Transition Model for Incomplete Longitudinal Binary Data

不完整纵向二进制数据的转换模型

基本信息

批准号：
6676189
负责人：
XIAOWEI YANG
金额：
$ 6万
依托单位：
BAYESSOFT, INC.
依托单位国家：
美国
项目类别：
财政年份：
2003
资助国家：
美国
起止时间：
2003-07-15 至 2004-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/6676189
关键词：
computer data analysis computer simulation data collection methodology /evaluation mathematical model model design /development statistics /biometry substance abuse related disorder time resolved data

项目摘要

DESCRIPTION (provided by applicant): Compared with longitudinal designs in other fields, at least three distinct features are observed with the designs in substance abuse treatment studies: (1) behavioral correlates of drug dependence result in missing values in the data matrix due to either nonresponse or dropout, (2) the maximum number of repeated measures is large, and (3) binary repeated measures, as opposed to continuous measures, are most often seen. This B/START proposal aims to identify optimal methods for studying the probability of developing strategies to conduct incomplete binary longitudinal data analysis. Determined by the above three features, transition models, based on Markov stochastic process, provide a more appropriate modeling strategy than other longitudinal modeling choices such as marginal models using quasi-likelihood functions and generalized linear mixed models. Computationally, transition models for binary repeated measures are easier to be fitted and applied after the data matrix has been reformed, since they are just logistic or Iogit regression models. Making use of the past responses in predicting the future ones usually produces analytical inferences that are more meaningful and interpretable. Large number of repeated measures on each experiment subject makes Markov process modeling more appealing. Using transitional models, we also have more choices to handle missing data. The proposed project will develop, compare, and evaluate two missing data strategies: multiple partial imputation (MPI), and multicategory-logit model (MLM). In MPI approach, intermittent missing data are imputed several times with missing data due to dropout left as they are, and then transition models will be fitted for each of these partially imputed data sets, and finally the multiple results are combined to make one final inference. In MLM approach, status of missingness is treated as a third category to extend the repeated measures into three-category ones.

描述（由申请人提供）：与其他领域的纵向设计相比，使用药物滥用治疗研究中的设计观察到至少三个不同的特征：（1）药物依赖性的行为相关性导致由于无响应或脱落而导致的数据矩阵中缺少值，（2）最大的重复测量数是较大的，并且（3）二进制重复测量，相对于连续的测量值，通常是相反的衡量标准。该B/开始建议旨在确定研究制定策略以进行不完整二进制纵向数据分析的概率的最佳方法。由以上三个特征确定，基于马尔可夫随机过程的过渡模型比其他纵向建模选择（例如使用准类型函数和广义线性混合模型）提供了更合适的建模策略。在计算上，对二进制重复测量的过渡模型在对数据矩阵进行改革后更容易拟合和应用，因为它们只是逻辑或IOGIT回归模型。利用过去的响应预测未来的响应通常会产生更有意义和可解释的分析推论。每个实验对象的大量重复措施使马尔可夫过程建模更具吸引力。使用过渡模型，我们还有更多选择来处理丢失的数据。拟议的项目将开发，比较和评估两种缺失的数据策略：多个部分插补（MPI）和多级逻辑模型（MLM）。在MPI方法中，间歇性丢失的数据被归推几次，由于剩余的辍学，数据丢失，然后将适合这些部分估算的数据集的过渡模型，最后将多个结果组合在一起以进行最终推断。在MLM方法中，缺失的状态被视为第三类，将重复测量的措施扩展到三类措施。