Rank tests for clustered data with potentially informative cluster size: Novel st

对具有潜在信息簇大小的簇数据进行排名测试：Novel st

• 批准号: 8321444
• 负责人: Somnath Datta
• 金额: $ 14.85万
• 依托单位: UNIVERSITY OF LOUISVILLE
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-01 至 2014-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8321444
• 关键词: Accounting; Adult; Aftercare; Data; Data Analyses; Data Set; Databases; Dental; Dental Hygiene; Dental Research; Dental caries; Dependence; Development; Disease; Environment; Fluorides; Goals; Individual; Iowa; Lead; Literature; Location; Measurement; Methods; Modeling; Only Child; Oral health; Outcome; Outcome Measure; Paired Comparison; Patients; Periodontal Diseases; Physiological; Population; Procedures; Property; Rank-Sum Tests; Research; Research Personnel; Resources; Sampling; Statistical Methods; Sum; Testing; Tooth Loss; Tooth structure; Weight; Work; base; improved; interest; novel; statistics

DESCRIPTION (provided by applicant): The overall goal of this proposal is to develop appropriate rank based tests for clustered data when the cluster size is potentially informative and apply the resulting methods for various marginal comparisons (e.g., average condition of teeth before and after treatment) using existing dental database resources, specifically as obtained from the Piedmont 65 + Dental Study and Iowa Fluoride Study. Informative cluster size arises when the number of units in a cluster is non-constant/random and in correlation with the outcome of interest. In the context of dental data, all teeth belonging to an individual will form a cluster. Since tooth loss (in adult) is correlated with two of the diseases we are planning to study, namely, periodontal disease and dental caries, we have potentially informative cluster sizes in the Piedmont data sets. It is a methodological challenge to adapt a classical rank test to such situations. As for example, the two sample Wilcoxon rank sum test has difficulty maintaining the correct size/significance level under informative clustering even if it is adjusted for cluster dependence through appropriate variance estimate. This proposal has a goal of developing proper classes of rank based tests (and related R estimators) and studying their statistical properties for three classical problems adapted to marginal inference under cluster dependence with informative cluster size. These are the so called one sample location problem (Aim 1), the regression problem (Aim 2) and the association problem (Aim 3). In each of these problems, we will obtain a class of test statistics using general score functions that maintain proper asymptotic size under the informative cluster size scenario. We will also study the properties of the related R estimates of marginal parameters. Multivariate extensions of the first two problems will also be considered (Aim 4). Another signification component of the proposed research will be to extend these procedures to handle missing data where the missingness mechanism can be modeled using observable covariates (Aim 5). Finally, when the cluster size is not informative, as in the case of Iowa Study which comprises of children only, we will be able to increase the power of our tests by incorporating cluster specific weights in the construction of our test statistics (Aim 6).

描述（由申请人提供）：本提案的总体目标是，当聚类大小可能提供信息时，为聚类数据开发适当的基于秩的检验，并将所得方法应用于各种边际比较（例如，治疗前后牙齿的平均状况），特别是从皮埃蒙特65 +牙科研究和爱荷华州氟化物研究中获得的。当集群中的单元数量是非恒定/随机的并且与感兴趣的结果相关时，信息集群大小出现。在牙科数据的上下文中，属于个体的所有牙齿将形成集群。由于牙齿脱落（成人）与我们计划研究的两种疾病相关，即牙周病和龋齿，因此我们在皮埃蒙特数据集中具有潜在的信息聚类大小。这是一个方法上的挑战，以适应这种情况下，经典的秩检验。例如，双样本Wilcoxon秩和检验在信息聚类下难以保持正确的大小/显著性水平，即使通过适当的方差估计调整聚类依赖性。 该建议的目标是开发适当的类的秩为基础的测试（和相关的R估计），并研究其统计特性的三个经典问题，适用于边缘推断下集群依赖与信息集群大小。这些是所谓的单样本定位问题（目标1），回归问题（目标2）和关联问题（目标3）。在这些问题中，我们将获得一类测试统计量使用一般的评分函数，保持适当的渐近规模下的信息集群大小的情况下。我们还将研究边际参数的相关R估计的性质。前两个问题的多元扩展也将被考虑（目标4）。拟议研究的另一个重要组成部分将是扩展这些程序，以处理缺失数据，其中缺失机制可以使用可观察的协变量进行建模（目标5）。最后，当聚类大小不能提供信息时，如爱荷华州研究仅包括儿童，我们将能够通过在检验统计量的构建中加入聚类特定权重来增加检验的功效（目标6）。