Hierarchical Models in Health Services Research

描述（由申请人提供）：提供者和医生分析，基因表达研究的数据结构和推理目标;大多数与健康相关的科学和政策研究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、方差分量和单位特定随机效应（提供者、临床医生、地区或基因特定潜在属性）进行有效和高效的估计。正确开发的贝叶斯模型有效地实现了这些目标，考虑了方差分量和其他不确定性，改善了随机效应的估计。在损失函数的指导下，该方法构建了非标准的推论，如排名（包括识别极差和良好的表现）和估计随机单位特定效应的直方图。推理必须对目标目标表现良好，并将联合收割机效率（充分利用可用信息）与鲁棒性（在广泛的基本假设范围内的高效率）相结合。拟议的研究建立在最近开发和评估的排名/排序方法。这些研究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，开发数据分析量化方法的操作特性，实施单变量和多变量的强大先验和直方图估计。研究将基于数学和基于模拟的分析;对卫生服务和基因表达数据集的分析，这些数据集说明了方法并产生了实质性的发现。继续开发基于R的模拟和数据分析软件将确保“可重复的研究”，并使其他人能够实施程序。约翰霍普金斯大学生物统计学博士前学生将得到支持和指导。拟议的研究将产生创新的统计方法，在解决多个目标的权衡提供指导，交流有趣和翔实的案例研究，教育和文化的博士前学生。

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.