Conditional Independence based Model Diagnostic Methods

基于条件独立的模型诊断方法

• 批准号: 8182129
• 负责人: DAVID B. NELSON
• 金额: --
• 依托单位: MINNEAPOLIS VA MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2012-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8182129
• 关键词: Address; Applications Grants; Area; Case Mixes; Cataloging; Catalogs; Clinical; Complex; Diagnosis; Diagnostic; Diagnostic Procedure; Epidemiology; Exhibits; Health; Health Services Research; Healthcare; Intervention; Lead; Linear Models; Linear Regressions; Link; Literature; Logistic Regressions; Medical; Medical Research; Methodology; Methods; Modeling; Modification; Observational Study; Outcome; Patient Care; Pattern; Policies; Procedures; Process; Regression Analysis; Research; Research Personnel; Residual state; Risk Adjustment; Specific qualifier value; Statistical Models; Testing; Translating; Translations; Veterans; Work; abstracting; base; improved; programs; research study; stem; theories; tool

Abstract Project Background: Statistical inference in the medical research used to inform VA policy deci- sions, healthcare initiatives, and patient care frequently requires use of complex regression mod- els. Regression model diagnostics therefore are critically important for the analysis of these re- search studies to establish the appropriateness of the models from which inference is based and to protect the validity and trustworthiness of inferences drawn from these statistical models. There exist sizable statistical theory and methodologies for linear regression model diagnostics that functions well. Much of the diagnostic theory and methods for generalized linear models, such as logistic regression and Poisson regression, are direct translations and modifications of the residual based diagnostic theory for linear models. However, several of the residual based diagnostic methods may not perform as well for generalized linear models as for linear models. Project Objectives: The proposed research will develop a non-residual based statistical theory for generalized linear regression model diagnostics that will address many of the shortcomings of current residual based methods. Project Methods: For a generalized linear regression, the proposed research will demonstrate the predictors and the outcome are independent given the correct regression function. Hence, if the regression function is well specified then the outcome and the predictors will appear independent given the value of the estimated regression function. This simple result then opens numerous possibilities for diagnostic techniques. For example, with an estimated regression function close to the true regression function, simple scatterplots of the outcome against the predictors conditional on estimated regression function should exhibit independence. We will use asymptotic theory for likelihood estimates under mis- specified models and other areas of statistical theory to develop simple graphical methods for assessing the fit of generalized linear models. Preliminary mathematical results indicate that these simple scatterplots together with smoothing and aggregation of these plots can diagnosis omission of interactions and transformations of the predictors from the regression function. In ad- dition, the proposed research will investigate use of moment generating functions, aggregation of p-values for within strata tests, and stratified nonparametric tests to develop formal tests for lack of fit for generalized linear regression models. Importance to VA: In developing methods that lead to improved, more reliable inference in epi- demiological, clinical, and health services research, the proposed study will lead to more soundly established medical interventions and health programs that will directly impact veteran's health. Over the course of numerous such research studies the cumulative indirect impact of this research could be substantial.

摘要 项目背景：医学研究中的统计推断用于为退伍军人管理局政策决策提供信息 医疗保健计划和患者护理经常需要使用复杂的回归模型， 埃尔。因此，回归模型诊断对于分析这些回归模型至关重要。 搜索研究，以确定推断所依据的模型的适当性， 保护从这些统计模型得出的推论的有效性和可信度。 线性回归模型诊断有相当多的统计理论和方法 功能良好。广义线性模型的许多诊断理论和方法， 例如逻辑回归和泊松回归，是对 基于残差的线性模型诊断理论。然而，一些基于 诊断方法对于广义线性模型可能不如对于线性模型执行得好。 项目目标：本研究将建立一个基于非残差的统计理论 广义线性回归模型诊断，将解决许多缺点， 当前基于残差的方法。 项目方法：对于广义线性回归，拟议的研究将证明 在给定正确的回归函数的情况下，预测因子和结果是独立的。因此，如果 回归函数被很好地指定，则结果和预测因子将显示为独立的 给定估计的回归函数的值。这个简单的结果然后打开许多 诊断技术的可能性。 例如，对于接近真实回归函数的估计回归函数，简单 结果与预测因子的散点图，以估计的回归函数为条件 应该表现出独立性。我们将使用渐近理论的似然估计下的错误- 指定的模型和其他领域的统计理论，以开发简单的图形方法， 评估广义线性模型的拟合度。初步的数学结果表明， 这些简单的散点图加上这些图的平滑和聚合可以诊断 从回归函数中省略预测因子的相互作用和转换。在公元- 此外，拟议的研究将调查使用矩生成函数，聚合 层内检验的p值和分层非参数检验，以开发缺乏的正式检验 广义线性回归模型的拟合。 对VA的重要性：在开发方法，导致改进，更可靠的推断，在epi- 神学、临床和健康服务研究，拟议的研究将带来更健全的结果 建立医疗干预和健康计划，将直接影响退伍军人的健康。 在众多此类研究的过程中， 可能会很重要