METHODS FOR ANALYZING REPEATED CATEGORICAL DATA

分析重复分类数据的方法

• 批准号: 6145746
• 负责人: Stuart R. Lipsitz
• 金额: $ 8.3万
• 依托单位: MEDICAL UNIVERSITY OF SOUTH CAROLINA
• 依托单位国家: 美国
• 财政年份: 1992
• 资助国家: 美国
• 起止时间: 1992-05-06 至 2000-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6145746
• 关键词: clinical trials; computer data analysis; computer simulation; data collection methodology /evaluation; gene mutation; health science research analysis /evaluation; longitudinal human study; mathematical model; method development; statistics /biometry

DESCRIPTION: (Adapted from investigator's abstract) Repeated measures studies are undertaken in all areas of Public Health. In repeated measures studies, the basic sampling unit is a group or cluster of subjects; a measurement is made on each subject within the cluster. Missing 'responses and covariates are common occurrences in repeated measures studies, and the majority of the proposal relates to missing data problems in repeated measures studies. First, we will evaluate and extend existing methods for estimating measures of association in longitudinal studies with missing responses. Secondly, the likelihood methods can be computationally intensive, so we propose a pseudolikelihood methods to estimate parameters of marginal models for longitudinal studies with nonignorable nonmonotone missing outcomes. Thirdly, conditional logistic regression is often used to eliminate nuisance 'fixed' cluster effects, and we propose a modified conditional logistic regression which is appropriate to use with missing covariates. Fourthly, because of current techniques of determining gene mutation, investigators are now interested in estimating the odds ratio between genetic status (mutation, no mutation) and treatment success yes, no). Unfortunately, it is not always possible to perform a complete genetic evaluation to determine if a gene has mutated, resulting in 'missing data'. We will apply missing data methods to analyze the mutation status of the gene. Our final proposed project does not concern missing data, although it does evaluate a method that can give biased results in clustered data studies. We will determine if clustering affects the usual test statistics of no treatment effect in a randomized clinical trial.

描述：（改编自研究者的摘要）重复测量 研究在公共卫生的所有领域进行。 在反复的措施中 研究中，基本抽样单位是一组或一组受试者；一个 对集群内的每个受试者进行测量。 缺少“回复” 和协变量在重复测量研究中很常见，并且 该提案的大部分涉及重复数据丢失问题 措施研究。 首先，我们将评估和扩展现有方法 估计纵向研究中缺失的相关性测量 回应。 其次，似然法可以通过计算 密集，所以我们提出了一种伪似然方法来估计参数 不可忽略的非单调纵向研究的边际模型 缺少结果。 第三，条件逻辑回归常用于 消除令人讨厌的“固定”集群效应，我们提出了一种改进的 条件逻辑回归适用于缺失 协变量。 第四，由于目前的基因检测技术 突变，研究人员现在有兴趣估计优势比 遗传状态（突变、无突变）和治疗成功之间是的， 不）。 不幸的是，并不总是能够进行完整的遗传分析。 评估以确定基因是否发生突变，从而导致“数据缺失”。 我们将应用缺失数据方法来分析突变状态 基因。 我们最终提出的项目并不涉及缺失数据，尽管它 确实评估了一种可以在聚类数据中给出有偏差结果的方法 研究。 我们将确定聚类是否影响通常的测试统计数据 随机临床试验中没有治疗效果。