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Multimodel Spaces for Robust Inference

用于稳健推理的多模型空间

基本信息

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
项目类别：
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

项目摘要

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，对医学和健康相关的研究界来说是无价的。目前，单一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，以及评估和证明公共卫生政策的合理性。然而，尽管单一模型估计和推断被广泛用于与健康有关的研究，但这种方法忽视了模型的不确定性，从而丧失了以下机会：1)发现额外的统计规律（例如，治疗效果、风险因素）