用相对统计和近似疾病流行率比较筛查试验

IF 1.2 4区 数学
Samuel Frank, Abigail Craig
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引用次数: 2

摘要

Schatzkin等人证明了一些条件统计(如真阳性比例)的比率等于无条件统计(如疾病检出率)的比率,因此我们可以计算出同一人群中两次筛查试验之间的比率,即使阴性检测患者没有参考程序,并且真阴性率和假阴性率未知。我们将演示相同的属性适用于预期的效用度量。我们还证明,虽然相对特异性和ROC曲线(AUC)下的相对面积的简单估计确实依赖于未知的负率,但我们可以根据疾病患病率来编写这些比率,并且这些比率对假定患病率的依赖性通常很弱,特别是如果患病率很小或两个筛选测试的表现相似。因此,如果我们使用疾病患病率的近似值,我们可以估计相对特异性或AUC,而准确度几乎没有损失。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Using Relative Statistics and Approximate Disease Prevalence to Compare Screening Tests
Schatzkin et al. and other authors demonstrated that the ratios of some conditional statistics such as the true positive fraction are equal to the ratios of unconditional statistics, such as disease detection rates, and therefore we can calculate these ratios between two screening tests on the same population even if negative test patients are not followed with a reference procedure and the true and false negative rates are unknown. We demonstrate that this same property applies to an expected utility metric. We also demonstrate that while simple estimates of relative specificities and relative areas under ROC curves (AUC) do depend on the unknown negative rates, we can write these ratios in terms of disease prevalence, and the dependence of these ratios on a posited prevalence is often weak particularly if that prevalence is small or the performance of the two screening tests is similar. Therefore we can estimate relative specificity or AUC with little loss of accuracy, if we use an approximate value of disease prevalence.
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来源期刊
International Journal of Biostatistics
International Journal of Biostatistics Mathematics-Statistics and Probability
CiteScore
2.30
自引率
8.30%
发文量
28
期刊介绍: The International Journal of Biostatistics (IJB) seeks to publish new biostatistical models and methods, new statistical theory, as well as original applications of statistical methods, for important practical problems arising from the biological, medical, public health, and agricultural sciences with an emphasis on semiparametric methods. Given many alternatives to publish exist within biostatistics, IJB offers a place to publish for research in biostatistics focusing on modern methods, often based on machine-learning and other data-adaptive methodologies, as well as providing a unique reading experience that compels the author to be explicit about the statistical inference problem addressed by the paper. IJB is intended that the journal cover the entire range of biostatistics, from theoretical advances to relevant and sensible translations of a practical problem into a statistical framework. Electronic publication also allows for data and software code to be appended, and opens the door for reproducible research allowing readers to easily replicate analyses described in a paper. Both original research and review articles will be warmly received, as will articles applying sound statistical methods to practical problems.
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