Division of Cancer Epidemiology and Genetics, Department of Health and Human Services, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
Division of Cancer Prevention, Department of Health and Human Services, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
J Natl Cancer Inst. 2024 Oct 1;116(10):1675-1682. doi: 10.1093/jnci/djae218.
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.
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]).
Compared with the standard analysis for the 3 existing trials, the IE design could have reduced cancer-specific mortality P values 6-fold (NLST), 33-fold (MINN-FOBT-A), or 260 000-fold (PLCO-CRC) or, alternately, reduced sample size (90% power) by 25% (NLST), 47% (MINN-FOBT-A), or 63% (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.
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.
癌症筛查试验需要大样本量和长时间跨度才能证明癌症死亡率降低，这是癌症筛查的主要目标。我们检验了利用对照臂标本检测信息的假设和潜在效能增益，我们称之为“预期效果”（IE）分析，我们将在此详细解释。IE 分析特别适合于可以对对照臂中储存的标本进行检测的试验，例如用于多癌种检测（MCD）试验的储存血液。
我们模拟了假设的 MCD 筛查试验，以比较标准分析与 IE 分析的效能和样本量。根据我们在此详细说明的两个假设，我们对 3 项现有筛查试验（国家肺癌筛查试验 [NLST]、明尼苏达州结肠癌控制研究 [MINN-FOBT-A]和前列腺、肺、结直肠、卵巢癌筛查试验-结直肠部分 [PLCO-CRC]）进行了 IE 分析预测。
与 3 项现有试验的标准分析相比，IE 设计可以将癌症特异性死亡率 P 值降低 6 倍（NLST）、33 倍（MINN-FOBT-A）或 260000 倍（PLCO-CRC），或者降低样本量（90%效能）25%（NLST）、47%（MINN-FOBT-A）或 63%（PLCO-CRC）。对于潜在的 MCD 试验设计，每个试验臂需要 100000 例受试者才能达到标准分析中多癌种死亡率 90%的效能，IE 分析仅需每个试验臂 37500-50000 例即可达到 90%的效能，具体取决于对照臂试验阳性的假设。
在筛查试验的对照臂中检测储存标本以进行 IE 分析可以大大提高降低样本量或加速试验的效能，并且可以为 MCD 试验提供特别强大的效能增益。
