分析策略。所提出的框架的灵感来自于一项正在进行的针对被诊断患有胰腺癌的医疗保险受益人的再入院风险研究，并通过该研究进行说明。利用2000年至2009年期间新英格兰六个州112家医院的n = 5298名患者的数据，我们考虑的关键科学问题包括患者层面风险因素在再入院风险中的作用，以及未由患者病例组合差异解释的各医院风险差异程度。
