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Loss function based ranking methods with applications to health services research and gene expression.

机译：基于损失函数的排序方法，应用于健康服务研究和基因表达。

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摘要

Ranking methods are important in performance comparison of a group of units and in identifying outlying units. Examples of the former are to rank health services providers or educational institutions; examples of this later are to identify regions with elevated disease incidence and to identify differentially expressed genes. When (posterior) distributions of the parameters of interest are stochastically ordered, all reasonable ranking methods should lead to same result. However, when these distributions are not stochastically ordered, the performance of ranks based on traditional statistics (e.g., Maximum likelihood estimates, Bayes Posterior Means, hypothesis test statistics) are usually not optimal; since these statistics were not designed to produce effective ranks.; In this thesis, we consider loss function based ranking methods. With loss functions as guides, we use both parametric and semi-parametric hierarchical models to produce ranks and evaluate them by both mathematical analysis and computer simulation. We find that estimates that minimize Squared Error Loss for ranks (e.g., the posterior mean ranks) are effective, but in many applications interest focuses on identifying the relatively good (e.g. 7 in the upper 10%) or relatively poor performers. Therefore, we construct loss functions and optimizing rank estimates that address these goals and evaluate these and other candidate estimates. We apply our new ranking methods to two applications: ranking dialysis providers based on standardized mortality ratios using data from the United States Renal Data System and selection of the most differentially expressed genes using data on two groups of lung cancer patients. We compare results to traditional analyses of these data.
机译：排名方法对于一组单位的绩效比较和确定外围单位很重要。前者的例子是对卫生服务提供者或教育机构进行排名；稍后的例子是确定疾病发病率升高的区域并鉴定差异表达的基因。当随机排列感兴趣参数的（后）分布时，所有合理的排序方法都应得出相同的结果。但是，如果这些分布不是随机排列的，则基于传统统计数据（例如，最大似然估计，贝叶斯后验均值，假设检验统计数据）的排名表现通常不是最佳的；因为这些统计数据并非旨在产生有效的排名。本文考虑基于损失函数的排序方法。以损失函数为指导，我们使用参数和半参数层次模型来生成等级，并通过数学分析和计算机仿真对其进行评估。我们发现，使等级（例如后平均等级）的平方误差损失最小化的估计是有效的，但是在许多应用中，关注点集中在确定性能相对较好（例如最高10％的7％）或性能相对较差的产品。因此，我们构建损失函数并优化可实现这些目标的等级估计，并评估这些和其他候选估计。我们将新的排名方法应用于两个应用程序：使用来自美国肾脏数据系统的数据，基于标准化死亡率对透析提供者进行排名，并使用两组肺癌患者的数据选择表达差异最大的基因。我们将结果与这些数据的传统分析进行比较。

著录项

  • 作者

    Lin, Rongheng.;

  • 作者单位

    The Johns Hopkins University.;

  • 授予单位 The Johns Hopkins University.;
  • 学科 Biology Biostatistics.
  • 学位 Ph.D.
  • 年度 2006
  • 页码 121 p.
  • 总页数 121
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 生物数学方法;
  • 关键词

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