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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有三个明显的特点：（1）药物依赖的行为相关性导致数据矩阵中的缺失值，这是由于无应答或脱落，（2）重复测量的最大数量很大，（3）二元重复测量，而不是连续测量，是最常见的。本B/START提案旨在确定最佳方法，用于研究制定进行不完全二元纵向数据分析的策略的可能性。基于马尔可夫随机过程的过渡模型是一种比基于拟似然函数的边际模型和广义线性混合模型等纵向建模方法更合适的建模策略。在计算上，二进制重复测量的过渡模型，更容易拟合和应用后，数据矩阵已被改造，因为它们只是逻辑或对数回归模型。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更可解释的分析推理。对每个实验对象进行大量的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，我们也有更多的选择来处理丢失的数据。该项目将开发，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，间歇性缺失数据被插补多次，由于脱落而缺失的数据保持原样，然后将过渡模型拟合到每个部分插补的数据集，最后将多个结果组合起来以进行最终推断。在传销方法中，失踪状态被视为第三类，将重复测量扩展为三类测量。