MISSPECIFICATION IN LONGITUDINAL MODELS

描述：(改编自《调查员摘要》)此次续订 应用程序正在请求支持以扩展第一个 调查人员描述了资金周期模型的影响 线性回归中的错误说明到第二个周期，在那里他们将 专注于有顺序和绝对结果的分析。具体地说， 调查人员建议评估遗漏(令人困惑)的影响 协变量、测量误差和选择偏差对参数的影响 通过Logistic回归、GEE和其他分类方法进行估计。 关于对混淆的评估，调查人员提议 概括他们早期模型的混乱结构，并将其扩展到 将线性结果应用于GEE估计。测量中需要解决的问题 错误包括扩展了社会调查中使用的潜在变量方法 科学到生物统计学，以评估错误分类的影响 二元和有序的结果衡量标准，并审查 关于混杂因素的分布假设。与以下内容关联的主题 要解决的选择偏差包括评估信息性的影响 对选定模型的审查和模型对抽样的调整 方法(如过抽样)。最后，检测的程序 将开发出模型不匹配的产品。该应用程序隐含地建议 所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.