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

Hormuzd A Katki, Philip C Prorok, Philip E Castle, Lori M Minasian, Paul F Pinsky
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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 to 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 multi-cancer 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 provide particularly strong power gains for MCD tests.
通过检测对照组标本提高筛查试验的能力:应用于多癌检测筛查
背景癌症筛查试验需要大量样本和较长的时间跨度来证明癌症死亡率的降低,而这正是癌症筛查的主要目标。我们研究了利用对照组标本测试信息的假设和潜在功率增益,我们称之为 "预期效应"(IE)分析,并在此详细说明。IE 分析尤其适用于可在控制臂储存标本上进行的检测,如用于多癌检测(MCD)的储存血液。方法 我们模拟了假设的 MCD 筛查试验,以比较标准分析与 IE 分析的功率和样本大小。在本文详述的两个假设下,我们对 3 项现有筛查试验(全国肺筛查试验 (NLST)、明尼苏达结肠癌控制研究 (MINN-FOBT-A) 和前列腺癌、肺癌、结直肠癌、卵巢癌筛查试验-结直肠部分 (PLCO-CRC))进行了 IE 分析预测。结果 与现有 3 项试验的标准分析相比,IE 设计可将癌症特异性死亡率 p 值降低 5 倍(NLST)、33 倍(MINN-FOBT-A)或 14,160 倍(PLCO-CRC),或者将样本量(90% 功率)减少 26%(NLST)、48%(MINN-FOBT-A)或 59%(PLCO-CRC)。对于潜在的 MCD 试验设计,标准分析需要每臂 100,000 例受试者才能达到 90% 的多癌症死亡率检测功率,而 IE 分析每臂只需 37,500-50,000 例受试者就能达到 90% 的检测功率,具体取决于对照臂检测阳性的假设。结论 在筛查试验的对照组中检测储存的标本以进行 IE 分析,可大大提高降低样本量或加速试验的功率,尤其可为多发性癌症检测提供更强的功率增益。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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