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控制美国医疗保健成本的迫切需要 而不牺牲护理质量。作为医疗保健费用的最大支付者，医疗保险和医疗补助中心 CMS在全国范围内开展全面的工作，以监测护理质量。然而，这些努力的重点 治愈率高、死亡率低的急性疾病。对于一个广泛的日益普遍的 “晚期健康状况”，如癌症和老年痴呆症，治愈率低，短期死亡率低， 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。尽管成本巨大， 没有全面的国家努力来监测终末期护理的质量。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，这个模型忽略了死亡作为一个截断 活动因此，将当前CMS方法用于高级 健康状况不适当，可能会导致偏见，并可能对医院的工作产生重大影响。 因优质/劣质护理而受到奖励/惩罚。在统计学文献中，对非终结事件的研究 (e.g.再入院），这是受到终端事件（如死亡）被称为“半竞争风险”的问题。 目前国家护理质量评估工作忽视了半竞争性风险问题。主要贡献 因素是统计学文献中未考虑聚集的半竞争性风险数据。小说 因此，必须制定和评价半竞争性风险数据的统计方法。我们将开发 半竞争风险数据的全面、统一的贝叶斯分析框架。拟议框架 将允许研究人员利用贝叶斯范式内提供的众多好处。一 关键的贡献将是一种新的贝叶斯分层模型的重复测量半， 相互竞争的数据，其中个人聚集在医院内。新型多变量医院级措施， 随着时间的推移，将开发共同适应非终点事件和终点事件的方法， 推理、排名和优/差医院的识别。最后，使用所有医疗保险登记者的数据， 从2000-2010年和肿瘤数据从SEER医疗保险，我们将我们的方法，以终末期护理的质量 胰腺癌、肺癌、结肠癌和脑癌。拟议的工作将立即大大改善和 扩大用于EOL护理质量评估的统计工具集，并提供关键的流行病学信息。 美国癌症治疗的结果。这些方法将广泛适用于所有先进的健康状况， 癌症，其中许多直接影响日益老龄化的美国人口的大部分。