Kim Min Seo, Yon Dong Keon, Lee Seung Won, Rahmati Masoud, Solmi Marco, Carvalho Andre F, Koyanagi Ai, Smith Lee, Shin Jae Il, Ioannidis John Pa
Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul 06351, Republic of Korea.
Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA.
J R Soc Med. 2025 Aug 21:1410768251366880. doi: 10.1177/01410768251366880.
ObjectivesImmortal time bias (ITB) occurs when a period during which, by design, participants cannot experience the outcome (like death) is incorrectly included in the treatment group's follow-up, artificially making the treatment look better than it truly is. We aimed to identify a systematic sample of cases of ITB in the literature of studies using survival analysis and assess the impact of ITB on the results.DesignMeta-epidemiological study (PROSPERO[CRD42022356073]).SettingWe searched PubMed/MEDLINE, Embase and Cochrane Database of Systematic Reviews from database inception to August 2024. Systematic reviews with quantitative syntheses that allowed subgroup analysis by the presence of ITB for any available exposure-outcome pairs ('topics') were eligible for inclusion.ParticipantsParticipants included in the systematic reviews.Main outcome measuresInformation on ITB and effect sizes (ESs) with 95% confidence interval for individual studies in forest plots were extracted to run re-analysis using generic inverse variance fixed- and random-effects methods. After extracting data, we conducted subgroup analysis by the presence of ITB for all available topics and assessed the impact of ITB on the heterogeneity (), vulnerability of evidence (or conclusion), statistical significance of the finding, and altering ES in favour of intervention/exposure.ResultsThe median (interquartile range (IQR)) number of studies included for a topic was 6 (4-10). Across 25 topics (including 182 studies), 44.0% of the eligible studies (80 studies) were affected by ITB. Among the 21 topics where both studies with ITB and studies without ITB were available (four topics only had studies unaffected by ITB), 57.1% (12/21) demonstrated statistically significant results only in studies with ITB ( = 11 topics) or only in studies without ITB (one topic). In 23.8% (5/21), the overall summary results changed from statistically significant to non-statistically significant or vice versa after excluding studies with ITB. The ratio of ES - summary ES from studies with ITB relative to summary ES from studies without ITB - was 0.71 (95% CI, 0.66-0.78), suggesting that the ES from studies with ITB was larger by an average of 29% in favour of the intervention/exposure. Excluding studies involving ITB reduced between-study heterogeneity () by 21.4% on average.ConclusionsITB can be common among studies in some medical areas, and its presence may substantially inflate the ESs and lead to misleading, exaggerated evidence.
目的:当在设计上参与者无法经历某种结局(如死亡)的时间段被错误地纳入治疗组的随访时间时,就会出现永生时间偏倚(ITB),这会人为地使治疗效果看起来比实际更好。我们旨在从使用生存分析的研究文献中识别出ITB病例的系统样本,并评估ITB对研究结果的影响。
设计:Meta流行病学研究(PROSPERO[CRD42022356073])。
研究背景:我们检索了PubMed/MEDLINE、Embase和Cochrane系统评价数据库,检索时间从数据库建立至2024年8月。纳入的系统评价需具备定量合成内容,且能按是否存在ITB对任何可用的暴露-结局对(“主题”)进行亚组分析。
研究对象:系统评价中纳入的研究对象。
主要结局指标:提取森林图中各研究的ITB信息及效应量(ESs)和95%置信区间,采用通用逆方差固定效应和随机效应方法进行重新分析。提取数据后,我们按是否存在ITB对所有可用主题进行亚组分析,并评估ITB对异质性()、证据(或结论)的脆弱性、研究结果的统计学显著性以及使效应量更有利于干预/暴露的影响。
结果:一个主题纳入研究数量的中位数(四分位间距(IQR))为6(4 - 10)。在25个主题(包括182项研究)中,44.0%的符合条件的研究(80项研究)受到ITB影响。在21个既有受ITB影响的研究又有未受ITB影响的研究的主题中(4个主题仅有未受ITB影响的研究),57.1%(12/21)仅在受ITB影响的研究中(= 11个主题)或仅在未受ITB影响的研究中(1个主题)显示出统计学显著结果。在23.8%(5/21)的主题中,排除受ITB影响的研究后,总体汇总结果从统计学显著变为非统计学显著,反之亦然。受ITB影响的研究的效应量与未受ITB影响的研究的汇总效应量之比为0.71(95%CI,0.66 - 0.78),这表明受ITB影响的研究的效应量平均高出29%,更有利于干预/暴露。排除涉及ITB的研究平均使研究间异质性()降低了21.4%。
结论:ITB在某些医学领域的研究中可能很常见,其存在可能会大幅夸大效应量,并导致产生误导性、夸张的证据。