METHODS FOR ANALYZING REPEATED CATEGORICAL DATA

分析重复分类数据的方法

• 批准号: 2007984
• 负责人: Stuart R. Lipsitz
• 金额: $ 12.13万
• 依托单位: DANA-FARBER CANCER INST
• 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/2007984
• 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：（根据调查员的摘要进行了改编）重复措施 在公共卫生的所有领域进行研究。 在重复的措施中 研究，基本的抽样单元是一组或群体。一个 对集群中的每个主题进行测量。 缺少“响应” 在重复措施研究中，协变量是常见的事件， 该提案的大部分涉及重复丢失的数据问题 衡量研究。 首先，我们将评估并扩展现有方法 在纵向研究中估算关联的量度 回答。 其次，可能性方法可以计算 密集型，因此我们提出了一种伪造方法来估计参数 纵向研究的边际模型的边缘模型 缺少结果。 第三，有条件的逻辑回归通常用于 消除令人讨厌的“固定”聚类效应，我们提出了一个修改的 有条件的逻辑回归，可用于丢失 协变量。 第四，由于当前确定基因的技术 突变，研究人员现在有兴趣估计优势比 在遗传状态（突变，无突变）和治疗成功之间，是的， 不）。 不幸的是，并非总是可以执行完整的遗传 评估以确定基因是否已突变，从而导致“缺失数据”。 我们将应用缺少的数据方法来分析 基因。 我们提出的最终项目并不涉及丢失数据，尽管它 确实评估了一种可以在群集数据中产生偏差结果的方法 研究。 我们将确定聚类是否影响通常的测试统计数据 在一项随机临床试验中无治疗效果。