Paul F Pinsky

Contrasts in colorectal cancer incidence and mortality in screening trials of sigmoidoscopy vs the Nordic-European Initiative on Colorectal Cancer colonoscopy trial

Abstract Background Interim 10-year results from the Nordic-European Initiative on Colorectal Cancer (NordICC), a randomized controlled trial (RCT) of screening colonoscopy, demonstrated a statistically significant reduction in colorectal cancer (CRC) incidence but not mortality, contrary to results from 4 flexible sigmoidoscopy RCTs. Methods We constructed CRC incidence and mortality Kaplan–Meier curves through 10 years to standardize comparisons across RCTs and examined CRC screen detection and stage. Novel analyses of 1 flexible sigmoidoscopy RCT (Prostate, Lung, Colorectal, and Ovarian cancer screening trial [PLCO]) assessed year-by-year mortality in screen-detected CRCs. Results At 10 years, all RCTs demonstrated statistically significant CRC incidence reductions with screening (ratio = 0.77, 95% confidence interval [CI] = 0.70 to 0.84, to ratio = 0.82, 95% CI = 0.69 to 0.97, vs control arm; P ≤ .011). Two flexible sigmoidoscopy RCTs and NordICC showed no statistically significant CRC mortality reduction (ratio = 0.84, 95% CI = 0.64 to 1.10, to ratio = 0.90, 95% CI = 0.69 to 1.18; P = .10-0.23). In 3 flexible sigmoidoscopy RCTs and NordICC, relative reductions were greater in CRC incidence than CRC mortality, but only NordICC reported higher CRC mortality with screening vs the control arm for the first 7 years. In contrast, PLCO observed fewer CRC deaths with screening by year 2 (ratio = 0.59; P = .03), and screen-detected CRCs were less often advanced (odds ratio = 0.26; P < .001) or fatal (ratio = 0.50; P < .001). Conclusions After 10 years, NordICC is similar to 2 flexible sigmoidoscopy RCTs in observing statistically significant reductions in CRC incidence but not CRC mortality. However, only NordICC observed greater CRC mortality with screening vs the control arm for 7 years. Granular analyses of CRC cases and deaths in NordICC, paralleling our PLCO analyses, could provide insight into why CRC mortality results differ in NordICC vs flexible sigmoidoscopy RCTs.

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

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 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. 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.

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

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.

Estimating stage-specific sensitivity for cancer screening tests

Objectives When evaluating potential new cancer screening modalities, estimating sensitivity, especially for early-stage cases, is critical. There are methods to approximate stage-specific sensitivity in asymptomatic populations, both in the prospective (active screening) and retrospective (stored specimens) scenarios. We explored their validity via a simulation study. Methods We fit natural history models to lung and ovarian cancer screening data that permitted estimation of stage-specific (early/late) true sensitivity, defined as the probability subjects screened in the given stage had positive tests. We then ran simulations, using the fitted models, of the prospective and retrospective scenarios. Prospective sensitivity by stage was estimated as screen-detected divided by screen-plus interval-detected cancers, where stage is defined as stage at detection. Retrospective sensitivity by stage was estimated based on cancers detected within specified windows before clinical diagnosis with stage defined as stage at clinical diagnosis. Results Stage-specific true sensitivities estimated by the lung cancer natural history model were 47% (early) and 63% (late). Simulation results for the prospective setting gave estimated sensitivities of 81% (early) versus 62% (late). In the retrospective scenario, early/late sensitivity estimates were 35%/57% (1-year window) and 27%/49% (2-year window). In the prospective scenario, most subjects with negative early-stage screens presented as other than early-stage interval cases. Results were similar for ovarian cancer, with estimated prospective sensitivity much greater than true sensitivity for early stage, 84% versus 25%. Conclusions Existing methods for approximating stage-specific sensitivity in both prospective and retrospective scenarios are unsatisfactory; improvements are needed before they can be considered to be reliable.