BAYESIAN PATTERN-MIXTURE MODELS FOR QUALITY OF CARE DATA

护理质量数据的贝叶斯模式混合模型

• 批准号: 7050465
• 负责人: SUSAN M. PADDOCK
• 金额: $ 4.88万
• 依托单位: RAND CORPORATION
• 依托单位国家: 美国
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-09-30 至 2007-09-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7050465
• 关键词: adolescence (12-20); alcoholism /alcohol abuse therapy; clinical research; data management; data quality /integrity; decision making; disease /therapy duration; drug abuse therapy; health care policy; health care quality; human subject; interview; longitudinal human study; mathematical model; method development; model design /development; patient oriented research; therapy compliance

DESCRIPTION (PROVIDED BY THE APPLICANT): Policy makers rely on accurate assessments of quality of care when making decisions about health care delivery. This task is complicated by the pervasive problem in health services research (HSR) of data that are missing due to client departure prior to completing a full course of treatment and/or loss to follow-up. Analytic methods employed in HSR frequently ignore the mechanism by which data are missing. Biased estimates from the quality of care model could result if the true nature of the missing data is unaccounted for in the analysis, thereby hindering a policy maker's ability to improve quality. The pattern-mixture model (PMM) has been proposed in the statistical literature as an alternative to standard analytic methods that ignore the mechanism by which data are missing. Our specific aims are to: 1) improve the assessment of the relationships among treatment structure, process, and outcomes when treatment dropout and study attrition occur; and 2) utilize expert opinion and knowledge about the reasons for treatment dropout and attrition in order to inform the PMM-building process. Aim 1 will be achieved by constructing a pattern-mixture quality of care model. Aim 2 will build upon Aim 1 by addressing the subjective decisions that are required to build a PMM via Bayesian statistics, properly addressing the uncertainty inherent in these decisions, and measuring their effect on conclusions drawn from the analysis.

描述（由申请人提供）：决策者在做出有关医疗保健提供的决定时依靠对护理质量的准确评估。这项任务因卫生服务研究（HSR）的普遍问题而变得复杂，这些数据在完成完整的治疗过程和/或随访过程中，由于客户出发而缺少的数据。 HSR中采用的分析方法经常忽略数据缺失的机制。如果在分析中没有丢失数据的真实性质，从而妨碍了政策制定者提高质量的能力，则可能会产生偏见的估计。统计文献中已经提出了模式混合模型（PMM），作为忽略数据缺失机制的标准分析方法的替代方法。我们的具体目的是：1）改善治疗结构，过程和结果之间的关系的评估； 2）利用专家意见和有关治疗辍学原因和流失损耗的理解，以告知PMM建设过程。 AIM 1将通过构建模式的护理模型来实现。 AIM 2将通过解决通过贝叶斯统计数据来构建PMM所需的主观决策来建立目标1，正确解决这些决策中固有的不确定性，并衡量它们对分析得出的结论的影响。