Robust Missing Data Methods for Categorical Regression

描述(由申请人提供)：在数据缺失和模型错误指定的情况下，从分类回归模型中获得稳健的统计推断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，流行病学模型通常用于确定酒精相关症状的模式，定义酒精使用障碍的标准，并评估管理酒精饮料使用和分销的政策。这类模型经常依赖于包含不完整数据的数据集。虽然商业上可用的统计软件提供了一些自动的缺失值程序(例如，数据补偿、期望最大化)，但还需要进一步的理论和实证研究，以开发更可靠的统计方法。在其第一阶段可行性研究中，Martingale Research成功地开发了稳健的估计和推断算法，这些算法结合了随机估计、渐近统计和广义Logistic回归的最新进展，适合于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一个与酒精相关的研究问题。此外，还开展了新的理论研究，将缺失数据和模型错误指定统一起来，以支持新的稳健缺失数据推断统计学的发展。 第二阶段的研究将扩展第一阶段的发现，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：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)规格检验。第二阶段的实验设计将利用蒙特卡罗模拟自举方法，以评估使用具有代表性的酒精相关数据库的缺失数据方法。具体地说，模拟研究将实证地表征大样本假设对于一致性估计和统计推断的适当性。这些模拟研究方法连同新的稳健的缺失数据方法将被整合到一个用户友好的原型独立软件包中，以支持流行病学和与健康有关的回归建模。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，长期目标是提供一套新的缺失数据处理方法，作为改进流行病学和健康相关研究的衰退建模的高级统计工具。

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.