Hierarchical Models in Health Services Research

描述（由申请人提供）：数据结构和推断目标，用于提供者和医生分析，基因表达研究；大多数与健康相关的科学和政策研究都需要一个分层的随机效应模型。在有效的不确定性评估的背景下，对群体参数、方差成分和单位特异性随机效应（提供者、临床医生、区域或基因特异性潜在属性）进行有效和有效的估计。适当发展的贝叶斯模型有效地实现了这些目标，考虑了方差成分和其他不确定性，提高了对随机效应的估计。在损失函数的指导下，该方法构建了非标准推理，如排名（包括识别极差和良好的表现）和估计随机单元特定效应的直方图。推断必须对目标目标执行良好，并将效率（充分利用可用信息）与鲁棒性（在广泛的潜在假设范围内的高效率）结合起来。拟议的研究建立在最近开发和评估的排名/百分位方法之上。这些研究已经确定了需要进一步研究的领域，包括新的排名方法，以解决当前方法的潜在缺点，开发方法操作特征的数据分析量化，实现单变量和多变量稳健先验和直方图估计。研究将基于数学和模拟分析；分析卫生服务和基因表达数据集，说明方法并产生实质性发现。基于r的模拟和数据分析软件的持续发展将确保“可重复研究”，并允许其他人实施程序。约翰霍普金斯大学生物统计学博士预科生将得到支持和指导。拟议的研究将产生创新的统计方法，为解决多个目标的权衡提供指导，传达有趣和翔实的案例研究，教育和适应博士生。

DESCRIPTION (provided by applicant):The data structure and inferential goals for provider and physician profiling, gene expression studies; most health-related science and policy studies require a hierarchical, random effects model. Valid and efficient estimation of population parameters, variance components, and unit-specific random effects (provider, clinician-, region- or gene-specific latent attributes), all in the context of valid uncertainty assessments. Properly developed Bayesian models effectively accomplish these goals, accounting for variance components and other uncertainties, improving estimation of random effects. Guided by a loss function, the approach structures non-standard inferences such as ranking (including identification of extremely poor and good performers) and estimating the histogram of random unit-specific effects. Inferences must perform well for the target goal(s) and combine efficiency (making good use of the available information) with robustness (high efficiency over a broad range of underlying assumptions). The proposed research builds on recently developed and evaluated ranking/percentiling methods. These studies have identified areas requiring additional research, including new ranking methods to address potential drawbacks of current methods, developing data analytic quantification of a method's operating characteristics, implementation of univariate and multivariate robust priors and histogram estimates. Research will be based on mathematical and simulation-based analysis; analysis of health services and gene expression datasets that illustrate approaches and produce substantive findings. Continued development of R-based, simulation and data analysis software will ensure "reproducible research" and allow others to implement procedures. A pre-doctoral Biostatistics student at Johns Hopkins will be supported and mentored. Proposed research will produce innovative statistical methodology, provide guidance on trade-offs in addressing multiple goals, communicate interesting and informative case studies, educate and acculturate a pre-doctoral student.