Papers

Anti-amyloid antibodies (lecanemab, donanemab) reproducibly slow Clinical Dementia Rating-Sum of Boxes (CDR-SB) decline by roughly 27-35%, yet whether this is clinically meaningful remains deadlocked because trial-end group-mean differences (0.45-0.67 points over 18 months) fall below anchor-derived minimal clinically important differences (MCIDs). This paper argues the deadlock is largely an estimand artifact: a fixed-time mean difference and a between-person anchor-based MCID are incommensurable quantities, and under a constant proportional-slowing model the absolute between-arm gap is an arbitrary function of when outcomes are measured. Using only published summary statistics, I back-calculate the implied outcome variance, demonstrate the timepoint-dependence numerically, and report a reproducible power analysis with a candid negative result: a naive longitudinal CDR-SB slope confers no inherent power advantage, so the real efficiency lever is a higher signal-to-noise surrogate, not longitudinal modeling per se. I propose a falsifiable biomarker-velocity adaptive platform that co-estimates a plasma p-tau217-velocity surrogate against the clinical endpoint through a pre-specified trial-level surrogacy gate (Prentice/meta-analytic R-squared), with APOE4-stratified safety, and I state exactly what prospective data would confirm or refute each claim. All quantitative claims are model-based or derived from cited aggregate data; no patient-level data were used or invented.

Multi-cancer early detection (MCED) tests issue many cancer-type-specific positive calls from one blood draw, so a single overall specificity is really a shared false-positive budget split across cancer-type detectors. Standard multiclass Neyman-Pearson theory allocates such a budget to maximize detections, and the MCED literature has noted only as a fortunate empirical accident that these tests happen to under-detect indolent, overdiagnosis-prone cancers. We give the normative result behind that accident. Working purely from expected-utility theory over published-style parameters - we run no trial and report no measurements - we show that the budget should be allocated to maximize net benefit, weighting each cancer-type detector by an actionable-fraction value v_k = m_k B_k - (1-m_k) H_k that subtracts overtreatment harm from indolent detections. The optimum equalizes the value-weighted marginal detection rate pi_k v_k g_k'(f_k) and admits a cancer type only when pi_k v_k g_k'(0) exceeds the per-false-positive work-up harm. This inverts the detection-maximizing rule: a common, easily detected, but indolent cancer can optimally receive less budget - or zero - than a rarer, less detectable, but lethal and actionable one. Deliberately under-spending specificity on overdiagnosis-prone cancers is therefore optimal design, not a biological accident, and detection-count-optimized panels are predictably misallocated. We give the inclusion threshold, the exclusion result, and exactly what a trial must measure to use the rule.

Observational studies of cancer immunotherapy frequently compare patients who received a treatment to those who did not, but eligibility for treatment often requires surviving long enough to receive it, introducing immortal-time bias that spuriously favours the treated group. We give a methodological framework that identifies when published checkpoint-blockade observational analyses are vulnerable to this bias and shows how landmark analysis and time-varying-exposure models correct it. We derive the direction and approximate magnitude of the bias as a function of the treatment-initiation delay and the baseline hazard, working entirely from established survival-analysis theory and published summary statistics. No patient data are collected or analysed; the contribution is an analytic framework, a checklist, and worked corrections of published designs that flag what prospective validation would require.