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

描述：医学研究所最近的一份报告强调了在不牺牲医疗质量的情况下控制美国医疗成本的迫切需要。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）在全国范围内进行全面的努力，以监测医疗质量。然而，这些努力的重点是治愈率高、死亡率低的急性病。对于各种日益普遍的“晚期健康状况”，如癌症和阿尔茨海默病，治愈率很低，短期死亡率很高，疾病管理的重点是生命末期（EOL）姑息治疗。然而，这种护理是昂贵的。2010年，全国癌症治疗费用估计为1250亿美元。尽管花费巨大，但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logistic-Normal广义线性混合模型（GLMM）。然而，这个模型忽略了作为截断事件的死亡。因此，naïve将目前的CMS方法应用于晚期健康状况的EOL评估质量是不合适的，可能会导致偏见，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。在统计文献中，对非终末事件（如再入院）与终末事件（如死亡）相关的研究被称为“半竞争风险”问题。目前国家医疗质量评估工作忽略了半竞争风险问题。一个主要的影响因素是聚类的半竞争风险数据在统计文献中没有被考虑。因此，必须开发和评估半竞争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。提出的框架将允许研究人员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新的贝叶斯层次模型，用于重复测量半竞争数据，其中个人聚集在医院内。随着时间的推移，将开发新的多变量医院级措施，共同适应非终末和终末事件，以及用于估计、推断、排名和识别优秀/差医院的方法。最后，利用2000-2010年所有医疗保险参保者的数据和SEER-Medicare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。拟议的工作将立即和实质性地改进和扩展用于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晚期健康状况，其中许多疾病直接影响到日益老龄化的美国人口的很大一部分。

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.