Improving Validity Measures for Alcohol-Related Models

描述(由申请人提供)： 改进的拟合度(GOF)测量支持二元逻辑、多项逻辑和线性回归模型的规范测试，对于酒精相关流行病学研究以及更广泛的临床试验和卫生服务研究社区的研究人员来说，将是非常宝贵的。这类模型被广泛用于确定与酒精有关的症状模式，确定酒精使用障碍的标准，并评估管理酒精饮料使用和分销的政策。然而，许多回归模型不可避免地被错误指定(即，不包含生成观测数据的真实分布)。在应用标准推理技术时，这可能会导致不正确的推断。在实际操作中，目前可用的统计软件使用GOF汇总措施和统计诊断，这些措施和统计诊断并不是专门为检测模型错误说明而设计的，在存在常见的模型错误说明来源的情况下，可以证明它们作出了不正确的统计推断。因此，研究人员只有最少的统计工具来做出可靠的推断，以便容易地评估他们拟合的模型是否是理论上正确的模型。信息矩阵(IM)测试是一种模型误指定测试(White，1982,1994,1998)，专门用于解决前面提到的问题。 第二阶段研究将扩展第一阶段的研究结果，以开发和实施新的IM统计检验：1)多项Logistic回归，2)独立同分布以及局部相关观测的线性回归。第二阶段的实验设计将利用蒙特卡洛模拟自举方法，目的是使用具有代表性的NIAAA数据库来评估新的IM测试。具体地说，模拟研究将对大样本假设的适当性以及新IM测试的特异性和敏感性(即统计能力)进行实证表征。这些模拟研究方法连同新的IM测试将被整合到一个用户友好的原型独立软件包中，以支持流行病学和与健康有关的回归建模。总而言之，第二阶段的研究将为第三阶段的商业化奠定必要的技术基础，长期目标是提供一套模型规格测试，作为回归建模的高级统计工具，以改善流行病学和与健康有关的研究。

DESCRIPTION (provided by applicant): Improved goodness-of-fit (GOF) measures that support specification testing for binary logistic, multinomial logistic, and linear regression models would be invaluable to investigators in alcohol-related epidemiological research and the broader clinical trials and health services research communities. Such models are used extensively to identify patterns of alcohol-related symptoms, define criteria of alcohol use disorders, and evaluate policies regulating use and distribution of alcoholic beverages. However, many regression models are inevitably misspecified (i.e., do not contain the true distribution that generated the observed data). This may lead to incorrect inferences when applying standard inferential techniques. In actual practice currently available statistical software uses GOF summary measures and statistical diagnostics that are not specifically designed to detect model misspecification and can be shown to make incorrect statistical inferences in the presence of common sources of model misspecification. Accordingly, researchers have minimal statistical tools to make reliable inferences for readily evaluating if their fitted model is the theoretically correct one. Information matrix (IM) tests are a type of model misspecification test (White, 1982, 1994, 1998) that are specifically intended to solve problems such as those previously mentioned. Phase II research will extend Phase I findings to develop and implement new IM statistical tests for: 1) multinomial logistic regression, and 2) linear regression on independent identically distributed as well as locally correlated observations. The Phase II experimental design will utilize Monte Carlo simulation bootstrapping methods for the purposes of evaluating the new IM tests using representative NIAAA databases. Specifically, the simulation studies will empirically characterize both the appropriateness of the large sample assumptions as well as the specificity and sensitivity (i.e., statistical power) of the new IM tests. These simulation study methodologies in conjunction with the new IM tests will be integrated into a prototype user-friendly standalone software package for the purposes of supporting epidemiological and health related regression modeling. In summary, Phase II research will establish the essential technical foundation for Phase III commercialization with the long-term objective of providing a suite of model specification tests as an advanced statistical tool for regression modeling in order to improve epidemiological and health-related research.