Best Predictor Methods for Correlated Data

摘要0103792本项目旨在开发用于分析相关、非正态分布数据的统计方法。 这些数据通常出现在临床试验、家族遗传学研究、生态学研究以及其他各种情况下，即在同一受试者、同一家族、同一地点或同一地块上收集二进制或计数数据时。 在分析这类数据时，如果不考虑相关性，很容易得出误导性的结论。这些方法将基于在广义线性混合模型的范围内推导方差分量和回归参数的无偏估计方程。 我们的目标是获得具有良好小样本性能的广泛实用的方法。 新开发的方法的性能将在以下方面单独进行评估：计算的难易程度、无偏倚、均方误差小、计算准确标准误差的难易程度、计算准确置信区间和进行假设检验的难易程度。 这些方法还将使用相同的标准与现有方法（最大似然法、高阶拉普拉斯近似法、惩罚准似然法和广义估计方程）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.