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

产品说明：美国医学研究所（Institute of Medicine）最近的一份报告强调，在不牺牲医疗质量的前提下，控制美国医疗成本是当务之急。作为医疗费用的最大支付者，医疗保险和医疗补助服务中心（CMS）进行全面的国家努力，以监测护理质量。然而，这些努力侧重于治愈率高、死亡率低的急性疾病。对于广泛的日益普遍的“先进的健康状况”，如癌症和阿尔茨海默氏病，治愈率低，短期死亡率高，疾病管理的重点是临终（EOL）姑息治疗。然而，这种护理是昂贵的。2010年，全国癌症护理费用估计为1250亿美元。尽管这些巨大的成本，有没有全面的国家努力，以监测终末期护理的质量。这些努力的一个主要障碍是缺乏适当的统计方法。为了估计医院特定的再入院率，CMS目前使用逻辑正态广义线性混合模型（GLMM）。然而，该模型忽略了死亡作为截断事件。因此，单纯应用当前CMS方法评估晚期健康状况的EOL质量是不合适的，可能会导致偏倚，并可能对医院如何因优质/劣质护理而受到奖励/惩罚产生重大影响。在统计学文献中，非终末事件（例如，再入院）受终末事件（例如，死亡）影响的研究被称为“半竞争风险”问题。目前国家护理质量评估工作忽视了半竞争性风险问题。一个主要影响因素是统计文献中未考虑聚集的半竞争性风险数据。因此，必须开发和评估半竞争性风险数据的新统计方法。我们将为半竞争风险数据开发一个全面、统一的贝叶斯分析框架。拟议的框架将允许研究人员利用贝叶斯范式内提供的众多好处。一个至关重要的贡献将是发展新的贝叶斯层次模型的重复测量半竞争数据，其中个人聚集在医院。将开发新的多变量医院水平的措施，共同适应非终端和终端事件随着时间的推移，将用于估计，推断，排名和识别优秀/较差的医院的方法。最后，使用2000-2010年所有医疗保险登记者的数据和SEER医疗保险的肿瘤数据，我们将把我们的方法应用于胰腺癌、肺癌、结肠癌和脑癌的EOL护理质量。拟议的工作将立即大幅改善和扩大用于EOL护理质量评估的统计工具集，并提供美国癌症护理的关键流行病学结果。这些方法将广泛适用于癌症以外的所有高级健康状况，其中许多直接影响日益老龄化的美国人口的大部分。

DESCRIPTION: 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 car 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 estimat 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, naïve 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, he study of a non-terminal event (e.g. readmission) that is subject to a terminal event (e.g. death) i 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 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.