Statistical methods for utility of multiple tests

多项测试效用的统计方法

• 批准号: 6497424
• 负责人: YING LU
• 金额: $ 7.38万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-04-01 至 2004-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6497424
• 关键词: bone fracture; clinical research; computer program /software; computer system design /evaluation; cost effectiveness; data collection methodology /evaluation; diagnosis quality /standard; diagnostic tests; disease /disorder proneness /risk; human data; musculoskeletal disorder diagnosis; osteoporosis; statistics /biometry

Osteoporosis is a major health problem in our aging society. As a silent disease, diagnosis and risk assessment of osteoporosis rely on diagnostic tests. In the past decade, a great number of diagnostic techniques have been developed and used to assess bone density and quality. The accuracy and cost of these tests vary greatly. With such a variety of diagnostic tests, the clinicians must determine the best diagnostic strategy for specific patient populations, both for screening and for selecting and monitoring treatment. Because of the lack of appropriate statistical tools to assess diagnostic utility of combining multiple tests in their accuracy in predicting osteoporotic fractures and cost-effectiveness, it is difficult to identify the optimum combination of tests. The specific aims of our study are the following: 1. Development of receiver operating characteristics (ROC) region analysis for parallel tests. 2. Development of recursive partitioning trees for test series using cost criteria. 3. Development of cost-effectiveness models of optimal decision thresholds for sequential tests. We will study theoretical properties, develop computer software for above statistical tools and apply them to examine the combinations of DXA, SXA, QCT, QUS, biomarkers, and epidemiological risk factors such as age, weight, fall rate, family and fracture history, etc., based on data from the Study of Osteoporotic Fractures (SOF) and simulation models using cost-effectiveness and population distribution parameters published in literature.

骨质疏松症是老龄化社会的一个主要健康问题。作为一种隐性疾病，骨质疏松症的诊断和风险评估依赖于诊断试验。在过去的十年里，大量的诊断技术被开发出来并用于评估骨密度和骨质量。这些测试的准确性和成本差别很大。有了这么多的诊断测试，临床医生必须为特定的患者群体确定最佳的诊断策略，既用于筛查，也用于选择和监测治疗。由于缺乏适当的统计工具来评估组合多种测试在预测骨质疏松性骨折的准确性和成本效益方面的诊断效用，因此很难确定最佳的测试组合。我们研究的具体目的如下：平行试验受试者工作特征（ROC）区域分析的发展。2. 使用代价准则的测试序列递归划分树的开发。3. 序列测试最优决策阈值的成本效益模型的开发。我们将研究上述统计工具的理论性质，开发计算机软件，并将其应用于研究DXA、SXA、QCT、QUS、生物标志物以及年龄、体重、跌倒率、家庭和骨折史等流行病学危险因素的组合，基于研究骨质疏松性骨折（SOF）的数据和模拟模型，使用文献中发表的成本效益和人群分布参数。