Multimodel Spaces for Robust Inference

用于稳健推理的多模型空间

• 批准号: 8738691
• 负责人: Steven S Henley
• 金额: $ 28.31万
• 依托单位: MARTINGALE RESEARCH CORPORATION
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-20 至 2016-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8738691
• 关键词: Accounting; Address; Algorithm Design; Algorithms; Applied Research; Attention deficit hyperactivity disorder; Bayesian Modeling; Classification; Clinical Sciences; Clinical Trials; Collection; Communities; Computer software; Confidence Intervals; Data; Data Analyses; Data Set; Development; Diagnosis; Discipline; Disease; Documentation; Economics; Effectiveness; Ensure; Epidemiology; Evaluation; Feasibility Studies; Foundations; Health; Health Policy; Health Services Research; Healthcare; Investigation; Literature; Medical; Mental Depression; Methodology; Methods; Modeling; National Institute of Drug Abuse; National Institute of Mental Health; Observational Study; Patients; Pattern; Performance; Phase; Plague; Process; Public Health; Research; Research Design; Research Personnel; Research Project Grants; Risk Factors; Safety; Sampling Errors; Science; Selection Bias; Series; Sociology; Software Design; Solutions; Source; Space Models; Specific qualifier value; Statistical Methods; Statistical Models; Structure; Symptoms; System; Technology; Testing; Translational Research; Uncertainty; United States National Institutes of Health; Validation; Weight; Work; base; commercialization; design; improved; innovation; neglect; new technology; novel; phase 1 study; prototype; public health relevance; screening; simulation; software development; statistics; technological innovation; theories; tool; treatment effect; user-friendly

DESCRIPTION (provided by applicant): Improving statistical methods to provide better inferences and new analytical capabilities for categorical regression models would be invaluable to the medical and health-related research communities. Presently, single regression models are used extensively to identify patterns of disease-related symptoms, screen for disorders, analyze the results of clinical trials, and for the assessment and justification of public health policies. However, while single model estimation and inference is widely used in health-related studies, such approaches neglect model uncertainty, thus abrogating the opportunity to: i) detect additional statistical regularities (e.g., treatment effects, risk factors), ii) improve the precision of statistical inferences for estimation and prediction/classification (e.g., patient screening, diagnosis), iii) control for overfitting (e.g., model selection bias), and iv) include different yet highly correlated risk factors. This Phase I study investigates the feasibility of combining robust estimators and specification analysis methods within a multimodel framework to create a robust multimodel estimation and inference technology that addresses the limitations of the single model approach. Robust multimodeling is a specific type of Frequentist Model Averaging (FMA) methodology. First, an important feature of this approach is that it provides robust confidence intervals on predictions and effect sizes averaged across multiple models, which simultaneously incorporate sources of uncertainty that arise from the presence of many different (yet equally appropriate) models of the same data generating process as well as sources of uncertainty resulting from sampling error. A second feature of our robust multimodeling approach is that it has a robust Bayesian Model Averaging (BMA) interpretation. Specifically, theoretical arguments establish that all inferences are robust with respect to the presence of model misspecification. Third, previous work in the BMA and FMA literature has tended to focus upon using the "most probable" models constrained within a model space by applying Occam's Window to identify a group of best models, rather than all possible models in computationally tractable model spaces. In this Phase I study, alternative strategies for multimodel estimation and inference involving large model spaces will be empirically studied with extensive simulations using realistic models on clinical trial datasets (NIDA-CTN, NIMH-STAR*D). Finally, Phase I feasibility results will provide the preliminary research and design for the Phase II prototype software and support technology dissemination through collaborative health-related research projects to establish the essential foundation for Phase III product commercialization.

描述（由申请人提供）：改进统计方法，为分类回归模型提供更好的推断和新的分析能力，这对医学和健康相关的研究界来说是非常宝贵的。目前，单回归模型被广泛用于识别疾病相关症状的模式，筛查疾病，分析临床试验结果，以及评估和论证公共卫生政策。然而，虽然单一模型估计和推理广泛用于健康相关研究，但此类方法忽视了模型不确定性，从而剥夺了以下机会：i）检测额外的统计规律（例如，治疗效果，风险因素），ii）改善 用于估计和预测/分类的统计推断的精度（例如，患者筛选、诊断），iii）过拟合控制（例如，模型选择偏差），以及iv）包括不同但高度相关的风险因素。这第一阶段研究的可行性相结合的稳健估计和规范分析方法在多模型框架内创建一个强大的多模型估计和推理技术，解决了单模型方法的局限性。鲁棒多模型是频率主义模型平均（FMA）方法的一种特定类型。首先，这种方法的一个重要特点是，它提供了强大的置信区间的预测和效果大小的平均在多个模型，其中同时纳入了不确定性的来源，所产生的许多不同的（但同样适当的）模型的存在相同的数据生成过程以及来源的不确定性所造成的抽样误差。我们强大的多模型方法的第二个特点是，它具有强大的贝叶斯模型平均（BMA）解释。具体来说，理论论证建立，所有的推论是强大的模型误指定的存在。第三，以前的工作在BMA和FMA文献往往集中在使用“最可能的”模型约束在一个模型空间内，通过应用奥卡姆窗口，以确定一组最佳模型，而不是所有可能的模型在计算上听话的模型空间。在这项I期研究中，将使用临床试验数据集（NIDA-CTN，NIMH-STAR*D）上的真实模型进行广泛模拟，对涉及大模型空间的多模型估计和推断的替代策略进行经验性研究。最后，第一阶段的可行性结果将为第二阶段的原型软件提供初步研究和设计，并通过与健康有关的合作研究项目支持技术传播，为第三阶段的产品商业化奠定必要的基础。