{"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}
引用次数: 0
Abstract
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.
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。
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.