BAYESIAN PATTERN-MIXTURE MODELS FOR QUALITY OF CARE DATA

描述（由申请人提供）：决策者在做出有关医疗保健服务的决策时，依赖于对医疗质量的准确评估。这项任务是复杂的普遍存在的问题，在卫生服务研究（HSR）的数据，由于客户端离开之前完成一个完整的疗程和/或损失的后续行动。 在高速铁路中采用的分析方法经常忽略数据丢失的机制。如果在分析中没有考虑到缺失数据的真实性质，可能会导致护理质量模型的偏倚估计，从而阻碍决策者提高质量的能力。模式混合模型（PMM）已在统计文献中提出，作为标准分析方法的替代方法，忽略了数据丢失的机制。我们的具体目标是：1）当发生治疗脱落和研究流失时，改善对治疗结构、过程和结局之间关系的评估; 2）利用专家意见和关于治疗脱落和流失原因的知识，为PMM构建过程提供信息。目标1将通过构建模式混合护理质量模型来实现。目标2将建立在目标1的基础上，通过贝叶斯统计来解决建立PMM所需的主观决策，适当解决这些决策中固有的不确定性，并衡量它们对分析结论的影响。

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.