Increasing power in screening trials by testing control-arm specimens: application to multicancer detection screening

医学 癌症筛查 样本量测定 结直肠癌 全国肺筛查试验 假阳性悖论 癌症 统计能力 肺癌筛查 肺癌 内科学 肿瘤科 统计 计算机科学 人工智能 数学
作者
Hormuzd A. Katki,Philip C. Prorok,Philip E. Castle,Lori M. Minasian,Paul F. Pinsky
出处
期刊:Journal of the National Cancer Institute [Oxford University Press]
卷期号:116 (8): 1280-1287 被引量:3
标识
DOI:10.1093/jnci/djae083
摘要

Abstract Background Cancer screening trials have required large sample sizes and long time-horizons to demonstrate cancer mortality reductions, the primary goal of cancer screening. We examine assumptions and potential power gains from exploiting information from testing control-arm specimens, which we call the “intended effect” (IE) analysis that we explain in detail herein. The IE analysis is particularly suited to tests that can be conducted on stored specimens in the control arm, such as stored blood for multicancer detection (MCD) tests. Methods We simulated hypothetical MCD screening trials to compare power and sample size for the standard vs IE analysis. Under two assumptions that we detail herein, we projected the IE analysis for 3 existing screening trials (National Lung Screening Trial [NLST], Minnesota Colon Cancer Control Study [MINN-FOBT-A], and Prostate, Lung, Colorectal, Ovarian Cancer Screening Trial—colorectal component [PLCO-CRC]). Results Compared with the standard analysis for the 3 existing trials, the IE design could have reduced cancer-specific mortality P values 5-fold (NLST), 33-fold (MINN-FOBT-A), or 14 160-fold (PLCO-CRC) or, alternately, reduced sample size (90% power) by 26% (NLST), 48% (MINN-FOBT-A), or 59% (PLCO-CRC). For potential MCD trial designs requiring 100 000 subjects per arm to achieve 90% power for multicancer mortality for the standard analysis, the IE analysis achieves 90% power for only 37 500-50 000 per arm, depending on assumptions concerning control-arm test-positives. Conclusions Testing stored specimens in the control arm of screening trials to conduct the IE analysis could substantially increase power to reduce sample size or accelerate trials and could provide particularly strong power gains for MCD tests.
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