Best Predictor Methods for Correlated Data

摘要本项目建议开发统计方法，用于分析相关的、非正态分布的数据。这样的数据通常出现在临床试验、家族遗传学研究、生态学研究以及各种其他情况下，当收集关于同一对象、同一家庭、同一地点或从土地地块上的二进制或计数数据时。在分析这类数据时不考虑相关性很容易导致误导性的结论。这些方法将基于在广义线性混合模型的背景下推导方差分量和回归参数的无偏估计方程。其目标是得到具有良好小样本性能的具有广泛实用性的方法。新开发的方法的性能将从计算的简便性、无偏差、均方误差小、计算准确的标准误差的容易程度以及计算准确的可信区间和执行假设检验的容易程度等方面进行评估。这些方法还将使用相同的标准与现有的方法(最大似然、高阶拉普拉斯近似、惩罚准似然和广义估计方程)进行比较。

ABSTRACT 0103792This project proposes to develop statistical methods useful for the analysis of correlated, non-normally distributed data. Such data arises commonly in clinical trials, in familial genetic studies, in ecological studies, and in a variety of other contexts when binary or count data is gathered on the same subject, on the same family, at the same site, or from the plot of land. Failure to account for correlations in the analysis of such data can readily lead to misleading conclusions.The methods will be based on deriving unbiased estimating equations for both variance components and regression parameters within the context of a generalized linear mixed model. The goal is to derive methods of wide utility with good small sample performance. The performance of the newly developed methods will be assessed on their own with regard to ease of computation, lack of bias, smallness of mean square error, the ease with which accurate standard errors can be computed, and the ease of calculating accurate confidence intervals and performing hypothesis tests. The methods will also be compared to extant methods (maximum likelihood, higher order Laplace approximations, penalized quasi-likelihood and generalized estimating equations) using the same criteria.