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

描述(由申请人提供)：本提案的总体目标是，当集群大小可能提供信息时，为集群数据开发适当的基于排名的测试，并使用现有的牙科数据库资源，特别是从皮埃蒙特65+牙科研究和爱荷华州氟化物研究获得的资源，将所产生的方法应用于各种边际比较(例如，治疗前和治疗后的牙齿平均状况)。当一个簇中的单元数目是非恒定/随机的并且与感兴趣的结果相关时，信息性的簇大小就会出现。在牙齿数据的上下文中，属于个人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相关，即牙周病和龋齿，我们在皮埃蒙特数据集中有潜在的信息性簇大小。使经典的等级检验适应这种情况是一个方法论上的挑战。例如，两个样本的Wilcoxon秩和检验在信息性聚类下很难保持正确的大小/显著水平，即使通过适当的方差估计针对聚类相关性进行了调整。这一建议的目的是发展适当的秩基检验(以及相关的R估计)，并研究三个经典问题的统计性质，这些经典问题适用于具有信息簇大小的簇依赖下的边际推理。它们是所谓的单样本选址问题(目标1)、回归问题(目标2)和关联问题(目标3)。在每个问题中，我们将使用一般的得分函数来获得一类测试统计量，这些函数在信息性的簇大小情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。前两个问题的多元扩展也将被考虑(目标4)。拟议研究的另一个有意义的部分将是将这些程序扩展到处理缺失数据，其中缺失机制可以使用可观测协变量来建模(目标5)。最后，当集群大小不能提供信息时，就像爱荷华州的研究那样，只由儿童组成，我们将能够通过在构建测试统计数据(目标6)中纳入集群特定权重来增加测试的能力。 公共卫生相关性：拟议的研究将在非参数/等级检验和集群数据估计器方面带来新的方法学和理论发展，这将对牙科数据的分析产生直接影响。这项拟议研究的结果有可能改变实际中处理集群数据的方式。牙科研究人员和从业者将更多地意识到信息性的集群大小问题，并采用诸如这里开发的那些稳健的方法，这些方法解释了集群大小的不可忽视。 -群集可互换性仍然是一个问题。

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.