EXPLOITING HIDDEN STRUCTURES IN EPIDEMIOLOGICAL DATA

提高分类回归模型性能的准确性和范围对酒精相关和卫生保健研究界将是非常宝贵的。提供更好的统计意义和预测性能的流行病学模型提高了研究人员识别酒精相关症状新模式的能力，并改进了对现有数据库中医疗或精神状况的分析。我们的第一阶段理论研究和实证结果表明，通过使用最大似然（ML）方法来改进用户选择的解释变量（连续的，名义的）的数据表示（重新编码）方案，可以实现分类回归模型的实质性性能改进。本文还开发并评估了一种新的统计检验方法，用于比较不同的编码策略和分类回归模型中标准误差估计的稳健方法。在第二阶段，Martingale Research将开发结合第一阶段发现的软件，并利用模式识别、经典统计和非线性优化技术，以原则性的方式利用解释变量和分类结果之间的结构关系。第二阶段的研究还将证明ML重新编码产生关于解释变量的新信息，并提高分类回归模型的可靠性和有效性。先进的统计软件包将交付给NIH/NIAAA用于流行病学和健康相关的研究。建议的商业应用：Martingale Research公司打算在商业软件包中实现用于分类回归分析的ML编码。该软件的开发是为了提高分类模型的整体性能，这些模型旨在解释医疗保健领域经常遇到的数据。这种方法也适用于其他利用分类建模进行财务预测、风险分析、信息解释和管理的行业。

Improving the accuracy and scope of categorical regression modeling performance would be invaluable to alcohol-related and the health care research communities. Epidemiological models that provide better statistical significance and predictive performance enhance the researcher's ability to identify new patterns of alcohol-related symptoms as well as improve analysis of medical or psychiatric conditions in existing databases. Our Phase I theoretical research and empirical results demonstrated that substantial performance improvements in categorical regression models could be achieved by using maximum likelihood (ML) methods to improve data representation (recoding) schemes for user selected explanatory variables (continuous, nominal). A new statistical test for comparing competing recoding strategies and robust methods for standard error estimation in categorical regression models were also developed and evaluated. During Phase II, Martingale Research will develop software that incorporates Phase I findings and utilizes techniques from pattern recognition, classical statistics, and nonlinear optimization to exploit structural relationships between explanatory variables and categorical outcomes in a principled manner. Phase II research will also demonstrate that ML recoding yields new information regarding explanatory variables and improves reliability and validity for categorical regression models. The advanced statistical software package will be delivered to NIH/NIAAA for use in epidemiological and health-related research. PROPOSED COMMERCIAL APPLICATION: Martingale Research corporation intends to implement ML recoding for categorical regression analysis into a commercial software package. The software is developed to improve the overall performance of categorical models designed to explain data frequently encountered in the health care field. This approach applies as well to other industries that utilize categorical modeling to do financial prediction, risk analysis, and information interpretation and management.