Robust Missing Data Methods for Categorical Regression

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，从分类回归模型中获得稳健统计推断的改进方法将是流行病学和医疗保健研究界的宝贵工具。目前流行病学模型通常被设计用于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。这些模型通常依赖于包含不完整数据的数据集。虽然商业上可获得的统计软件提供了一些自动缺失值程序（例如，数据填补，期望最大化），需要进一步的理论和实证研究，以开发更强大的统计方法。在其第一阶段可行性研究中，Martingale Research成功地开发了鲁棒的估计和推理算法，该算法联合收割机了随机估计，渐近统计和广义逻辑回归的最新进展，适用于存在缺失数据和模型错误指定的流行病学问题的分类回归建模。这些结果在模拟研究中得到了验证，并将该方法应用于酒精相关的研究问题。此外，新的理论研究，统一了缺失数据和模型误设定的发展，以支持新的强大的缺失数据推理统计的发展。 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的缺失数据方法，用于分类回归建模，包括：i）参数估计的假设检验，ii）标准误差估计，iii）模型选择标准，iv）规格检验。II期实验设计将利用Monte Carlo模拟自举方法，使用代表性酒精相关数据库评价缺失数据方法。具体而言，模拟研究将从经验上表征一致估计和统计推断的大样本假设的适当性。这些模拟研究方法与新的可靠的缺失数据方法将被整合到一个原型用户友好的独立软件包，以支持流行病学和健康相关的回归建模。总之，第二阶段研究将为第三阶段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，作为经济衰退建模的先进统计工具，改善流行病学和健康相关研究。

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.