权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

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，药物滥用治疗研究的设计至少观察到三个明显的特征：（1）药物依赖的行为相关性导致数据矩阵中由于无应答或退出而缺失值；（2）重复测量的最大数量很大；（3）与连续测量相反，二元重复测量是最常见的。该 B/START 提案旨在确定研究制定策略以进行不完整二元纵向数据分析的概率的最佳方法。由上述三个特征决定，基于马尔可夫随机过程的转移模型提供了比其他纵向建模选择（例如使用拟似然函数的边际模型和广义线性混合模型）更合适的建模策略。在计算上，二元重复测量的转换模型在数据矩阵重组后更容易拟合和应用，因为它们只是逻辑回归或 Iogit 回归模型。利用过去的反应来预测未来的反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测量使得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缺失数据。拟议的项目将开发、比较和评估两种缺失数据策略：多重部分插补 (MPI) 和多类别 Logit 模型 (MLM)。在MPI方法中，间歇性缺失数据被多次插补，由于dropout而缺失的数据保持原样，然后将为每个部分插补的数据集拟合转换模型，最后将多个结果组合起来做出最终的推断。在传销方法中，缺失状态被视为第三类，将重复测量扩展为三类测量。