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

临终关怀研究质量中的聚类半竞争风险分析

• 批准号: 8612275
• 负责人: SEBASTIEN HANEUSE
• 金额: $ 47.5万
• 依托单位: HARVARD SCHOOL OF PUBLIC HEALTH
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-02-13 至 2018-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8612275
• 关键词: Acquired Immunodeficiency Syndrome; Acute; Acute myocardial infarction; Affect; Aging; Alzheimer' s Disease; Bayesian Analysis; Bayesian Method; Bayesian Modeling; Brain; Caring; Cessation of life; Characteristics; Clinical; Colon; Computer software; Data; Dementia; Dependence; Development; Diagnosis; Disease Management; Epidemiologic Studies; Epidemiology; Evaluation; Event; Failure; Goals; Health; Health Care Costs; Heart; Hospitals; Individual; Institute of Medicine (U.S.); Joints; Lead; Left; Literature; Logistic Regressions; Logistics; Lung; Malignant Neoplasms; Malignant neoplasm of pancreas; Measures; Medicare; Methodology; Methods; Modeling; Monitor; Palliative Care; Patients; Performance; Pneumonia; Population; Property; Provider; Quality of Care; Recurrence; Reporting; Research Personnel; Rewards; Risk; Sampling; Specific qualifier value; Statistical Methods; Structure; Time; Translating; United States Centers for Medicare and Medicaid Services; Variant; Veins; Work; cancer care; cost; end of life; flexibility; improved; mortality; novel; palliative; public health relevance; simulation; statistics; tool; tumor; user friendly software

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.

项目摘要 最近的一份医学研究所报告强调了在美国控制医疗保健费用的迫切需求 不牺牲护理质量。作为医疗保健费用最大的付款人，Medicare和Medicaid中心 服务（CMS）竭尽全力监测护理质量。但是，这些努力重点 在治愈率高且死亡率低的急性条件下。对于越来越普遍的 “晚期健康状况”，此类癌症和阿尔茨海默氏病，治愈率很低，短期死亡率为 疾病管理的焦点是生命终结（EOL）姑息治疗。但是，这种护理很昂贵。 2010年，全国癌症护理成本估计为1,250亿美元。尽管这些巨额成本，但仍有 没有全面的全国性努力来监控EOL护理的质量。这些努力的关键障碍是缺乏 适当的统计方法。为了估计医院特定的再入院率，CMS当前使用 逻辑正常通用线性混合模型（GLMM）。但是，该模型忽略了死亡作为截断 事件。因此，当前CMS方法将EOL评估质量应用于高级 健康状况是不合适的，可能会导致偏见，并可能对医院的状态产生重大影响 因出色/差的护理质量而受到奖励/处罚。在统计文献中，非末端事件的研究 （例如，再入院）受终端事件（例如死亡）的约束被称为“半竞争风险”问题。 当前的国家护理评估工作忽略了半竞争风险问题。一个主要的贡献 因素是统计文献中尚未考虑聚类的半竞争风险数据。小说 因此，必须开发和评估半竞争风险数据的统计方法。我们将发展 半竞争风险数据的全面，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中获得的众多好处。一个 至关重要的贡献将是一种新型贝叶斯分层模型的发展，以重复测量 竞争数据，个人聚集在医院中。新型的多元医院级别措施 随着时间的推移，将共同适应非末端和末端事件，以及估计方法的方法 推论，排名和出色/贫穷医院的识别。最后，使用所有医疗保险注册的数据 从2000年至2010年，Seer-Medicare的肿瘤数据将应用于EOL护理质量的方法 胰腺，肺，结肠和大脑的癌症。拟议的工作将立即并实质上改进，并且 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 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，其中许多直接影响美国人口越来越老化的大部分。