BAYESIAN PATTERN-MIXTURE MODELS FOR QUALITY OF CARE DATA

描述(由申请人提供)：政策制定者在做出关于医疗保健提供的决定时，依赖于对医疗质量的准确评估。这项任务因卫生服务研究(HSR)中普遍存在的数据问题而变得复杂，这些数据由于客户在完成整个疗程之前离开和/或失去随访而丢失。高铁采用的分析方法经常忽略数据丢失的机制。如果在分析中不考虑缺失数据的真实性质，则可能会导致护理质量模型的偏颇估计，从而阻碍政策制定者提高质量的能力。模式混合模型(PMM)已在统计文献中被提出，作为忽略数据丢失机制的标准分析方法的替代方法。我们的具体目标是：1)当发生治疗退出和研究磨损时，改进对治疗结构、过程和结果之间关系的评估；以及2)利用关于治疗退出和磨损原因的专家意见和知识，以便为PMM的建立过程提供信息。目标1将通过构建模式-混合护理质量模型来实现。目标2将在目标1的基础上，通过贝叶斯统计处理建立项目管理模型所需的主观决定，适当处理这些决定中固有的不确定性，并衡量其对分析得出的结论的影响。

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.