Improving Validity Measures for Alcohol-Related Models

描述（由申请人提供）： 改进的拟合优度（GOF）的措施，支持规格检验的二进制逻辑，多项逻辑和线性回归模型将是非常宝贵的研究人员在酒精相关的流行病学研究和更广泛的临床试验和卫生服务研究社区。这些模型被广泛用于识别酒精相关症状的模式，定义酒精使用障碍的标准，并评估规范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）。这可能会导致不正确的推理时，应用标准的推理技术。在实际实践中，目前可用的统计软件使用GOF汇总测量和统计诊断，这些测量和统计诊断不是专门设计用于检测模型错误指定，并且可以在存在模型错误指定的共同来源的情况下显示出做出不正确的统计推断。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的拟合模型是否是理论上正确的模型。信息矩阵（IM）检验是一种模型误设检验（白色，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统计测试：1）多项逻辑回归，2）独立同分布以及局部相关观察的线性回归。第II阶段实验设计将利用Monte Carlo模拟自举方法，使用代表性NIAAA数据库评价新的IM试验。具体而言，模拟研究将根据经验表征大样本假设的适当性以及特异性和灵敏度（即，统计功效）的新IM测试。这些模拟研究方法与新的IM测试将被整合到一个原型用户友好的独立软件包，以支持流行病学和健康相关的回归建模。总之，第二阶段研究将为第三阶段商业化奠定必要的技术基础，其长期目标是提供一套模型规范测试，作为回归建模的高级统计工具，以改善流行病学和健康相关研究。

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.