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: 8805834
• 负责人: SEBASTIEN HANEUSE
• 金额: $ 45.42万
• 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/8805834
• 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; simulation; statistics; tool; tumor; user friendly software

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.

描述：最近的一份医学研究所报告强调了在不牺牲护理质量的情况下控制美国医疗保健费用的迫切需求。作为医疗保险和医疗补助服务中心（CMS），作为最大的保健汽车成本付款人，采取了全面的国家努力来监测护理质量。但是，这些努力集中在治愈率高且死亡率低的急性条件上。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，短期死亡率很高，疾病管理的重点是寿命终止（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。尽管成本巨大，但仍未全面监控EOL护理质量。这些努力的关键障碍是缺乏适当的统计方法。为了估计医院特异性的再入院率，CMS当前使用逻辑普通的线性混合模型（GLMM）。但是，该模型将死亡忽略为截断事件。因此，当前的CMS方法对EOL评估的幼稚应用在先进的健康状况上是不合适的，可能会导致偏见，并且可能会对医院如何因出色/差的护理质量而受到奖励/惩罚。在统计文献中，他研究了一个非末端事件（例如，再入院），该事件受到终端事件（例如死亡）的约束，我被称为“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。一个主要的促成因素是统计文献中尚未考虑群集的半竞争风险数据。因此，必须开发和评估半竞争风险数据的新型统计方法。我们将为半竞争风险数据开发全面的统一贝叶斯分析框架。拟议的框架将使研究人员能够利用贝叶斯范式内的众多好处。至关重要的贡献将是开发新型贝叶斯分层模型，用于重复测量半竞争数据，其中个人被聚集在医院内。随着时间的推移，将开发出共同适应非末端和终末事件的新型多元医院级别，以及估计，推理，排名和良好/贫穷医院的识别的方法。最后，使用2000 - 2010年所有Medicare注册的数据以及Seer-Medicare的肿瘤数据，我们将把方法应用于胰腺，肺，结肠和大脑的癌症的EOL护理质量。拟议的工作将立即并实质上改善并扩大了EOL护理质量评估的一组统计工具，并为美国的癌症护理提供关键的流行病学结果。这些方法将广泛适用于所有先进的健康状况，除了癌症之外，其中许多方法直接影响了日益衰老的美国人群的大部分。