Synthesis is a peer-reviewed, diamond open-access journal publishing concise, transparent, and reproducible evidence syntheses across all areas of medicine. Articles appear continuously online and are compiled into monthly issues. There are no fees for authors and no fees for readers; every article is licensed under CC BY 4.0 with author copyright retained. The journal's encouraged default format is the E156 Research Letter: a 156-word abstract-as-paper. Long-form methods notes, narrative reviews, case-based teaching, editorials, viewpoints, and debate pieces are equally welcome. Synthesis adheres to the recommendations of ICMJE, COPE, and WAME. ISSN application is in preparation; PubMed Central, DOAJ, Scopus, and Web of Science inclusion are actively being pursued.
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Articles in Synthesis are published under the Creative Commons Attribution 4.0 International (CC BY 4.0) licence. Authors retain copyright.
NICE Cardiology Guidance Under the Microscope: A Data-Driven Statistical Critique
Correction (Version 2 — 10 July 2026): This corrected version repairs reference and arithmetic errors in the original while confirming its core result. Seven of twelve reference PMIDs were wrong and are corrected; the original E-value (~2.1) was arithmetically incorrect and is corrected to 1.85; and the unverifiable claim that NICE was the “last of six” bodies on a specific named technology appraisal is withdrawn as not independently verifiable. The reproducible core is unchanged and independently regenerated by the companion script: the SGLT2-inhibitor class effect on cardiovascular death or heart-failure hospitalisation is HR 0.79 (I²=8%, Q=7.61), and a new analysis shows no difference between dedicated heart-failure trials and diabetes cardiovascular-outcome trials (interaction p=0.72). Background. Sodium–glucose co-transporter-2 (SGLT2) inhibitors are among the most thoroughly evidenced advances in cardiovascular pharmacotherapy of the past decade. We ask two questions: (i) how statistically robust and reversal-resistant is the class effect on the composite of cardiovascular death or heart-failure hospitalisation (CV-death/HHF), and (ii) can guideline-adoption timing across national bodies be quantified from the public record? Methods. Eight pivotal randomised trials reporting a CV-death/HHF composite were pooled on the log-hazard-ratio scale using REML (primary), Paule–Mandel and DerSimonian–Laird τ² estimators, each with a Hartung–Knapp–Sidik–Jonkman (HKSJ) t-interval (q≥1 floor) and, for like-for-like reproduction, a random-effects z-interval. We report Cochran Q, I², a 95% prediction interval, leave-one-out influence, a new dedicated-HF-trial-versus-diabetes-CVOT subgroup interaction test, a seeded Monte-Carlo reversal probability, and E-values. Guideline dates were taken from the primary guideline publications; unverifiable claims are flagged, not asserted. Results. Pooled HR 0.79 (REML; RE z-CI 0.749–0.827; HKSJ 0.739–0.837), I²=8.0%, Q=7.61 (df=7, p=0.37), τ²≈0. The 95% prediction interval (0.74–0.84) excludes 1. All eight leave-one-out estimates lie in 0.78–0.80 with CIs excluding 1. The class effect does not differ between the four dedicated HF trials (HR 0.781) and the four diabetes CVOTs (HR 0.795): interaction p=0.72. Monte-Carlo P(HR≥1) < 0.001%. E-value 1.85 (point), 1.71 (CI bound). Conclusion. The SGLT2-inhibitor CV-death/HHF class effect is statistically mature, homogeneous across trial designs, and highly reversal-resistant. The existence of measurable inter-body adoption lags is defensible from public dates; the specific claim that NICE was "last of six" is not independently verifiable from the substrate here and is flagged accordingly. ---
Copyright (c) 2026 The Author(s) https://creativecommons.org/licenses/by/4.0/
Dynamics of Zoonotic Disease at the One Health Interface: Evidence from a Systematic Review
Correction (Version 2 — 10 July 2026): The original article's thesis — that Africa is deficient in zoonotic-disease trials, evidenced by zoonotic disease being only ~0.3% of African trials — is WITHDRAWN as a denominator artifact. Recomputed from ClinicalTrials.gov/AACT, African sites host 22.92% of zoonotic-condition trials, 5.3× the 4.33% baseline African share of all trials: Africa is over-represented in zoonotic trials, exactly as the epidemiology (Ebola/Marburg/Rift Valley/Lassa are intrinsically African) predicts. Recycled and anachronistic reference PMIDs have been corrected. Corrected finding: the real gaps are a global scarcity of zoonotic trials, an outbreak-reactive research cycle, neglect of endemic zoonoses (rabies, brucellosis, anthrax), and an African investigator-leadership gap. Background. Africa carries much of the global burden of epidemic and endemic zoonoses, and the original analysis read a "0.3% zoonotic trial share" as African research neglect. We test that framing reproducibly. Methods. Zoonotic-condition trials were recomputed from the AACT April-12-2026 snapshot (`_africa_equity_verify.py`), with like-for-like and baseline comparisons (`53-zoonotic-verify.py`). Every PMID was re-verified against PubMed metadata. Results. African sites host 99 of 432 zoonotic-condition trials (22.92%; interventional 69/344, 20.06%) — 5.3× the 4.33% baseline African trial share. Zoonotic disease is 0.39% of African trials but only 0.075% of global trials (5.3× higher in Africa). So the "0.3%" is a denominator artifact, not a deficit. What survives: only 432 zoonotic trials exist worldwide (a global scarcity); registrations cluster around outbreaks (2014–16 Ebola, 2022 Mpox); endemic zoonoses (rabies ~59,000 global / ~21,500 African deaths/yr, Hampson 2015; brucellosis; anthrax) attract few trials; and activity concentrates in Egypt and South Africa. Emergence context is verified: 60.3% of emerging infectious diseases are zoonotic (Jones 2008); 61% of human pathogens and 75% of emerging pathogens are zoonotic (Taylor 2001). Conclusion. Africa is not under-represented in zoonotic trials relative to the world — it is over-represented, as the epidemiology dictates. The real gaps are the global scarcity of zoonotic trials, the outbreak-reactive research cycle, the neglect of endemic zoonoses, and African investigator leadership — a more defensible and more actionable diagnosis than the withdrawn "0.3% deficit." ---
Copyright (c) 2026 Nakhabi Anna, Jacob Muruhukye, Eleanor Sabuka, Christopher Tibamwenda, Lauritta Chinazaekpere Ndufeiya, Gloria Margaret, Nanono, Muhammad Murtala Yusuf, Grace Kemigisha , Phiona Obbo, Peter Ebot Eyong, Prossy Nabateregga , Nwanja Olive Ebere https://creativecommons.org/licenses/by/4.0
The Antimicrobial-Resistance “Trial Deficit” in Africa, Re-examined: Why the Headline Statistic Fails and What the Real Gap Is
Correction (Version 2 — 10 July 2026): The original article's central thesis — that Africa suffers a structural antimicrobial-resistance (AMR) “trial deficit”, evidenced by AMR being only ~0.2% (“virtually 0%”) of African trials — is WITHDRAWN as a denominator artifact. Recomputed from ClinicalTrials.gov/AACT, African sites host 18.15% of AMR-condition trials, which is 4.2× the 4.33% baseline African share of all trials: Africa is over-represented, not under-represented, on this metric, and at near parity once adjusted for AMR burden (18.8 vs 20.4 trials per 100,000 attributable deaths). In addition, 8 of 9 checkable reference PMIDs in the original pointed to unrelated papers and have been corrected. Corrected finding: the genuine problem is a global scarcity of AMR trials relative to burden, plus the absence of novel-agent trials at African sites and concentration in South Africa/MDR-TB. Background. Africa carries a heavy antimicrobial-resistance (AMR) burden, and the original analysis argued that a "0.2% AMR trial share" evidences a structural African trial deficit. We test that claim reproducibly. Methods. AMR-condition trials were recomputed from the AACT April-12-2026 snapshot (reusable `_africa_equity_verify.py`), and like-for-like plus burden-adjusted comparisons were derived (`54-amr-crisis-verify.py`). Burden anchors were taken from the PubMed-verified GRAM global (Murray 2022) and WHO African-region (Sartorius 2024) analyses. Every PMID was re-verified. Results. African sites host 47 of 259 AMR-condition trials (18.15%), 4.2× the 4.33% baseline African trial share; interventional 31/162 (19.14%). AMR is 0.19% of African trials but only 0.045% of global trials (a 4.2× higher Africa fraction). Burden-adjusted, Africa runs 18.8 vs the global 20.4 AMR trials per 100,000 attributable AMR deaths — near parity. WHO African-region 2019 burden: 250,000 attributable / 1.05 million associated deaths (Sartorius 2024), against a global 1.27 million attributable (Murray 2022). What survives as real: the AMR trial enterprise is tiny relative to burden everywhere; novel-agent (cefiderocol, ceftazidime-avibactam, imipenem-relebactam) trials at African sites are absent; African AMR trials concentrate in South Africa and MDR-TB. Conclusion. Africa is not specifically under-represented in AMR trials on any like-for-like or burden-adjusted metric. The genuine crisis is a global scarcity of AMR trials relative to burden, plus specific gaps in novel-agent evaluation and geographic breadth — a more defensible and more actionable framing than the withdrawn "0.2% deficit." ---
Copyright (c) 2026 Gloria Margaret Nanono, Mr., Mr., Ms., Mr., Ms., Prossy Nabateregga https://creativecommons.org/licenses/by/4.0
Inequities in Clinical Trial Distribution for Neglected Tropical Diseases: A Cross-Sectional Analysis of Global Research Concentration
Correction (Version 2 — 10 July 2026): This corrected version confirms the original finding and repairs its references. The central result reproduces: neglected tropical diseases receive ~0.85% of African clinical-trial activity, versus roughly 8× more for HIV. Corrections: five of six checkable reference PMIDs were wrong and are fixed; a substring-matching artifact (the token “noma” matching melanoma/carcinoma/lymphoma) that had inflated the NTD count 2.5-fold was removed; and the burden-weighted “~30-fold” gap is softened to the verified 8× trial-count gap. The African deficit is reframed as the local expression of a global ~1% allocation of trial and drug-development effort to neglected diseases (Pedrique 2013; Trouiller 2002). Background. Neglected tropical diseases (NTDs) impose a large burden on low-income populations, especially in Africa, yet attract little clinical-trial activity. We reproduce and reference-repair the original registry analysis. Methods. African-site trials for NTDs (WHO-Roadmap African-endemic set) and three comparators (HIV/AIDS, cancer, cardiovascular disease) were recomputed from the AACT April-12-2026 snapshot (`40-ntd-verify.py`), with careful exclusion of substring false-matches. Every PMID was re-verified against PubMed metadata. Results. Of 25,125 African-site trials, 214 (0.85%) targeted NTDs, versus HIV/AIDS 1,713 (6.8%), cancer 1,731 (6.9%), and CVD 946 (3.8%) — reproducing the v1 counts (209 / 1,691 / 1,659 / 1,012). HIV alone has ~8× the NTD trial count. This African under-representation mirrors a global neglect: Pedrique 2013 found only ~1% of registered clinical trials and 1% of new chemical entities (2000–11) targeted neglected diseases, and Trouiller 2002 found 16 of 1,393 NCEs (1975–99) were for tropical diseases and TB. The v1's burden-weighted "~30-fold" gap depends on GBD burden inputs not verifiable here and is softened to the verified trial-count gap. Conclusion. The NTD trial deficit is real and reproduces cleanly. It is best understood as the African expression of a global structural neglect of neglected-disease R&D — a framing that both the reproduced counts and two independent verified landscape studies support. ---
Copyright (c) 2026 Ronald Bwambale, Mutebi, Nelson Nsubuga, Janepher Nabaasa, Aggrey Gavamukulya, Persis Tikabulamu, Geofrey Emesu, Phiona Obbo https://creativecommons.org/licenses/by/4.0
Effectiveness of Differentiated Service Delivery ART Care Among Priority Populations in Sub-Saharan Africa: Evidence from a Systematic Review and Meta-analysis
Correction (Version 2 — 10 July 2026): This version is a truth-first rebuild of the original advanced draft, which contained placeholder PRISMA counts, a fabricated per-model subgroup table, an unsourced patient-satisfaction estimate, and several citations whose coordinates pointed to unrelated papers. It is re-anchored on two PubMed-verified RCT-only meta-analyses (Bwire 2023; Nega 2026). The evidence base is corrected from “predominantly observational (VERY LOW)” to randomised (viral-suppression certainty HIGH; retention MODERATE), and genuine model-level heterogeneity (I² up to 90% within single models) is restored in place of the original's fabricated uniform low heterogeneity. Findings: DSD preserves viral suppression (RR 1.01, 95% CI 1.00–1.02) and improves retention (RR 1.09; ~7 percentage points). Background. Differentiated service delivery (DSD) for antiretroviral therapy (ART) tailors care intensity to clinically stable patients — community ART groups, adherence clubs, multi-month dispensing, and community pickup points. We synthesise the strongest available evidence (randomised trials) on whether DSD preserves viral suppression and improves retention versus standard facility care. Methods. Truth-first synthesis anchored on two independent RCT-only meta-analyses in sub-Saharan Africa: Bwire 2023 (16 RCTs, 13,886 participants) and Nega 2026. Estimates are taken verbatim from the published reports and cross-checked by a deterministic script that (i) tests non-inferiority against a 0.95 margin, (ii) re-expresses Nega's non-suppression estimate on the suppression side to test concordance with Bwire (without pooling overlapping trials), and (iii) frames the verified retention benefit in absolute terms with a stated baseline. GRADE for RCT evidence. Results. Across 16 RCTs, any DSD model versus standard care gave viral suppression RR 1.01 (95% CI 1.00–1.02, I²=0%) and retention RR 1.09 (1.08–1.11, I²=0%) (Bwire 2023). The independent Nega 2026 MA concurred: viral non-suppression RR 0.89 (0.74–1.07) — implying a suppression-side RR ≈1.01–1.02 across plausible baselines. Non-inferiority on suppression is met (lower CI ≥0.95). The retention benefit corresponds to an absolute gain of ~7 percentage points (NNT ≈14 at 80% baseline). Crucially, heterogeneity is model- and setting-dependent: single-model pools reach I²=77–84% (adherence clubs; Bwire) and I²=90% (non-retention; Nega), so the tight any-DSD pool must not be read as uniformity. Conclusion. RCT evidence supports DSD scale-up for stable ART patients: viral suppression is preserved and retention improves on average. But the benefit is not uniform across models or settings, and program-level results can vary widely — a nuance the original draft's fabricated low-heterogeneity table obscured. ---
Copyright (c) 2026 Christine Muhumuza, Mahmood Ahmad, Ruchius Philbert, Tamale Bridget https://creativecommons.org/licenses/by/4.0
Sponsor-Level Accountability on ClinicalTrials.gov: Modality-Stratified Backlog, Ghost Protocols, Overdue Debt, and Narrative-Field Gaps in 249,507 Older Closed Studies
ClinicalTrials.gov hosts hundreds of thousands of closed interventional studies, yet systematic accountability for missing results, absent narrative fields, and lagging actual-record discipline has rarely been mapped at the sponsor level across multiple dimensions simultaneously. This article synthesises 15 linked analyses of 249,507 eligible older closed interventional studies drawn from the March 29, 2026 full-registry snapshot. The unifying methodological thesis is that pooled, single-dimension sponsor rankings systematically understate backlog concentration: hiddenness is structurally stratified by intervention modality, geography, text-field type, and silence depth, and only modality-disaggregated, multi-lens rankings reveal which sponsors are repeat offenders across dimensions. Key quantitative findings include: GlaxoSmithKline carried 1,826 detailed-description gaps and 1,033 missing-results studies in drug portfolios, making it the largest named contributor on both text and results dimensions; Boehringer Ingelheim accumulated 15,166 unresolved overdue years and a mean unresolved age of 17.7 years; Cairo University led sponsor ghost excess at 228 studies, and Assistance Publique — Hôpitaux de Paris led adjusted excess no-results at 265 studies; missing actual enrollment corresponded to a 100.0 percent no-results rate and a 62.8 percent ghost-protocol rate, establishing actual-field compliance as the strongest single structural predictor of result opacity; and industry no-results rates rose from 27.3 percent in mixed U.S.-plus-non-U.S. portfolios to 70.9 percent in non-U.S.-only portfolios. Together, these analyses establish a multi-lens accountability framework in which sponsor rank depends fundamentally on the dimension of hiddenness being measured.
The Country Anatomy of CT.gov Transparency Gaps: A Fourteen-Lens Audit of 249,507 Older Closed Interventional Studies
ClinicalTrials.gov hosts the world's largest public registry of clinical research, yet a substantial share of older closed studies remain silent — posting no results, no linked publications, and no endpoint descriptions. Whether this silence is geographically structured, and in what ways, remains poorly characterised. We conducted fourteen pre-specified analyses of 249,507 eligible older closed interventional studies drawn from the March 29, 2026 full-registry snapshot, measuring country-linked transparency across nine distinct gap dimensions: ancient backlog, description black-box, detailed-description gap, enrollment gap, composite excess watchlist, ghost-protocol excess, narrative gap, overdue debt, primary-only gap, primary-outcome gap, country-linked sponsor concentration, text asymmetry, and U.S.-versus-ex-U.S. sponsor-class stratification. The United States dominated absolute stock tables on every dimension by virtue of registry scale; rate tables revealed a different and smaller group of countries — Iran, Japan, France, South Korea, Egypt, Norway, Slovakia — each leading on different gap dimensions. The most policy-salient finding was the jurisdictional divide: studies involving at least one U.S. site carried a 52.1% no-results rate versus 88.7% for studies with no U.S. involvement, a 36.7-percentage-point gap consistent with the regulatory reach of the FDA Amendments Act. Disease and geography interact non-additively: oncology studies involving China reached 79.0% no results versus 52.6% for those involving the United States, and cardiovascular studies involving Egypt reached 95.9%. No single country or metric captures the problem; the geography of transparency failure is multidimensional.
The Disease-Family Anatomy of CT.gov Transparency: Eleven Condition-Stratified Dimensions across 249,507 Older Closed Interventional Studies
Background: CT.gov transparency has been reviewed at the registry level. It remains unclear how opacity is distributed across therapeutic areas, and whether different types of silence cluster differently by condition family. We decompose transparency using eleven condition-stratified dimensions. Methods: We analysed 249,507 eligible older closed interventional studies from the 29 March 2026 ClinicalTrials.gov snapshot, assigning each study one condition-family label. We assessed eleven dimensions: (1) ancient backlog, (2) detailed-description gap, (3) enrollment gap, (4) excess no-results stock, (5) excess ghost stock, (6) narrative gap, (7) overdue debt, (8) primary-only gap, (9) primary-outcome gap, (10) sponsor repeaters, and (11) text asymmetry. We also conducted a geography sub-analysis. Results: Two distinct silence patterns emerged. Oncology and cardiovascular dominate absolute counts: oncology contributed 11,369 ancient-backlog studies, 255,229 cumulative overdue years (9.0 years per study), and 11,207 primary-outcome gaps; cardiovascular contributed 6,545 ancient-backlog studies, 154,672 overdue years, and 7,006 primary-outcome gaps. Healthy volunteers showed the opposite pattern — higher rates: 50.2% description gap, 35.0% primary-outcome gap, 31.5% ancient-backlog rate, 22.0% narrative gap, and 1,032 excess ghost protocols above expectation (versus below-expectation ghost counts for oncology and cardiovascular). Geography amplified these differences: non-U.S. studies showed 86.8–90.3% no-results rates, compared with 29.4–39.4% for mixed-geography studies in the same disease families. Sponsor-level pockets were invisible in registry-wide tables: the National Cancer Institute held 909 oncology no-results studies, Assistance Publique–Hôpitaux de Paris reached 100.0% no-results in cardiovascular, and Novo Nordisk carried 391 metabolic no-results studies. Conclusions: CT.gov silence is structurally patterned by condition family; accountability requires condition-stratified, geography-disaggregated, and sponsor-resolved metrics.
The Anatomy of Hiddenness: A Multi-Axis Audit of Structural Non-Disclosure in ClinicalTrials.gov
Systematic reviews increasingly use ClinicalTrials.gov as a core source for registered trials, but the completeness and linkage quality of the registry record are rarely incorporated formally into synthesis. A large number of registered studies never post results and never link to a publication. Using a single 29 March 2026 full-registry snapshot — 578,109 records, of which 441,191 were interventional and 290,524 were closed interventional — we used 249,507 eligible older closed interventional studies as the analytic core and audited structural non-disclosure along six axes. Two-year no-results rates ran from 67.0% in oncology to 76.2% in metabolic medicine. The missingness is structured: it tracks phase, architecture (one-arm 72.8% vs >=10-arm 55.3%), and outcome density (100% no-results when zero primary outcomes are recorded). Geographic footprint matters (single-site 79.5% vs >=20-site 31.7%), as does completion speed (same-year 85.7% vs 6-10-year lag 57.6%). Missingness is concentrated - the top 1% of lead sponsors hold 39.6% of the backlog (Gini 0.818). A strict NCT-indexed PubMed audit rescued only 1.2% of no-link studies; broadening to Europe PMC lifted weighted NCT-visible external-trace matching to 40.8%, indicating that a large fraction of apparent registry silence is an indexing-and-linkage gap rather than true absence. Hiddenness is a predictable, measurable property of the registry, and should be priced into downstream synthesis.
Copyright (c) 2026 Andrew Woo https://creativecommons.org/licenses/by/4.0/
The Integrity Ratio and the Transparency Discount: a registry-based framework for correcting and pricing publication bias in evidence synthesis and HTA
Background: Evidence synthesis assumes the published literature is an unbiased sample of all conducted research, yet selective publication -- positive trials published, negative trials left in the file drawer -- is pervasive. Standard corrections such as trim-and-fill and Copas selection models rest on symmetry assumptions and treat the missing studies as a theoretical quantity inferred from the shape of what was published. ClinicalTrials.gov now offers what those methods do not use: a near-census of intended research, from which the proportion of evidence that actually reached print can be measured directly. Methods: We define a single registry-derived quantity, the Integrity Ratio lambda -- the fraction of enrolled patient data visible in the published literature -- and use it twice. As a statistical correction, the Ghost-Weight Adjustment Model (GWAM) classifies registered, completed trials as published, results-posted, or ghost (completed but never reported), treats the ghost layer as latent data, re-weights the pooled effect by lambda, and propagates the uncertainty about unobserved trials through a Monte-Carlo layer. As an economic price, the Transparency Discount converts the same lambda into a corrected ratio: discounted ICER = Cost / (Effectiveness x lambda). Results: Across 5,000 Monte-Carlo replications, a standard random-effects estimator carried a bias of +0.168 in a high-suppression scenario, while GWAM reduced this to -0.050 -- trading a large optimistic bias for a small conservative one. On the Turner et al. (2008) antidepressant dataset, where the published literature claimed an effect size of 0.41 against an FDA all-trials ground truth of 0.31, a registry scan showed 27.5% of patient data missing (lambda = 0.725); GWAM recovered 0.31 and the simpler multiplicative discount returned 0.30, both within rounding of the FDA value. In a worked HTA example, a drug priced at an ICER of $40,000/QALY rose to $80,000/QALY once a lambda = 0.5 discount was applied -- crossing a conventional threshold from fundable to low-value. Conclusion: One registry-anchored metric corrects the effect size for statisticians and prices the missing evidence for payers; making non-publication financially visible turns transparency from an ethical appeal into a budget line.
One corpus, six lenses: how much of a Cochrane conclusion survives analyst choices, bias methods, and computational re-execution
Background: Whether a meta-analytic conclusion is robust depends on choices that are usually examined one review and one method at a time: which specification an analyst picks, which publication-bias method is trusted, how much evidence has accumulated, what was selectively reported, and whether the numbers can be re-derived at all. These properties are rarely measured together, and almost never on a single standardised corpus large enough to make the answer a property of the evidence base rather than of a chosen example. Methods: We assembled one validated corpus -- the Pairwise70 extract of roughly 500 Cochrane pairwise reviews -- and first established its near-independence so it could serve as a single clean benchmark (the keystone), then interrogated it with five complementary forensic engines: multiverse specification search, publication-bias method discordance, cumulative analytic stability, outcome-reporting-bias excess significance, and computational re-execution from open-access sources. Denominators differ by each engine's eligibility filter (307-591 reviews; 11,899-14,340 studies) and are reconciled explicitly. Every headline rate was reused from an independently verified component package or recomputed here from the row-level artifacts. Results: The keystone holds: the corrected covered area was 0.0001, with only 5.0% of 11,899 studies appearing in more than one of 591 reviews -- the corpus is near-independent. The forensic lenses then converge. Two robustness lenses put fragility at 40.0% of 403 reviews (Fragile or Unstable across 648 specifications) and 53.4% of 365 reviews never reaching analytic stability. Two bias lenses put the flagged share near one in six: 13.7% of 307 reviews were discordant across eight publication-bias methods, and 15.2% of 473 carried high outcome-reporting-bias risk. The re-execution lens found that only 11.8% of 14,340 studies (1,688) had the open data needed to re-verify them. Across the multiverse, bias correction was the single dominant source of specification variance (eta-squared = 0.929 in 99.0% of reviews). Conclusion: One near-independent corpus, read through five complementary lenses, returns a convergent verdict: a large share of Cochrane conclusions do not survive contact with defensible analyst choices, about one in six carry a bias or selective-reporting signature, and only about one in eight can even be re-executed. Robustness is measurable at corpus scale, reproducibly and honestly -- and the most informative output is the convergence, not any single rate.
The Shape of Disagreement: Six Mathematical Lenses on the Structure -- Not the Magnitude -- of Meta-Analytic Heterogeneity
Background: Between-study heterogeneity in meta-analysis is almost always reported as a single scalar -- I-squared or tau-squared. A scalar answers how much studies disagree, but is silent on the structure of that disagreement: whether it lives in the tails, drifts over time, breaks into clusters, or curves the parameter space. Methods: We assemble six advanced-mathematics lenses -- information theory (entropy), extreme value theory (EVT), spectral analysis, topological data analysis, information geometry, and a hyperbolic-geometry umbrella (HyperMeta) -- each computed from one common study-level feature matrix (effect, precision, time, risk-of-bias). Every lens was validated on simulated meta-analyses only. Topology and information geometry are presented as deep-dives of HyperMeta's homology and information-geometry modules, not as independent results. Results: On simulated data: entropy tracked prediction-interval (PI) width better than I-squared (r = 0.91, 95% CI 0.87-0.94, vs 0.73); GEV-tail detection recovered planted outliers at 94% sensitivity (95% CI 90-97; 3% false-positive rate) vs 78% for standardised residuals; spectral decomposition flagged a periodic component in 12% (95% CI 8-17) and trend greater than 30% of heterogeneity in 23%; persistent homology found distinct clusters in 34% (95% CI 28-41); Fisher-information distance detected outliers at 91% sensitivity (95% CI 86-95) vs 82% for Mahalanobis; and hyperbolic embedding preserved hierarchy with 15% lower distortion (95% CI 11-19) than Euclidean MDS. Conclusion: Six lenses, one argument: heterogeneity has a shape, and a scalar collapses it. The case is proof-of-concept -- entirely simulated, each lens bounded by a sample-size or modelling requirement -- and the necessary next step is validation on real meta-analytic corpora.
Three reversals, one lesson: surrogate substitution, confounding, and spurious consistency in the evidence that misled medicine
Background: Clinical practice is periodically built on evidence that proves not merely incomplete but directionally wrong. We ask whether such reversals share a common structure. Approach: We re-examine three landmark evidence reversals — the Cardiac Arrhythmia Suppression Trial (CAST, 1989), the Women's Health Initiative oestrogen-plus-progestin trial (WHI, 2002), and the albumin controversy (Cochrane meta-analysis 1998 versus the SAFE trial, 2004) — recomputing each headline effect from the published counts and reading them as three instances of one phenomenon: truth recovery, in which a definitive randomised trial overturns an established belief. Findings: In each case the prevailing belief was confidently held and wrong in direction. CAST: antiarrhythmics that suppressed the surrogate (ventricular ectopy) increased all-cause death or cardiac arrest, 56/730 versus 22/725, relative risk 2.5 (95% CI 1.6-4.5). WHI: hormone therapy, judged cardioprotective across four decades of observational data (pooled observational RR approximately 0.50), instead raised coronary heart disease (HR 1.29), stroke (1.41) and breast cancer (1.26). Albumin: a pooled relative risk of death of 1.68 (1.26-2.23) across 30 small trials collapsed to 0.99 (0.91-1.09) in the 6,997-patient SAFE trial. The three errors have distinct mechanisms: surrogate substitution, confounding by indication, and spurious consistency among underpowered trials sharing biases. Conclusion: The reversals are not anomalies but a taxonomy. They argue for weighting evidence by its capacity to be wrong — privileging endpoints that matter, designs that break confounding, and adequate power over mere agreement — and for treating consistency in synthesis as something shared bias can manufacture, not a guarantee of truth.
Three Lenses on a Trustworthy Evidence Base: Completeness, Authenticity, and Robustness as a Reproducible Computational Integrity Audit
Background: Trust in the clinical-trial evidence base is usually defended one study at a time, by hand. We ask whether it can instead be audited computationally, at registry and corpus scale, and whether such auditing can be honest about its own blind spots. Methods: We assemble three automated, reproducible lenses on a single programme and apply each to public data: (a) field-level structural missingness across the full ClinicalTrials.gov registry (AACT snapshot, 12 April 2026; 579,828 studies); (b) a Benford leading-digit screen of 1,175,056 numeric values across 403 Cochrane reviews (the Pairwise70 corpus); and (c) the Walsh statistical Fragility Index recomputed from a landmark trial public 2x2 table (DAPA-HF primary composite). Results: Completeness is poor and uneven: 62.9% of records lacked publication links, 48.2% lacked IPD sharing statements, 32.8% lacked a detailed description, and 10.2% lacked locations, with industry the most affected named sponsor class. Authenticity returns an honest null: the corpus first-digit mean absolute deviation (MAD) was 0.013 (95% CI 0.013-0.014), within Nigrini marginal-conformity band, with no corpus-level digit anomaly. Robustness is modest where it matters: a result significant at p = 1.6x10-5 rested on a Fragility Index of 62 events out of 4,744 randomised (Fragility Quotient 1.31%). Conclusion: Completeness, authenticity, and robustness form a layered, deployable integrity audit — but each lens is bounded, and naming those bounds is part of the method, not a footnote to it.
CT.gov Structural Missingness: Field-level information loss across the full ClinicalTrials.gov registry
What information disappears from ClinicalTrials.gov even before one asks whether the results were posted? We analysed the 12 April 2026 full-registry snapshot and quantified structural missingness in publication links, IPD statements, detailed descriptions, locations, and outcome fields. The source universe comprised 579,828 studies, allowing field-level omission rates and sponsor-specific sparsity patterns to be estimated without sampling. Across the full registry, 62.9 percent of records lacked publication links, 48.2 percent lacked IPD sharing statements, 32.8 percent lacked a detailed description, and 10.2 percent lacked locations. Structural sparsity was unevenly distributed: industry records were the most affected overall, NIH had the highest missingness of IPD sharing statements, and records carrying no sponsor class reflected malformed metadata. This missingness extends beyond results reporting into the descriptive fields needed for interpretation, replication, and scrutiny, leaving less context for appraisal, accountability, and public oversight. These metrics capture registry-visible information loss rather than proven intent to conceal.
Benford Screening of 1.2 Million Meta-Analytic Values Finds No Corpus-Level Digit Anomaly
Do the numerical outputs of large-scale meta-analytic workflows follow Benford law, or do they show corpus-level anomalies that might signal data-integrity concerns? We extracted leading digits from 1,175,056 values - point estimates, standard errors, and p-values - across the Pairwise70 corpus of 403 Cochrane review specifications. Digit frequencies were compared with Benford expectations using the Nigrini mean absolute deviation, chi-squared testing, and mantissa-arc uniformity, per field and pooled. The corpus first-digit mean absolute deviation was 0.013 (95% CI 0.013-0.014), within Nigrini marginal-conformity band, although the million-value sample made the omnibus chi-squared and mantissa tests reject uniformity. Effect-estimate and standard-error fields conformed (MAD approximately 0.009), p-values deviated as expected for a bounded quantity, and no review exceeded the critical threshold after Bonferroni correction. At corpus level these Cochrane outputs show no digit anomaly suggesting systematic fabrication or computational distortion in the meaningful effect-size and precision quantities. Benford screening is indirect and cannot detect fabrication that deliberately preserves expected digit frequencies.
TrialDiversityAtlas — Quantifying Demographic Representation Gaps in Global Clinical Trials
Can clinical-trial enrolment be compared with real-world disease burden across demographic groups using only public registry and Global Burden of Disease data? We tagged completed ClinicalTrials.gov trials by MeSH area in the April 2026 AACT snapshot, targeting the cardiovascular and oncology cohorts. TrialDiversityAtlas defines a Representation Disparity Index comparing participant-weighted enrolled proportions, read from posted baseline tables, with expected burden-based prevalence. Enrolled female participation was 49.0% in cardiovascular trials and 56.2% in oncology (pooled 52.1%; 43.6 million participants) — at or above parity — but the expected denominator was uncomputable because the public Global Burden of Disease files are sex- and age-aggregated. The female result spans tens of millions of participants, but the enrolled age breakdown covered few trials, and the over-65 gap had no burden comparator. For these cohorts the data contradict simple female-underrepresentation and show the disparity index is not estimable from standard public exports. Fully stratified burden data and race/ethnicity reporting are needed.
OutcomeSwitchDetector: Automated Detection of Primary Outcome Switching in ClinicalTrials.gov Protocols
Can automated text comparison detect primary-outcome switching between the first and latest registered protocol versions on ClinicalTrials.gov? Using the ClinicalTrials.gov version-history interface, we sampled 1,000 interventional trials with at least two posted protocol versions and compared their first- and latest-version primary outcomes. A diff engine with semantic endpoint parsing matched outcomes by normalized text similarity, classifying primary-outcome changes as additions, removals, substitutions, or rank reversals. A pre-specified primary outcome had changed between the first and latest version in 26.0% of trials (95% CI 23.4 to 28.8), with outright substitution in 15.9% and demotion to secondary in 2.4%. Switching was far commoner among the 21% of trials that posted results (73.2%) than those without (13.5%), and highest in oncology (33.5%). Automated version-history screening therefore flags primary-outcome switching reproducibly and at scale, surfacing candidates for manual adjudication of clinical importance. Detection is limited to ClinicalTrials.gov structured outcome fields and to trials registering more than one protocol version.
Two reporting requirements, divergent compliance: results-posting rises while PCR-correction stalls in 2,039 registered malaria trials
Do malaria trials registered on ClinicalTrials.gov comply with two reporting requirements â the FDAAA results-posting mandate and the WHO PCR-correction declaration in primary outcomes? The AACT 2026-04-12 snapshot of 2,039 registered malaria trials, 2004â2024 by primary completion date, was analyzed. P01 = the fraction posting results within 12 months; P03 = the fraction declaring PCR-correction (strict regex plus ACPR-inclusive broad regex), with Wilson 95% CIs. Compliance was below target: P01 = 5.3% [4.2, 6.6] of 1,407 completed trials; P03 strict = 5.4% [4.3, 6.7] / broad = 9.0% [7.6, 10.7] of 1,302 drug-efficacy trials. Trajectories diverged: FDAAA results-posting rose 2.5% â 10.1% pre/post-2017 (Fisher p<0.001), whereas WHO PCR-correction did not significantly change, 6.8% â 5.1% pre/post-2009 (p=0.43). Malaria-specific FDAAA compliance trails the ~41% cross-disease benchmark ~eightfold; the WHO methodological standard has not moved without enforcement infrastructure. P03 strict is a lower bound; trials registered only in PACTR/CTRI/ISRCTN are outside this corpus.
NCT-bridge methodology is structurally blind to PACTR-registered African trial publications
How well does NCT-bridge methodology, used by every existing ClinicalTrials.gov-style synthesis audit, capture publications from PACTR-registered African trials? We scraped 5,878 PACTR trials (snapshot 2026-05-04, sha256 44f99b14), assigned 768 to ten high-burden conditions, and tracked four gates by algorithm and a blinded 30-trial spot-check. Algorithm and auditor applied identical gate definitions: gate 2 via NCT-bridge to Europe PMC (algorithm) or direct title/PACTR-ID search (auditor), and gate 3 via the Pairwise70 index. The algorithm found 3.6% peer-published and 0.5% Cochrane-cited; the blinded spot-check found 53.3% peer-published (16/30) and one Cochrane match the algorithm missed (WOMAN trial, CDE coverage gap). Algorithm publication-detection sensitivity was 6.2%; 96.0% of trials carry no NCT cross-reference and are structurally unreachable through NCT-bridge. NCT-bridge methodology is blind to PACTR publications even when published, undercounting trial-to-publication conversion by an order of magnitude. The preregistered 30-trial spot-check returned 0/30 verdict-level agreement and the threshold was not lowered — the disagreement is the finding.
Meta-analytic entropy: information-theoretic heterogeneity beyond I-squared
Can information-theoretic metrics detect heterogeneity features that I-squared is blind to, namely multimodality, skewness, and distributional shape? We computed Shannon entropy, Kullback-Leibler divergence, effect-precision mutual information, Fisher information, and a Normalized Entropy Index for 403 Cochrane reviews from Pairwise70. Estimation used Monte Carlo sampling with 10,000 draws per review; the index compared mixture entropy against theoretical bounds, while Silverman kernel density counted modes on precision-weighted distributions. The median Normalized Entropy Index was 0.151 (IQR 0.109–0.229; 95% bootstrap CI 0.143–0.163), and 44.7% of reviews were multimodal despite I-squared below 60%. Because such estimates grow noisier below ten studies, we stratified by k; multimodality rose from 33% (k<10) to 62% (k≥10), a real signal, not a small-sample artefact. Effect-precision mutual information was positive in every computable review and significant by permutation in 21.5%, capturing dependence consistent with, but not proving, small-study mechanisms. The Normalized Entropy Index (0–1) thus captures distributional shape that complements, rather than replaces, variance-based heterogeneity measures.
Copyright (c) 2026 Mahmood Ahmad https://creativecommons.org/licenses/by/4.0/
The Meta-Analysis Ecosystem Model: evidence velocity, effect decay, and a Future-Proof Index across 501 Cochrane reviews
Does the magnitude of early meta-analytic effects systematically decay as evidence accumulates across the Cochrane Library? The Meta-Analysis Ecosystem Model analysed 3,651 trajectories from 501 Cochrane reviews (≥5 studies each) over a median 16-year span; 2,817 of 3,062 computable trajectories gave a finite, in-range decay ratio (834 excluded as undefined or >10). It applies cumulative fixed-effect meta-analysis to binary outcomes, which understates heterogeneity, and integrates velocity, decay, and stability with publication-bias scores into a Future-Proof Index (0–100). Among the 2,817 the median decay ratio was 0.66 (IQR 0.32–1.23), 67.3% shrinking; of 3,062, 25.1% reversed sign but only 3.4% meaningfully. Decay was independent of velocity (r=−0.002) and bias (r=+0.001)—not a scaling artefact—though velocity and bias correlated weakly (r=−0.19). Two-thirds of pooled effects shrank as evidence matured, so early meta-analytic results should not be treated as definitive. The decay ratio is sensitive to the fixed-effect choice, and the Future-Proof Index is an expert-weighted heuristic, not a validated instrument.
Modern random-effects methods change the significance judgement of Cochrane pairwise meta-analyses in 8.2% of cases
Does modernising RevMan's random-effects defaults from DerSimonian–Laird to REML+HKSJ with a prediction interval change the significance judgement of Cochrane pairwise meta-analyses? We re-analysed 6,386 loadable pairwise meta-analyses from 582 Cochrane reviews in the Pairwise70 corpus via the MetaAudit loader. For each meta-analysis we compared DL against REML+HKSJ+PI with a Q*/(k−1) floor and classified three flip tiers (significance, direction, clinically-important). Across the full corpus 8.2% of comparable meta-analyses flipped significance, 0.6% flipped direction, and 3.9% of the MID-available subset exceeded the clinically-important threshold. The effect concentrated in small-k and non-reproducible reviews; non-reproducible meta-analyses were approximately twice as method-sensitive as reproducible ones at every stratum examined. Method choice alone changes the significance judgement in roughly one in twelve Cochrane pairwise reviews, with the largest effect in continuous small-k analyses. Findings apply to aggregate pairwise data only; network meta-analyses, IPD reanalysis, and publication-bias methods are out of scope for v0.1.
ImpossibleMA: Bounded Synthesis of Cochrane Meta-Analyses Declared Unpoolable
Cochrane meta-analyses whose prediction intervals cross the null are conventionally deemed unpoolable, leaving guideline panels with narrative synthesis. Screening 6,229 MetaAudit meta-analyses (68,519 audit rows) for any CRITICAL flag, only prediction_gap fires CRITICAL, so the contribution is the envelope construction, not multi-module triage. The Possibility Envelope—(lower, upper, point|None, min_info, assumptions)—is computed by an adversarial engine enumerating inclusion-rule combinations and reporting REML+HKSJ extremes. 444 of 6,229 (7.13%) carried a CRITICAL flag, all converted to bounded adversarial envelopes, zero unclassified. REML matches metafor::rma below 1e-6 (our house ideal; τ≈1e-7 except the degenerate τ²=0 boundary); monotonicity and full-data containment property-tested across 23 inputs, 182 tests gating every commit. The envelope bounds defensible pooled effects under stated rules, and one named datum—a pre-registered protocol—collapses it to a point; since the bounded set is defined by the same prediction_gap criterion, "unpoolable" is definitional, not external. v1 covers adversarial, k=1, and missing-SE cases; figure-extraction, incommensurable outcomes, and per-MA runs are deferred.
Unmasking heterogeneous treatment effects in clinical trials: a location-scale quantile meta-analysis framework (IPD-QMA)
Background. Meta-analysis of randomized controlled trials is the basis of evidence-based medicine, but it usually aggregates treatment effects as a mean difference under a 'location-shift' assumption — that treatment shifts the outcome distribution uniformly for all patients. In psychiatry, oncology and critical care, interventions can instead create a 'location-scale shift', increasing outcome variance without changing the mean, so standard methods may fail to detect efficacy and discard treatments that benefit high-risk subgroups while harming low-risk ones. Methods. We introduce Individual Participant Data Quantile Meta-Analysis (IPD-QMA), a two-stage framework that uses quantile regression to estimate treatment effects across the severity distribution (τ = 0.1 to 0.9) within studies and pools the coefficients with a random-effects model. We compared IPD-QMA against standard meta-analysis (mean difference) and the log variance ratio (lnVR) using 2,000 Monte Carlo simulations on both Normal and skewed (exponential) data, and developed a 'slope test' (Wald statistic) to test effect homogeneity. Results. In 'hidden-utility' scenarios where treatment increases variance (scale × 3) but not the mean, standard meta-analysis rejected the null at close to the nominal 5% level (4.2% on Normal and 6.4% on skewed data) — effectively no power to detect the effect — whereas IPD-QMA achieved 100% power to detect benefit at the 90th percentile. The lnVR detected heterogeneity with high power but lacked directionality; the IPD-QMA quantile profile revealed that the treatment was harmful for mild cases but beneficial for severe cases. Conclusions. IPD-QMA is a diagnostic tool for precision medicine: whereas lnVR screens for heterogeneity, IPD-QMA characterizes its shape, preventing the loss of potentially life-saving interventions for high-risk populations. An open-source implementation is provided.
Copyright (c) 2026 Laiba Khan https://creativecommons.org/licenses/by/4.0
SeqNMA: sequential network meta-analysis with monitoring boundaries — a methods and software demonstration on a synthetic network
Background: Trial sequential analysis (TSA) applies group-sequential monitoring boundaries to a single cumulative pairwise meta-analysis, controlling type-I error as evidence accrues. Sequential monitoring has been extended to network meta-analysis (NMA) so that a network estimate can be declared conclusive once its cumulative z-score crosses an appended boundary (Nikolakopoulou et al., 2018). SeqNMA is an open, browser-based implementation of that idea, intended as a teaching and prototyping tool. Methods: An in-browser engine rebuilds a contrast-based random-effects NMA at each chronological study addition. For every pairwise comparison it computes a required information size from the heterogeneity-adjusted diversity design effect D\xc2\xb2 (Wetterslev et al., 2009; the diversity, not the cluster, design effect), and monitors the cumulative network z-score against an O'Brien-Fleming alpha-spending efficacy boundary z_k = z_\xce\xb1 / \xe2\x88\x9a(information fraction), Bonferroni-corrected across all T(T\xe2\x88\x921)/2 comparisons, with a non-binding beta-spending futility wedge. The boundary mathematics were independently re-derived and numerically confirmed. Results (illustrative, synthetic): On a deliberately synthetic, reproducibly seeded 12-study network of four treatments, the A-versus-C comparison crosses its monitoring boundary after study 10 (cumulative z = 3.34, log-odds-ratio 0.40), while A-versus-D remains inconclusive at 66% of its required information (z = 0.63). These numbers demonstrate the machinery; they are not an empirical finding about any treatment. Conclusions: Browser-based sequential NMA makes accumulating-evidence monitoring transparent and reproducible. Two limitations are essential: the method ASSUMES but does not TEST network consistency (no design-by-treatment interaction or node-splitting), so every crossing is valid only if direct and indirect evidence agree; and the worked example is synthetic, a software demonstration rather than evidence about real interventions.
Copyright (c) 2026 The Author https://creativecommons.org/licenses/by/4.0/
Auditing the public clinical-trials record: a registry-integrity atlas of registration, reporting and completion gaps
Background. Prospective registration and results reporting underpin trustworthy evidence synthesis, yet registration quality, results-reporting compliance and completion status are themselves rarely audited at scale, and the gaps they leave are inherited silently by downstream meta-analyses. Methods. We audited a full-registry snapshot of ClinicalTrials.gov obtained from the ClinicalTrials.gov API v2 on 29 March 2026 (578,109 records; 441,191 interventional; 290,524 closed interventional), with a primary cohort of 249,507 eligible older closed interventional studies whose primary completion was at least two years before the snapshot. Across twenty-four pre-specified analyses in eight thematic families we computed two-year no-results rates, ghost-protocol rates (missing results plus missing publication links), hiddenness scores, full visibility, black-box stock and study-mix-adjusted excess, broken down by registration fields, trial architecture, reporting era, intervention type, enrollment, stopped-trial status, geography, outcome density, design purpose, completion delay, site footprint, sponsor class, disease family and named sponsor. Results. Among closed interventional studies with primary completion at least two years earlier, 72.7 percent had no posted results, and recent eligible cohorts (2021–2024, 77.0 percent) were no cleaner than the FDAAA 801 era (2008–2016, 67.1 percent), with full visibility at 10.8 percent. Structural fields were strong opacity signals: missing actual enrollment 100.0 percent, one-arm studies 72.8 percent, blank primary purpose 86.4 percent and same-year submission-to-completion 85.7 percent. Opacity concentrated by geography and sponsor — single-site studies 79.5 percent versus 31.7 percent for studies with 20 or more sites; any-U.S. 52.1 percent versus 88.7 percent for no-U.S.; Egypt 95.8 percent among large named countries — and by disease family (cardiovascular 75.0 percent, metabolic 76.2 percent, oncology 67.0 percent). Study-mix-adjusted watchlists ranked France (2,187 excess no-results studies), oncology (543) and Assistance Publique – Hôpitaux de Paris (265) highest. Conclusions. Audited end to end, the public clinical-trials record carries systematic, measurable and structurally patterned gaps that reviewers should screen for before pooling.
Copyright (c) 2026 Andrew Woo https://creativecommons.org/licenses/by/4.0
Grey Relational Meta-Analysis: a robust pooling method with a redescending effect guard
Background. Inverse-variance meta-analysis can be sensitive to extreme effect outliers and high-leverage studies. Existing robust alternatives (M-estimators, heavy-tailed likelihoods, mixture models) address this, but a practical niche remains for methods that combine effect and precision information transparently with nonparametric inference. Methods. We propose Grey Relational Meta-Analysis (GRMA), a robust pooling estimator that uses grey relational similarity in a two-feature space (effect size and log-precision) with an explicit Tukey bisquare effect guard and bootstrap percentile-interval inference. We evaluated GRMA by simulation (2,000 replicates, 25 scenarios, 4 comparators: restricted maximum likelihood [REML], Hartung–Knapp–Sidik–Jonkman [HKSJ], weighted robust dispersion [WRD] and robust Bayesian mixture [RBM]) and on 4,572 real Cochrane meta-analyses (the Pairwise70 benchmark). Results. In simulation, GRMA reduced absolute bias in 4 of 5 outlier scenarios (up to 66% reduction), with root-mean-squared error (RMSE) comparable to HKSJ in outlier scenarios (ratio 1.01) but 8–18% higher under uncontaminated conditions; power was correspondingly lower under standard conditions (59% versus 72% at k = 10, τ² = 0.05). Bootstrap percentile intervals achieved mean coverage of 0.940 (excluding publication-bias stress tests, where all methods suffer). In the Pairwise70 benchmark (4,572 Cochrane meta-analyses) GRMA estimates correlated r = 0.956 with random-effects estimates, increasing to r = 0.980 at k ≥ 10. Conclusions. GRMA is a transparent robust pooling method, proposed as a companion to standard random-effects models rather than a replacement. All code (R and Python) and data are provided in a reproducible capsule under an MIT licence.
Copyright (c) 2026 Niraj Kumar https://creativecommons.org/licenses/by/4.0
Alcohol consumption and cardiovascular disease: a two-sample Mendelian randomization study with multivariable adjustment for smoking and body mass index
Background. Observational studies of the association between alcohol consumption and cardiovascular disease may be confounded by correlated lifestyle factors. We used Mendelian randomization (MR) to assess the causal relationship between alcohol consumption and cardiovascular disease. Methods. We conducted a two-sample MR using 35 genetic variants strongly associated (p < 5×10⁻⁸) with alcohol consumption from the GSCAN consortium, assessing seven cardiovascular outcomes: coronary artery disease (CAD), myocardial infarction (MI), atrial fibrillation (AF) and four stroke subtypes. Inverse-variance-weighted (IVW) MR with MR-Egger, weighted-median and weighted-mode sensitivity analyses was the primary analysis; multivariable MR (MVMR) was adjusted for smoking initiation and body mass index (BMI). Results. The 35 instruments had a mean F-statistic of 76.2 (minimum 29.8), indicating strong instruments. Genetically predicted alcohol consumption showed borderline associations with CAD (OR 1.27, 95% CI 0.97–1.67, p = 0.079) and large-artery stroke (OR 1.61, 95% CI 0.96–2.71, p = 0.071) in univariate MR; CAD sensitivity analyses were significant for the weighted-median (OR 1.48, p < 0.001) and weighted-mode (OR 1.46, p < 0.001) methods. Substantial heterogeneity was detected (CAD: Q = 84.3, p < 0.001), the MR-Egger intercept indicated pleiotropy for MI (p = 0.045), and leave-one-out analysis identified rs1229984 (ADH1B) as highly influential. In MVMR using SNPs available for all three exposures (n = 28 for CAD, n = 30 for large-artery stroke), associations were attenuated for both CAD (OR 1.20, 95% CI 0.82–1.75, p = 0.355) and large-artery stroke (OR 1.26, 95% CI 0.69–2.30, p = 0.464). Conclusions. After accounting for smoking and BMI, we found no evidence for independent causal effects of alcohol consumption on cardiovascular outcomes, despite robust associations in some sensitivity analyses. Observational associations may be explained by confounding from correlated lifestyle factors, consistent with the heterogeneity and pleiotropy seen in univariate analyses and the attenuation in MVMR.
Copyright (c) 2026 Hriday Bablani, Lucia Garcia Perez https://creativecommons.org/licenses/by/4.0
Study overlap across 501 Cochrane reviews: quantifying non-independence for meta-research
Objective. To quantify the degree of primary-study overlap across Cochrane systematic reviews and assess whether non-independence threatens the validity of methodological meta-research using these reviews. Design. Cross-sectional bibliometric analysis. Data source. 501 Cochrane reviews from the Pairwise70 dataset, containing 10,006 unique primary studies. Main outcome measures. Corrected Covered Area (CCA) index, pairwise Jaccard similarity coefficients, and study-frequency distributions across all 125,250 review pairs. Results. Overlap was minimal: CCA was 0.0001 (95% CI 0.00005–0.00018). Only 444 of 10,006 unique studies (4.4%) appeared in more than one review. Of 604 overlapping review pairs, the most overlapping pair shared 43 studies (Jaccard 0.37), while 72% of pairs shared only one study. The median number of reviews per overlapping study was 2 (IQR 2–2). Conclusions. The Pairwise70 Cochrane review collection exhibits negligible study overlap, supporting its use as a source of largely independent meta-analyses for large-scale methodological benchmarking.
Copyright (c) 2026 Mahmood Ahmad https://creativecommons.org/licenses/by/4.0