Model Selection Methods for Categorical Regression

描述（由申请人提供）：改进使用分类回归分析流行病学和临床数据集的模型选择方法对医学和卫生保健研究界来说是非常宝贵的。分类回归（logistic，多项logistic）模型被广泛用于识别酒精相关症状的模式，定义精神疾病的标准，并评估酒精管制政策。但不少 这些模型的开发缺乏充分的支持，无法充分探索预测变量和结果变量之间的复杂关系。商业上可用的统计软件提供了自动化程序（例如逐步的，最佳子集回归），在很大程度上忽略了模型的错误设定，实验误差率和多重共线性。此外，这些程序受到可以考虑的模型数量的严重限制，因此没有全局收敛的理论基础。 Martingale Research将开发一种新的模型选择方法，支持使用分类回归自动构建模型。本阶段I研究将使用NIAAA数据库的代表数据集证明，所提出的统计方法将：1）在存在模型错误指定的情况下支持正确的模型选择决策，2）在相当一般的条件下收敛到最佳模型，3）降低实验误差率。这些结果将证明进一步的第二阶段调查所需的基本技术可行性，并为开发商业软件提供基础。 拟定商业应用：Martingale Research Corporation打算将所提出的模型选择方法纳入高级分类回归软件包，该软件包解决模型错误指定，实验误差和全局收敛问题。 这将提供第三阶段的商业软件，支持改进解决与酒精有关的流行病学研究中遇到的各种问题以及数据库解释和管理的其他方面。

DESCRIPTION (provided by applicant): Improving model selection methods for analyzing epidemiological and clinical datasets using categorical regression would be invaluable to the medical and health care research communities. Categorical regression (logistic, multinomial logistic) models are used extensively to identify patterns of alcohol-related symptoms, define criteria of psychiatric disorders, and assess policies regulating alcohol. However, many such models are developed with inadequate support to fully explore the complex relationships between predictor and outcome variables. Commercially available statistical software provides automated procedures (e.g. stepwise, best subsets regression) that largely ignore model misspecification, experimentwise error rate, and multicollinearity. Furthermore, these procedures are severely limited by the number of models that can be considered and thus have no theoretical basis for global convergence. Martingale Research will develop a new model selection method that supports automated model-building using categorical regression. This Phase I study will demonstrate, using datasets representative of NIAAA databases, that the proposed statistical approach will: 1) support correct model selection decisions in the presence of model misspecification, 2) converge to an optimal model under fairly general conditions, and 3) mitigate experimentwise error rate. These results will demonstrate the essential technical feasibility required for further Phase II investigations and provide the foundation for developing commercially available software. PROPOSED COMMERCIAL APPLICATION: Martingale Research Corporation intends to incorporate the proposed model selection method into an advanced categorical regression software package that addresses model misspecification, experimental-wise error, and global convergence. This will provide Phase III commercial software that supports improved solutions to a wide range of problems encountered in alcohol-related epidemiological research, as well as other aspects of database interpretation and management.