Robust Missing Data Methods for Categorical Regression

用于分类回归的稳健缺失数据方法

• 批准号: 6953713
• 负责人: Steven S Henley
• 金额: $ 60.7万
• 依托单位: MARTINGALE RESEARCH CORPORATION
• 依托单位国家: 美国
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-09-25 至 2007-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6953713
• 关键词: alcoholism /alcohol abuse; clinical research; computer program /software; data collection methodology /evaluation; human data; mathematical model; method development; model design /development; statistics /biometry; substance abuse epidemiology

DESCRIPTION (provided by applicant): Improved methods for obtaining robust statistical inferences from categorical regression models in the presence of missing data and model misspecification would be an invaluable tool to the epidemiological and health care research communities. Presently epidemiological models are typically designed to identify patterns of alcohol-related symptoms, define criteria of alcohol use disorders, and evaluate policies regulating use and distribution of alcoholic beverages. Such models frequently rely on datasets that contain incomplete-data. While commercially available statistical software provides some automated missing value procedures (e.g., data imputation, Expectation-Maximization), further theoretical and empirical research is required to develop more robust statistical methods. In its Phase I feasibility study Martingale Research successfully developed robust estimation and inference algorithms that combine recent advances in stochastic estimation, asymptotic statistics, and generalized logistic regression that are suited to categorical regression modeling for epidemiological problems in the presence of missing data and model misspecification. These results were verified in simulation studies and the methods were applied to an alcohol-related research problem. Additionally, new theoretical research that unifies missing data and model misspecification was developed to support the development of new robust missing data inferential statistics. Phase II research will extend Phase I findings to develop and implement new robust missing data methods for categorical regression modeling in the areas of: i) hypothesis testing on parameter estimates, ii) standard error estimation, iii) model selection criteria, and iv) specification testing. The Phase II experimental design will utilize Monte Carlo simulation bootstrapping methods for the purposes of evaluating the missing data methods using representative alcohol-related databases. Specifically, the simulation studies will empirically characterize the appropriateness of the large sample assumptions for both consistent estimation and statistical inference. These simulation study methodologies in conjunction with the new robust missing data methods will be integrated into a prototype user-friendly standalone software package for the purposes of supporting epidemiological and health related regression modeling. In summary, Phase II research will establish the essential technical foundation for Phase III commercialization with the long-term objective of providing a suite of new missing data handling methods as an advanced statistical tool for recession modeling that improves epidemiological and health-related research.

描述（由申请人提供）：在存在缺失数据和模型错误指定的情况下从分类回归模型获得稳健统计推论的改进方法将是流行病学和卫生保健研究界的宝贵工具。目前，流行病学模型通常旨在识别酒精相关症状的模式，定义酒精使用障碍的标准，并评估规范酒精饮料使用和分销的政策。此类模型经常依赖于包含不完整数据的数据集。虽然商用统计软件提供了一些自动缺失值程序（例如数据插补、期望最大化），但仍需要进一步的理论和实证研究来开发更稳健的统计方法。在其第一阶段可行性研究中，Martingale Research 成功开发了稳健的估计和推理算法，该算法结合了随机估计、渐近统计和广义逻辑回归的最新进展，适合在存在缺失数据和模型错误指定的情况下对流行病学问题进行分类回归建模。这些结果在模拟研究中得到了验证，并将这些方法应用于与酒精相关的研究问题。此外，还开发了统一缺失数据和模型错误指定的新理论研究，以支持新的稳健缺失数据推断统计的开发。 第二阶段研究将扩展第一阶段的研究结果，以开发和实施新的稳健缺失数据方法，用于以下领域的分类回归建模：i）参数估计的假设检验，ii）标准误差估计，iii）模型选择标准，以及iv）规格测试。第二阶段实验设计将利用蒙特卡罗模拟引导方法，以使用具有代表性的酒精相关数据库评估缺失数据方法。具体来说，模拟研究将凭经验描述大样本假设对于一致估计和统计推断的适当性。这些模拟研究方法与新的稳健缺失数据方法将被集成到一个用户友好的原型独立软件包中，以支持流行病学和健康相关的回归建模。总之，第二阶段的研究将为第三阶段的商业化奠定必要的技术基础，其长期目标是提供一套新的缺失数据处理方法，作为衰退建模的先进统计工具，从而改善流行病学和健康相关的研究。