EXPLOITING HIDDEN STRUCTURES IN EPIDEMIOLOGICAL DATA

提高分类回归建模性能的准确性和范围将对酒精相关和医疗保健研究社区具有无可估量的价值。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与酒精有关的症状的新模式的能力，并改进了对现有数据库中的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)数据表示(重新编码)方案，分类回归模型的性能可以得到显著的改善。此外，还开发并评价了一种新的统计检验方法，用于比较分类回归模型中相互竞争的重新编码策略和标准误差估计的稳健方法。在第二阶段，Martingale Research将开发包含第一阶段发现的软件，并利用模式识别、经典统计学和非线性优化的技术，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重新编码产生了关于解释变量的新信息，并提高了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，用于流行病学和与卫生有关的研究。建议的商业应用：Martingale Research Corporation打算将分类回归分析的ML重新编码应用到一个商业软件包中。开发该软件是为了提高分类模型的整体性能，该模型旨在解释医疗保健领域经常遇到的数据。这种方法也适用于利用分类建模进行财务预测、风险分析以及信息解释和管理的其他行业。

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.