扩展 DeLong 算法，比较缺失数据下相关接收者操作特征曲线的面积。

IF 1.8 4区医学 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY

Statistics in Medicine Pub Date : 2024-09-20 Epub Date: 2024-07-16 DOI:10.1002/sim.10172

Lily Zou, Yun-Hee Choi, Leonardo Guizzetti, Di Shu, Joshua Zou, Guangyong Zou

{"title":"扩展 DeLong 算法，比较缺失数据下相关接收者操作特征曲线的面积。","authors":"Lily Zou, Yun-Hee Choi, Leonardo Guizzetti, Di Shu, Joshua Zou, Guangyong Zou","doi":"10.1002/sim.10172","DOIUrl":null,"url":null,"abstract":"A nonparametric method proposed by DeLong et al in 1988 for comparing areas under correlated receiver operating characteristic curves is used widely in practice. However, the DeLong method as implemented in popular software quietly deletes individuals with any missing values, yielding potentially invalid and/or inefficient results. We simplify the DeLong algorithm using ranks and extend it to accommodate missing data by using a mixed model approach for multivariate data. Simulation results demonstrate the validity and efficiency of our procedure for data missing at random. We illustrate our proposed procedure in SAS, Stata, and R using the original DeLong data.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":null,"pages":null},"PeriodicalIF":1.8000,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Extending the DeLong algorithm for comparing areas under correlated receiver operating characteristic curves with missing data.\",\"authors\":\"Lily Zou, Yun-Hee Choi, Leonardo Guizzetti, Di Shu, Joshua Zou, Guangyong Zou\",\"doi\":\"10.1002/sim.10172\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A nonparametric method proposed by DeLong et al in 1988 for comparing areas under correlated receiver operating characteristic curves is used widely in practice. However, the DeLong method as implemented in popular software quietly deletes individuals with any missing values, yielding potentially invalid and/or inefficient results. We simplify the DeLong algorithm using ranks and extend it to accommodate missing data by using a mixed model approach for multivariate data. Simulation results demonstrate the validity and efficiency of our procedure for data missing at random. We illustrate our proposed procedure in SAS, Stata, and R using the original DeLong data.\",\"PeriodicalId\":21879,\"journal\":{\"name\":\"Statistics in Medicine\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.8000,\"publicationDate\":\"2024-09-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Statistics in Medicine\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1002/sim.10172\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/7/16 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q3\",\"JCRName\":\"MATHEMATICAL & COMPUTATIONAL BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Statistics in Medicine","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1002/sim.10172","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/7/16 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"MATHEMATICAL & COMPUTATIONAL BIOLOGY","Score":null,"Total":0}

引用次数: 0

摘要

德隆等人于 1988 年提出了一种非参数方法，用于比较相关接收者操作特征曲线下的面积，这种方法在实践中被广泛使用。然而，在流行软件中实施的 DeLong 方法会悄悄删除任何缺失值的个体，从而产生可能无效和/或低效的结果。我们使用等级简化了 DeLong 算法，并通过使用多元数据混合模型方法对其进行扩展，以适应缺失数据。模拟结果表明，我们的程序对于随机缺失数据的有效性和效率。我们在 SAS、Stata 和 R 中使用原始 DeLong 数据对我们提出的程序进行了说明。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Extending the DeLong algorithm for comparing areas under correlated receiver operating characteristic curves with missing data.

A nonparametric method proposed by DeLong et al in 1988 for comparing areas under correlated receiver operating characteristic curves is used widely in practice. However, the DeLong method as implemented in popular software quietly deletes individuals with any missing values, yielding potentially invalid and/or inefficient results. We simplify the DeLong algorithm using ranks and extend it to accommodate missing data by using a mixed model approach for multivariate data. Simulation results demonstrate the validity and efficiency of our procedure for data missing at random. We illustrate our proposed procedure in SAS, Stata, and R using the original DeLong data.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Statistics in Medicine 医学-公共卫生、环境卫生与职业卫生

CiteScore

3.40

自引率

10.00%

发文量

334

审稿时长

2-4 weeks

期刊介绍： The journal aims to influence practice in medicine and its associated sciences through the publication of papers on statistical and other quantitative methods. Papers will explain new methods and demonstrate their application, preferably through a substantive, real, motivating example or a comprehensive evaluation based on an illustrative example. Alternatively, papers will report on case-studies where creative use or technical generalizations of established methodology is directed towards a substantive application. Reviews of, and tutorials on, general topics relevant to the application of statistics to medicine will also be published. The main criteria for publication are appropriateness of the statistical methods to a particular medical problem and clarity of exposition. Papers with primarily mathematical content will be excluded. The journal aims to enhance communication between statisticians, clinicians and medical researchers.