Statistical methods for utility of multiple tests

多项测试效用的统计方法

• 批准号: 6323904
• 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/6323904
• 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.

骨质疏松症是我们老龄化社会的一个主要健康问题。作为一种沉默的疾病，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，已经开发了大量的诊断技术，并用于评估骨密度和质量。这些测试的准确性和成本差异很大。有了这样各种各样的诊断测试，临床医生必须为特定的患者群体确定最佳的诊断策略，包括筛查和选择和监测治疗。由于缺乏适当的统计工具来评估多项测试组合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，因此很难确定测试的最佳组合。本研究的具体目的如下：1.发展平行测验的受试者工作特性(ROC)区域分析。2.使用成本标准为测试系列开发递归划分树。3.建立了序贯试验最优决策阈值的成本-效果模型。我们将根据骨质疏松性骨折(SOF)研究的数据和使用成本-效果和人口分布参数的模拟模型，研究上述统计工具的理论属性，开发计算机软件，并应用它们来检验DXA、SXA、QCT、QUS、生物标记物和流行病学危险因素的组合，如年龄、体重、跌倒比率、家族史和骨折史等。