An Exact Test for Overdispersion in Screening Mammography Assessments

描述(申请人提供)：乳腺癌的早期检测每年挽救成千上万的生命，如果更多的妇女和她们的医疗保健提供者利用这些测试，可能会拯救更多的生命。乳腺癌的早期发现在一定程度上取决于筛查乳房X光检查的准确性。人类翻译人员在检测乳腺癌存在或不存在方面的准确性可以通过评估敏感性和特异性等指标来评估。对这两个精确度参数的有效和准确估计对公共卫生规划和患者具有重要影响。当使用多个观测值和统计建模方法来评估精度指标时，需要确认数据的二项分布的标准假设。基于大样本理论的几个测试被广泛使用。然而，生物医学研究中产生的数据经常涉及小样本，因此分析此类数据的渐近方法的准确性受到质疑。这项研究计划的目标是开发一种在小样本中准确测试过度分散的方法。该项目将包括理论和实证工作，利用现有的数据源和通过H.Lee Moffitt癌症中心和研究所的研究人员合作提供的合作机会。这项提议的具体目标描述如下：具体目标1：开发对过度分散的准确测试；具体目标2：开发计算算法并发布公开可用的实施；具体目标3：使用真实生活数据集应用和评估所提议的方法。

DESCRIPTION (provided by applicant): Early detection tests for breast cancer save many thousands of lives each year, and many more lives could be saved if even more women and their health care providers took advantage of these tests. Early detection of breast cancer depends in part on the accuracy of screening mammogram interpretation. The accuracy of human interpreters in detecting the presence or absence of breast cancer can be evaluated by estimating indices like sensitivity and specificity. Efficient and precise estimation of both of these accuracy parameters have important consequences for public health planning and for patients. When multiple observations and statistical modeling approaches are employed for assessing the accuracy indices, the standard assumption of the binomial distribution for the data needs confirmation. Several tests based on large sample theory are widely available. However, the data generated in biomedical studies frequently involve small samples, and thus the accuracy of the asymptotic approach for analyzing such data is in question. The goal of this research proposal is to develop a method of exact testing for overdispersion in small samples. The project will involve both theoretical and empirical work, drawing on existing data sources and collaborative opportunities provided through the investigator's collaboration at H. Lee Moffitt Cancer Center & Research Institute. The specific aims for this proposal are described as: Specific Aim 1: Develop an exact test for overdispersion; Specific Aim 2: Develop a computational algorithm and release a publicly available implementation; Specific Aim 3: Apply and evaluate the proposed method using a real life dataset.