MISSPECIFICATION IN LONGITUDINAL MODELS

描述：（改编自研究者摘要）本次更新 应用程序正在请求支持，以扩展第一个 供资周期，其中调查人员描述了模型的影响 线性回归中的错误设定到第二个周期， 重点分析有序和分类结果。 具体而言是 研究者建议评估遗漏（混杂）的影响 协变量、测量误差和选择偏倚对参数的影响 通过逻辑回归、GEE和其他分类方法估计。 关于混杂评估，研究者建议 概括了他们早期模型的混杂结构，并扩展了 线性结果GEE估计。 衡量中应处理的问题 错误包括扩展潜变量的方法中使用的社会 生物统计学，以评估错误分类的影响 二进制和有序结果的措施，并检查的影响， 关于混杂因素的分布假设。 与以下主题相关的主题 要解决的选择偏差包括评估信息的影响， 对选定模型进行删失，并根据抽样调整建模 方法（如过采样）。 最后，检测程序 将开发不适合的模型。 申请书暗示 开发的技术将应用于现有的数据集。

DESCRIPTION: (Adapted from Investigator's Abstract) This renewal application is requesting support to extend the findings of the first funding cycle where the investigators described the impact of model misspecification in linear regression to the second cycle where they will focus on analyses with ordinal and categorical outcomes. Specifically, the investigators propose to assess the impact of omitted (confounding) covariates, the impact of measurement error and selection bias on parameters estimated by logistic regression, GEE, and other categorical approaches. With respect to the assessment of confounding, the investigators propose to generalize the confounding structure of their earlier models and to extend the linear results to GEE estimation. Topics to be addressed in measurement error include extending the latent variables approaches used in the social sciences to biostatistics, to assess the impact of misclassification in binary and ordinal outcome measures, and to examine the impact of distributional assumptions concerning confounders. Topics associated with selection bias to be addressed include assessing the impact of informative censoring on selected models and adjustments of modeling to sampling approaches (such as oversampling). Finally, the procedures for detecting model lack of fit will be developed. The application implicitly suggests that the techniques developed will be applied to existing data sets.