Robust Classification Methods for Categorical Regression

描述(由申请人提供)：改进统计方法，为分类回归提供更好的分类性能和新的分析能力，对医学和卫生保健研究界将是无价的。分类回归模型(二元Logistic模型、多项Logistic模型)被广泛用于识别酒精相关症状的模式、定义精神障碍的标准和评估管理酒精的政策。然而，许多这样的模型在开发时没有足够的自动化支持来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多次面临使用分类回归模型的分类决策，以i)识别高危个人或群体，ii)进行临床评估，或iii)建立政策和治疗指南，商业上可获得的统计软件没有提供自动程序来系统地估计和测试分类的决策阈值(S)的稳健性。此外，完全忽略了在决策阈值(S)上估计稳健的可信区间、比较竞争分类器或评估分类器误指定的存在的能力。 Martingale Research将开发统计分析工具，以提供自动支持，专门处理分类回归建模的分类方面。这项第一阶段的研究将使用具有代表性的NIAAA数据库的数据集来演示所提出的统计方法将1)估计分类决策阈值(S)，2)在决策阈值上提供稳健的可信区间(S)，以及3)应用高级模型选择测试来比较竞争分类器和分析分类器质量。这些结果将证明进一步进行第二阶段调查所需的基本技术可行性，并为开发商业软件奠定基础。

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.