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Bayesian Variable Selection in Generalized Linear Models with Missing Varibles

缺失变量的广义线性模型中的贝叶斯变量选择

基本信息

批准号：
8471550
负责人：
XIAOWEI YANG
金额：
$ 23万
依托单位：
HUNTER COLLEGE
依托单位国家：
美国
项目类别：
财政年份：
2011
资助国家：
美国
起止时间：
2011-08-11 至 2014-08-30
项目状态：
已结题

项目摘要

DESCRIPTION (provided by applicant): In conducting medical research, especially with behavioral and social problems, a challenge for statistical data analysis comes from the problems introduced by missing values. Missing values may be caused by subjective (e.g., nonresponse and dropout) and technical reasons (e.g., censoring over/below quantization level). Generalized linear models (GLMs) are popularly applied in biomedical data analysis where a fundamental task is to interpret or predict an outcome variable by a subset of potentially explanatory variables. Given an incomplete data set, practitioners frequently resort to the strategy of case-deletion where individuals are excluded from consideration if they miss any of the variables targeted for analysis. This is the default option used in many software packages. Yet, case-deletion may not only sacrifice useful information, but also give rise to biased estimates because it requires strong assumptions on the missingness mechanisms. A more satisfactory solution for missing data problems involves multiple imputation, where several imputations are created for the same set of missing values. The variance between imputations reflects the uncertainty due to missingness. Across multiply imputed data sets, however, traditional variable selection methods (based on significance tests or various criteria) often result in models with different selected predictors, thus presenting a problem of combining the models to make final inferences. In this R01 proposal with a 3-year research plan, we aim to develop two alternative strategies of variable selection for GLMs with missing values by drawing on a Bayesian framework. One approach, which we call "impute, then select" (ITS) involves initially performing multiple imputation and then applying Bayesian variable selection to the multiply imputed data sets. The second strategy - "simultaneously impute and select" (SIAS) - is to conduct Bayesian variable selection and missing data imputation simultaneously within one Markov Chain Monte Carlo (MCMC) process. ITS and SIAS offer two generic frameworks within which various Bayesian variable selection algorithms and missing data imputation algorithms can be implemented. Both strategies will be developed, evaluated, and implemented into an R library for normal regression, binomial regression, and other GLMs with categorical and/or continuous explanatory variables. Practical data sets from several studies on substances abuse and childhood autism will be used to address the effectiveness and flexibility of the proposed strategies. Development of these procedures and contribution of the software to statisticians and researchers in medical research would significantly improve the quality of evaluation of important and clinically relevant data.

描述（由申请人提供）：在进行医学研究时，特别是涉及行为和社会问题时，统计数据分析的挑战来自于缺失值带来的问题。缺失值可能是由于主观原因（如无响应和退出）和技术原因（如审查超过/低于量化水平）造成的。广义线性模型（GLMs）广泛应用于生物医学数据分析，其基本任务是通过潜在解释变量的子集来解释或预测结果变量。给定一个不完整的数据集，从业者经常诉诸于案例删除策略，即如果个体错过了任何用于分析的变量，则将其排除在考虑之外。这是许多软件包中使用的默认选项。然而，病例删除不仅可能牺牲有用的信息，而且还会产生有偏见的估计，因为它需要对缺失机制进行强有力的假设。对于缺失数据问题，一个更令人满意的解决方案涉及多个输入，其中为同一组缺失值创建多个输入。估算值之间的差异反映了由于缺失造成的不确定性。然而，在多个输入数据集中，传统的变量选择方法（基于显著性检验或各种标准）往往会导致模型中选择的预测因子不同，从而出现将模型组合起来进行最终推断的问题。在这个具有3年研究计划的R01提案中，我们的目标是通过借鉴贝叶斯框架，为缺失值的glm开发两种变量选择的替代策略。一种方法，我们称之为“输入，然后选择”（ITS），包括首先执行多个输入，然后将贝叶斯变量选择应用于多个输入数据集。第二种策略是“同时输入和选择”（SIAS），即在一个马尔可夫链蒙特卡罗（MCMC）过程中同时进行贝叶斯变量选择和缺失数据输入。ITS和SIAS提供了两个通用框架，其中可以实现各种贝叶斯变量选择算法和缺失数据输入算法。这两种策略都将被开发、评估并实现到R库中，用于正态回归、二项回归和其他具有分类和/或连续解释变量的glm。将利用几项关于药物滥用和儿童自闭症的研究的实际数据集来讨论拟议战略的有效性和灵活性。这些程序的开发和软件对医学研究中的统计学家和研究人员的贡献将大大提高重要和临床相关数据的评估质量。