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: 8046185
• 负责人: Somnath Datta
• 金额: $ 16.51万
• 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 2013-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8046185
• 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). PUBLIC HEALTH RELEVANCE: The proposed research will lead to novel methodological and theoretical development in nonparametric/rank tests and estimators for clustered data that will have direct impact on the analyses of a dental data. The results from the proposed research have the potential to transform the way clustered data are handled in practice. Dental researchers and practitioners will be more aware of the informative cluster size issue and employ robust methods such as the ones developed here that accounts for the non-ignorability of the cluster size. -cluster exchangeability remains an issue.

描述（由申请人提供）：该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排名的测试，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） ）使用现有的牙科数据库资源，特别是从 Piedmont 65 + 牙科研究和爱荷华州氟化物研究获得的资源。当簇中的单元数量非恒定/随机且与感兴趣的结果相关时，就会出现信息丰富的簇大小。在牙科数据的背景下，属于个体的所有牙齿将形成一个簇。由于牙齿缺失（成人）与我们计划研究的两种疾病（即牙周病和龋齿）相关，因此我们在皮埃蒙特数据集中获得了潜在的信息簇大小。让经典等级测试适应这种情况是一个方法论上的挑战。例如，即使通过适当的方差估计对聚类依赖性进行调整，两个样本 Wilcoxon 秩和检验也很难在信息聚类下保持正确的大小/显着性水平。 该提案的目标是开发适当的基于等级的测试类别（以及相关的 R 估计器），并研究它们在三个经典问题上的统计特性，这些经典问题适用于具有信息簇大小的簇依赖性下的边际推理。这些是所谓的单样本位置问题（目标 1）、回归问题（目标 2）和关联问题（目标 3）。在每个问题中，我们将使用通用评分函数获得一类检验统计量，这些函数在信息丰富的簇大小场景下保持适当的渐近大小。我们还将研究边际参数的相关 R 估计的性质。还将考虑前两个问题的多元扩展（目标 4）。拟议研究的另一个重要组成部分是扩展这些程序来处理缺失数据，其中可以使用可观察的协变量对缺失机制进行建模（目标 5）。最后，当聚类大小无法提供信息时，例如爱荷华州研究仅包含儿童的情况，我们将能够通过在测试统计数据的构建中纳入聚类特定权重来提高测试的功效（目标 6） 。 公共卫生相关性：拟议的研究将导致聚类数据的非参数/等级检验和估计器的新方法和理论发展，这将对牙科数据的分析产生直接影响。拟议研究的结果有可能改变实践中处理集群数据的方式。牙科研究人员和从业者将更加意识到信息簇大小问题，并采用稳健的方法，例如本文开发的方法，解释簇大小的不可忽略性。 -集群的可互换性仍然是一个问题。