METHODS FOR ANALYZING REPEATED CATEGORICAL DATA

分析重复分类数据的方法

• 批准号: 2700469
• 负责人: Stuart R. Lipsitz
• 金额: $ 11.8万
• 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/2700469
• 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：（改编自研究者摘要）重复测量 在公共卫生的所有领域都进行了研究。 在重复的措施 研究，基本抽样单位是一组或一组受试者; 对集群内的每个对象进行测量。 缺失的回答 和协变量在重复测量研究中很常见， 大多数提案涉及重复数据缺失问题， 措施研究。 首先，我们将评估和扩展现有的方法， 有缺失的纵向研究中关联测度的估计 应答 第二，似然方法可以计算 因此，我们提出了一种伪卡尔曼滤波方法来估计参数 非单调非线性纵向研究的边际模型 缺失的结果。 第三，条件Logistic回归常用于 消除滋扰'固定'集群效应，我们提出了一个修改后的 条件logistic回归适合用于缺失 协变量 第四，由于目前的基因测定技术， 突变，研究人员现在有兴趣估计比值比 基因状态（突变，无突变）和治疗成功之间的关系是， 否）。 不幸的是，并不总是能够进行完整的遗传学检查。 评估以确定基因是否发生突变，从而导致“缺失数据”。 我们将应用缺失数据方法来分析突变状态， 基因 我们最后提出的项目不涉及缺失数据，尽管它 确实评估了一种方法，该方法可能会在聚类数据中给出有偏差的结果 问题研究 我们将确定聚类是否影响通常的检验统计量 在随机临床试验中没有治疗效果。