量化多癌症检测试验的过度诊断:一种新方法

IF 1.8 4区 医学 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY Statistics in Medicine Pub Date : 2024-12-30 Epub Date: 2024-11-26 DOI:10.1002/sim.10285
Stuart G Baker
{"title":"量化多癌症检测试验的过度诊断:一种新方法","authors":"Stuart G Baker","doi":"10.1002/sim.10285","DOIUrl":null,"url":null,"abstract":"<p><p>Multicancer detection (MCD) tests use blood specimens to detect preclinical cancers. A major concern is overdiagnosis, the detection of preclinical cancer on screening that would not have developed into symptomatic cancer in the absence of screening. Because overdiagnosis can lead to unnecessary and harmful treatments, its quantification is important. A key metric is the screen overdiagnosis fraction (SOF), the probability of overdiagnosis at screen detection. Estimating SOF is notoriously difficult because overdiagnosis is not observed. This estimation is more challenging with MCD tests because short-term results are needed as the technology is rapidly changing. To estimate average SOF for a program of yearly MCD tests, I introduce a novel method that requires at least two yearly MCD tests given to persons having a wide range of ages and applies only to cancers for which there is no conventional screening. The method assumes an exponential distribution for the sojourn time in an operational screen-detectable preclinical cancer (OPC) state, defined as once screen-detectable (positive screen and work-up), always screen-detectable. Because this assumption appears in only one term in the SOF formula, the results are robust to violations of the assumption. An SOF plot graphs average SOF versus mean sojourn time. With lung cancer screening data and synthetic data, SOF plots distinguished small from moderate levels of SOF. With its unique set of assumptions, the SOF plot would complement other modeling approaches for estimating SOF once sufficient short-term observational data on MCD tests become available.</p>","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":" ","pages":"5935-5943"},"PeriodicalIF":1.8000,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11639630/pdf/","citationCount":"0","resultStr":"{\"title\":\"Quantifying Overdiagnosis for Multicancer Detection Tests: A Novel Method.\",\"authors\":\"Stuart G Baker\",\"doi\":\"10.1002/sim.10285\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Multicancer detection (MCD) tests use blood specimens to detect preclinical cancers. A major concern is overdiagnosis, the detection of preclinical cancer on screening that would not have developed into symptomatic cancer in the absence of screening. Because overdiagnosis can lead to unnecessary and harmful treatments, its quantification is important. A key metric is the screen overdiagnosis fraction (SOF), the probability of overdiagnosis at screen detection. Estimating SOF is notoriously difficult because overdiagnosis is not observed. This estimation is more challenging with MCD tests because short-term results are needed as the technology is rapidly changing. To estimate average SOF for a program of yearly MCD tests, I introduce a novel method that requires at least two yearly MCD tests given to persons having a wide range of ages and applies only to cancers for which there is no conventional screening. The method assumes an exponential distribution for the sojourn time in an operational screen-detectable preclinical cancer (OPC) state, defined as once screen-detectable (positive screen and work-up), always screen-detectable. Because this assumption appears in only one term in the SOF formula, the results are robust to violations of the assumption. An SOF plot graphs average SOF versus mean sojourn time. With lung cancer screening data and synthetic data, SOF plots distinguished small from moderate levels of SOF. With its unique set of assumptions, the SOF plot would complement other modeling approaches for estimating SOF once sufficient short-term observational data on MCD tests become available.</p>\",\"PeriodicalId\":21879,\"journal\":{\"name\":\"Statistics in Medicine\",\"volume\":\" \",\"pages\":\"5935-5943\"},\"PeriodicalIF\":1.8000,\"publicationDate\":\"2024-12-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11639630/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Statistics in Medicine\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1002/sim.10285\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/11/26 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.10285","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/11/26 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"MATHEMATICAL & COMPUTATIONAL BIOLOGY","Score":null,"Total":0}
引用次数: 0

摘要

多癌症检测(MCD)试验使用血液标本来检测临床前癌症。一个主要的问题是过度诊断,即在筛查中发现了临床前癌症,而如果没有进行筛查,这些癌症是不会发展成有症状的癌症的。由于过度诊断会导致不必要和有害的治疗,因此对其进行量化非常重要。一个关键指标是筛查过度诊断率(SOF),即筛查时过度诊断的概率。由于无法观察到过度诊断,因此估算 SOF 十分困难。对于 MCD 检测来说,这种估算更具挑战性,因为该技术变化迅速,需要短期结果。为了估算每年进行一次 MCD 检测项目的平均 SOF,我引入了一种新方法,该方法要求每年至少对不同年龄段的人群进行两次 MCD 检测,并且只适用于没有进行常规筛查的癌症。该方法假定在可操作筛查检测的临床前癌症(OPC)状态下的停留时间为指数分布,即一旦可筛查检测(筛查和检查结果呈阳性),则始终可筛查检测。由于这一假设只出现在 SOF 公式中的一个项中,因此结果对违反这一假设的情况是稳健的。SOF 图是平均 SOF 与平均停留时间的关系图。通过肺癌筛查数据和合成数据,SOF 图可以区分 SOF 的小度和中度水平。SOF 图具有一套独特的假设条件,一旦获得足够的 MCD 检测短期观察数据,它将成为其他估算 SOF 的建模方法的补充。
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
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Quantifying Overdiagnosis for Multicancer Detection Tests: A Novel Method.

Multicancer detection (MCD) tests use blood specimens to detect preclinical cancers. A major concern is overdiagnosis, the detection of preclinical cancer on screening that would not have developed into symptomatic cancer in the absence of screening. Because overdiagnosis can lead to unnecessary and harmful treatments, its quantification is important. A key metric is the screen overdiagnosis fraction (SOF), the probability of overdiagnosis at screen detection. Estimating SOF is notoriously difficult because overdiagnosis is not observed. This estimation is more challenging with MCD tests because short-term results are needed as the technology is rapidly changing. To estimate average SOF for a program of yearly MCD tests, I introduce a novel method that requires at least two yearly MCD tests given to persons having a wide range of ages and applies only to cancers for which there is no conventional screening. The method assumes an exponential distribution for the sojourn time in an operational screen-detectable preclinical cancer (OPC) state, defined as once screen-detectable (positive screen and work-up), always screen-detectable. Because this assumption appears in only one term in the SOF formula, the results are robust to violations of the assumption. An SOF plot graphs average SOF versus mean sojourn time. With lung cancer screening data and synthetic data, SOF plots distinguished small from moderate levels of SOF. With its unique set of assumptions, the SOF plot would complement other modeling approaches for estimating SOF once sufficient short-term observational data on MCD tests become available.

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
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.
期刊最新文献
Bayesian Modeling of Cancer Outcomes Using Genetic Variables Assisted by Pathological Imaging Data. Matching-Assisted Power Prior for Incorporating Real-World Data in Randomized Clinical Trial Analysis. Multiple Imputation for Longitudinal Data: A Tutorial. Reverse Time-to-Death as Time-Scale in Time-to-Event Analysis for Studies of Advanced Illness and Palliative Care. A Bayesian Multivariate Model With Temporal Dependence on Random Partition of Areal Data for Mosquito-Borne Diseases.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1