Robust Classification Methods for Categorical Regression

分类回归的稳健分类方法

基本信息

批准号：
7395177
负责人：
Steven S Henley
金额：
$ 85.72万
依托单位：
MARTINGALE RESEARCH CORPORATION
依托单位国家：
美国
项目类别：
财政年份：
2003
资助国家：
美国
起止时间：
2003-06-04 至 2011-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7395177
关键词：
Accident and Emergency department Achievement Address Administrator Agreement Alcohols Algorithms Area Bayesian Method Behavior Bipolar Depression Class Classification Clinical Clinical Investigator Collaborations Communicable Diseases Communities Complex Computer software Confidence Intervals Data Data Analyses Data Set Decision Analysis Decision Making Development Diagnosis Disease Disease regression Empirical Research Engineering Epidemiologic Studies Epidemiologist Evaluation Event Foundations Goals Guidelines Health Health Services Research Healthcare Industry Information Resources Management Injury Jordan Journals Knowledge Literature Logistic Regressions Logistics Malignant Neoplasms Malignant neoplasm of prostate Medical Mental Health Mental disorders Methodology Methods Modeling Nature Outcome Output Pathology Pattern Peer Review Performance Phase Phase I Clinical Trials Phase II Clinical Trials Policies Preparation Probability Process Public Health Publishing Quality of Care Relative (related person)Research Research Personnel Risk Robin bird Sampling Schizophrenia Screening procedure Sensitivity and Specificity Simulate Software Tools Specific qualifier value Specificity Standards of Weights and Measures Statistical Methods Surveys Symptoms Technology Testing Trauma anticancer research base commercialization computerized data processing density design graphical user interface improved innovation prototype simulation software development theories tool user friendly software

项目摘要

DESCRIPTION (provided by applicant): Improving statistical methods to provide better classification performance and new analytical capabilities for categorical regression would be invaluable to the medical and health care research communities. Categorical regression models (e.g., binary logistic, multinomial logistic) are used extensively to identify patterns of alcohol-related symptoms, screen for disorders, and assess policies. In addition, such models are used extensively in other areas of research such as mental illness, cancer, traumatic injuries, and AIDS-related pathologies. However, many such models are developed with inadequate support to fully analyze and exploit the intrinsically probabilistic nature of their results. This is of critical importance as health researchers, clinicians, and administrators are often faced with classification decisions using categorical regression models to identify unacceptable risks, adequate outcomes, and acceptable guidelines for screening, diagnoses, treatment, and quality of care. Commercially available statistical software does not offer sophisticated methods for robust estimation of posterior probabilities in the presence of model misspecification, missing covariates, and nonignorable missing data generating processes. Such robust missing data handling methods provide natural mechanisms for dealing with verification bias and modeling correlated, longitudinal, or survey data with complex sampling designs. Moreover, commercially available statistical software does not provide automated methods for using estimated posterior probabilities to make optimal classification decisions with respect to different optimality criteria. In particular, automated features such as optimizing multiple decision criteria (allocation rules) that trade off specificity against sensitivity, decision threshold confidence intervals, statistical tests for evaluating correct specification of posterior probabilities, statistical tests for comparing competing classifier thresholds, and methods for multi-outcome classification and inference are not readily available. Phase II research will extend Phase I findings for binary logistic regression to develop and implement automated robust classification methods for multinomial logistic regression modeling, which also applies to the larger class of nonlinear categorical regression models that output posterior probabilities. The Phase II software prototype will provide: 1) new user-selectable robust decision threshold estimators, 2) robust confidence intervals on decision threshold estimators, 3) new classifier threshold comparison tests, 4) new outcome probability specification tests, 5) efficient missing data handling methods in the presence of nonignorable nonresponse data, and 6) second-order analytic and simulation-based Bayesian methods for improved small sample and rare event outcome probability estimation. These new methodologies will be integrated into a prototype user-friendly software package, evaluated with extensive simulation studies, and then applied to real world classification problems encountered in: alcohol, mental illness (depression, bipolar, schizophrenia), cancer (prostate), trauma (emergency room), and infectious disease (AIDS) through collaborations with domain experts in those respective fields. In summary, Phase II research will establish the essential technical foundation for Phase III commercialization with the objective of providing a suite of new classification analysis methods as an advanced statistical tool that improves epidemiologic, clinical, and public health research.

描述（由申请人提供）：改进统计方法，以提供更好的分类性能和新的分类回归分析能力，这对医学和医疗保健研究社区将是无价的。广泛使用分类回归模型（例如，二进制逻辑，多项式逻辑逻辑）来识别与酒精相关症状的模式，疾病筛查和评估政策。此外，此类模型在其他研究领域进行了广泛使用，例如精神疾病，癌症，创伤性损伤和与艾滋病相关的病理。但是，许多这样的模型都是在支持不足以完全分析和利用其结果本质上的概率性质的情况下开发的。这至关重要，因为使用分类回归模型通常面临健康研究人员，临床医生和管理人员的分类决定，以确定不可接受的风险，适当的结果以及可接受的筛查，诊断，治疗，治疗和护理质量的指南。市售的统计软件不提供复杂的方法，用于在存在模型错误指定，丢失的协变量和不可签名的数据生成过程的情况下对后验概率的稳健估计。这种强大的缺少数据处理方法提供了与复杂采样设计相关，纵向或调查数据相关，纵向或调查数据的自然机制。此外，市售的统计软件不提供自动化方法，用于使用估计的后验概率来就不同的最优标准做出最佳分类决策。特别是，自动化功能，例如优化多个决策标准（分配规则），这些功能可以与敏感性，决策阈值置信区间进行权衡，以评估正确规范后验概率的统计测试，以比较竞争分类器阈值的统计测试结果分类和推理不容易获得。第二阶段的研究将扩展二进制逻辑回归的I阶段发现，以开发和实施多项式逻辑回归模型的自动鲁棒分类方法，这也适用于输出后验概率的较大类的非线性分类回归模型。 II期软件原型将提供：1）新的可选择的可稳健决策阈值估算器，2）稳健的置信区间在决策阈值估算器上，3）新的分类器阈值比较测试，4）新结果规范测试，5）5）有效丢失数据在存在不可降低反应数据的情况下处理方法，以及6）基于二阶分析和基于仿真的贝叶斯方法，用于改善小样本和罕见的事件结果概率估计。这些新方法将集成到原型用户友好的软件包中，并通过广泛的模拟研究进行评估，然后应用于：酒精，精神疾病（抑郁症，双极性，精神分裂症），癌症（前列腺），创伤中遇到的现实世界分类问题。（急诊室）和传染病（AIDS）通过与这些领域的领域专家合作。总而言之，第二阶段研究将建立III期商业化的基本技术基础，目的是提供一套新的分类分析方法作为改善流行病学，临床和公共卫生研究的高级统计工具。