Robust Classification Methods for Categorical Regression

描述（由申请人提供）：改进统计方法以提供更好的分类性能和新的分类回归分析能力，对医学和卫生保健研究界将是无价的。分类回归模型（二元逻辑、多项逻辑）被广泛用于识别酒精相关症状的模式，定义精神障碍的标准，并评估调节酒精的政策。然而，许多这样的模型是在自动化支持不足的情况下开发的，无法充分分析和利用其结果的内在概率性质。这是至关重要的，因为研究人员、临床医生和卫生保健管理人员经常面临使用分类回归模型进行分类决策，以i)识别高风险个人或群体，ii)进行临床评估，商业上可用的统计软件没有提供自动化的程序来系统地估计和测试分类回归背景下分类决策阈值的稳健性。此外，估计决策阈值上的稳健置信区间、比较竞争分类器或评估分类器错误规范存在的能力完全被忽略了。

DESCRIPTION (provided by applicant): Improving statistical methods to provide better classification performance and new analytical capabilities for categorical regression would be invaluable to the medical and health care research communities. Categorical regression models (binary logistic, multinomial logistic) 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 automated support to fully analyze and exploit the intrinsically probabilistic nature of their results. This is of critical importance as researchers, clinicians, and health-care administrators are many times faced with classification decisions using categorical regression models to i) identify high risk individuals or groups, ii) make clinical assessments, or iii) establish policy and treatment guidelines Commercially available statistical software provides no automated procedures to systematically estimate and test the robustness of decision threshold(s) for classification within the context of categorical regression Moreover, the capability to estimate robust confidence intervals on decision threshold(s), compare competing classifiers, or assess the presence of classifier misspecification is completely ignored. Martingale Research will develop statistical analysis tools to provide automated support that specifically addresses the classification aspects of categorical regression modeling. This Phase I study will demonstrate using datasets representative of NIAAA databases that the proposed statistical approach will 1) estimate classification decision threshold(s), 2) provide robust confidence intervals on decision threshold(s), and 3) apply an advanced model selection test for comparing competing classifiers and analyzing classifier quality. These results will demonstrate the essential technical feasibility required for further Phase II investigation and provide the foundation for developing commercially available software.