Clustered semi-competing risks analysis in quality of end-of-life care studies

项目总结 医学研究所最近的一份报告强调了在美国控制医疗成本的迫切需要 在不牺牲护理质量的情况下。作为医疗费用的最大支付者，医疗保险和医疗补助中心 服务(CMS)开展全面的国家努力，以监测护理质量。然而，这些努力集中在 在治愈率高而死亡率低的急性疾病上。对于范围广泛的日益流行的 ‘高级健康状况’，如癌症和阿尔茨海默病，治愈率低，短期死亡率低 高，疾病管理的重点是临终关怀(EOL)。然而，这样的护理是昂贵的。 2010年，全国癌症护理费用估计为1250亿美元。尽管有这些巨大的成本，但有 没有全面的国家努力来监测EOL护理的质量。这些努力的一个关键障碍是缺乏 适当的统计方法。为了估计特定于医院的再住院率，CMS目前使用 Logistic-正态广义线性混合模型。然而，这个模型忽略了死亡作为一种截断 事件。因此，NA1将采用当前的CMS方法，以评估对 健康状况不合适，可能会导致偏见，并可能对医院的状况产生重大影响 因护理质量好/差而受到奖励/惩罚。在统计学文献中，对非终端事件的研究 发生终末期事件(如死亡)的风险(如重新入院)被称为“半竞争风险”问题。 目前的国家护理质量评估工作忽视了半竞争性风险问题。一项重大贡献 其中的一个因素是，统计文献中没有考虑半竞争性风险数据的集群化。小说 因此，必须开发和评估半竞争性风险数据的统计方法。我们将发展 针对半竞争性风险数据的全面、统一的贝叶斯分析框架。拟议的框架 将使研究人员能够利用贝叶斯范式提供的众多好处。一个 关键的贡献将是开发一种新的贝叶斯分层模型，用于重复测量半 相互竞争的数据，其中个人聚集在医院内。新的多变量医院水平衡量标准 将开发随着时间的推移共同适应非终端和终端事件，以及用于估计的方法， 优劣医院的推理、排序和识别。最后，使用所有联邦医疗保险参与者的数据 从2000-2010年和来自SEER-Medicare的肿瘤数据，我们将应用我们的方法来提高EOL护理的质量 胰腺癌、肺癌、结肠癌和脑癌。拟议的工作将立即大幅改善和 扩展用于EOL护理质量评估的统计工具集，并提供关键的流行病学 关于美国癌症护理的结果。这些方法将广泛适用于所有高级健康状况， 癌症，其中许多直接影响到日益老龄化的美国人口的很大一部分人。

PROJECT SUMMARY A recent Institute of Medicine report highlighted the pressing need to control health care costs in the US without sacrificing quality of care. As the largest payer of health care costs, the Centers for Medicare and Medicaid Services (CMS) conducts comprehensive national efforts to monitor quality of care. However, these efforts focus on acute conditions for which cure rates are high and mortality low. For a broad range of increasingly prevalent 'advanced health conditions', such cancer and Alzheimer's disease, cure rates are low, short-term mortality is high and the focus of disease management is end-of-life (EOL) palliative care. Such care is expensive, however. In 2010 national cost of cancer care was estimated to be $125 billion. Despite these huge costs, there are no comprehensive national efforts to monitor quality of EOL care. A key barrier to these efforts is the lack of appropriate statistical methodology. To estimate hospital-specific readmission rates, CMS currently uses a logistic-Normal generalized linear mixed model (GLMM). However, this model ignores death as a truncating event. As such, na1¿ ve application of the current CMS approach for quality of EOL assessments for advanced health conditions is inappropriate, would likely lead to bias and could have a major impact on how hospitals are rewarded/penalized for excellent/poor quality of care. In the statistics literature, the study of a non-terminal event (e.g. readmission) that is subject to a terminal event (e.g. death) is known as the 'semi-competing risks' problem. Current national quality of care assessment efforts ignore the semi-competing risks problem. A major contributing factor is that clustered semi-competing risks data has not been considered in the statistical literature. Novel statistical methods for semi-competing risks data must, therefore, be developed and evaluated. We will develop a comprehensive, unified Bayesian analysis framework for semi-competing risks data. The proposed framework will permit researchers to take advantage of the numerous benefits afforded within the Bayesian paradigm. A crucial contribution will be the development of a novel Bayesian hierarchical models for repeated measures semi- competing data, where individuals are clustered within hospitals. Novel multivariate hospital-level measures that jointly accommodate non-terminal and terminal events over time will be developed, as will methods for estimation, inference, ranking and the identification of excellent/poor hospitals. Finally, using data on all Medicare enrollees from 2000-2010 and tumor data from SEER-Medicare, we will apply our methods to quality of EOL care for cancers of the pancreas, lung, colon and brain. The proposed work will immediately and substantially improve and expand the set of statistical tools use for EOL care quality assessments, as well as provide key epidemiological results on cancer care in the US. The methods will be broadly applicable to all advanced health conditions, beyond cancer, many of which directly affect large segments of an increasingly aging US population.