Stage Slip from Diagnostic Latency in MCED Trials: A Calibrated Monte Carlo Reconstruction of the NHS-Galleri Results

Abstract Background The NHS-Galleri trial reported a substantial reduction in Stage IV cancer diagnoses and a four-fold increase in cancer detection rates, but did not meet its primary endpoint of reducing combined Stage III+IV diagnoses in a prespecified group of 12 cancers. We hypothesize that stage slip — progression of cancers from Stage I/II to Stage III during diagnostic workup—is the primary mechanism behind this statistical masking. Methods We developed a Monte Carlo simulation of 142,000 participants (matching NHS-Galleri enrolment) across 12 cancer types, calibrated to NHS England population stage distributions. The model represents three competing clocks: biological sojourn times, screeninginitiated or symptom-driven pathway initiation, and diagnostic infrastructure delays. The median standard-of-care diagnostic delay (92 days) was constructed from five convergent evidence streams. We estimated the number of intervention-arm cases where a cancer was biologically Stage I/II but recorded as Stage III+IV at diagnosis, and determined how many such cases would need to be recovered for the composite endpoint to reach statistical significance. We validated that the 12 deadly cancers produce a sufficiently large early-stage patient pool and confirmed robustness across a wide range of test sensitivity and diagnostic delay assumptions. Findings The calibrated control arm reproduces NHS stage distributions within 1–2 percentage points for all 12 cancers. In the intervention arm, we estimate 84 cases of stage slip (95% CI: 68–104) at published sensitivity values. If only 25 of these cases (approximately one in three) had been diagnosed before crossing the Stage II/III boundary, the composite endpoint would have reached p < 0.05 at reported sensitivity levels. The slip count is robust to sensitivity assumptions, varying from approximately 74 to 85 cases across a 30–100% range of published CCGA 3 values. Across diagnostic delays from 65 to 120 days (at realised sensitivity), the estimate ranges from approximately 55 to 95 cases. The expected pool of early-stage cancers in the 12 deadly types (∼ 260–335 screen-detected Stage I/II cases per arm) is large enough that even a 12% slip rate—corresponding to the most optimistic delay assumption—produces sufficient slippage to exceed the significance threshold. Interpretation Stage slip provides a quantitatively sufficient and mechanistically transparent explanation for the primary endpoint miss. The test’s biological performance is intact: it detects cancers earlier and reduces Stage IV diagnoses. The composite endpoint was attenuated by systemic diagnostic latency in the NHS, not by a failure of the assay. Future MCED trials should consider endpoints less vulnerable to infrastructure delays or incorporate prespecified adjustments for expected diagnostic pathway timing. Funding None. Registration Not applicable.