Multimodel Spaces for Robust Inference

用于稳健推理的多模型空间

• 批准号: 8592200
• 负责人: Steven S Henley
• 金额: $ 28.95万
• 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 2015-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8592200
• 关键词: Accounting; Address; Algorithms; Applied Research; Attention deficit hyperactivity disorder; 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; Probability; 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; 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论和新的分析能力，将对医学和健康相关的研究界来说是非常宝贵的。目前，单一回归模型被广泛用于确定疾病相关症状的模式、筛查疾病、分析临床试验结果，以及评估和证明公共卫生政策的合理性。然而，虽然单一模型估计和推断在与健康有关的研究中被广泛使用，但这种方法忽略了模型的不确定性，从而失去了：i)检测额外的统计规律(例如，治疗效果、风险因素)的机会；ii)改善 用于估计和预测/分类(例如，患者筛查、诊断)的统计推断的精确度，iii)对过度拟合的控制(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。这项第一阶段研究探讨了在多模型框架内结合稳健估计器和规格分析方法的可行性，以创建稳健的多模型估计和推理技术，以解决单一模型方法的局限性。稳健多重建模是一种特殊类型的频域模型平均(FMA)方法。首先，这种方法的一个重要特点是，它为多个模型平均的预测和效果大小提供了稳健的可信区间，这些模型同时包含了由于同一数据生成过程的许多不同(但同样适当的)模型的存在而产生的不确定源，以及由抽样误差造成的不确定源。我们的稳健多重建模方法的第二个特征是它具有稳健的贝叶斯模型平均(BMA)解释。具体地说，理论论证确立了所有推论对于模型错误说明的存在是稳健的。第三，BMA和FMA文献中以前的工作倾向于通过应用Occam窗口来识别一组最佳模型，而不是在计算上容易处理的模型空间中识别所有可能的模型，从而集中使用模型空间中约束的“最可能的”模型。在这项第一阶段的研究中，涉及大型模型空间的多模型估计和推断的替代策略将通过在临床试验数据集(NIDA-CTN，NIMH-STAR*D)上使用现实模型进行广泛的模拟来进行经验性研究。最后，第一阶段可行性结果将为第二阶段原型软件提供初步研究和设计，并通过与健康相关的合作研究项目支持技术传播，为第三阶段产品商业化奠定必要的基础。