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Record W3109871844 · doi:10.1093/ehjci/ehaa946.3010

REDUCE-IT: accumulation of data across prespecified interim analyses to final results

2020· article· en· W3109871844 on OpenAlexaff
Brian Olshansky, Deepak L. Bhatt, Michael Miller, Philippe Gabríel Steg, Eliot A. Brinton, Terry A. Jacobson, Steven Ketchum, Ralph T. Doyle, Rebecca A. Juliano, Lixia Jiao, Craig Granowitz, Jean‐Claude Tardif, Cyrus R. Mehta, Christie M. Ballantyne, Mina K. Chung

Bibliographic record

VenueEuropean Heart Journal · 2020
Typearticle
Languageen
FieldMathematics
TopicAdvanced Causal Inference Techniques
Canadian institutionsUniversité de MontréalMontreal Heart Institute
FundersAmarin PharmaAmarin Corporation
KeywordsMedicineClinical endpointInterim analysisMyocardial infarctionInternal medicineRandomized controlled trialPlacebo

Abstract

fetched live from OpenAlex

Abstract Background REDUCE-IT (Reduction of Cardiovascular Events with Icosapent Ethyl-Intervention Trial), an event-driven trial, randomized 8,179 statin-treated patients with elevated triglycerides (TGs) and increased cardiovascular (CV) risk to icosapent ethyl (IPE); pure, stable prescription eicosapentaenoic acid, 4g/day or placebo. 1,612 primary endpoint events (CV death, nonfatal myocardial infarction [MI], nonfatal stroke, coronary revascularization, or hospitalization for unstable angina) projected 90% power to detect 15% relative risk reduction (5% 2-sided alpha). The key secondary composite endpoint was CV death, nonfatal MI, or nonfatal stroke. An independent data and safety monitoring committee (DMC) performed prespecified interim analyses (IAs) at ∼60% (IA1 31 May 2016 data cutoff; 2.9 y median primary endpoint follow-up) and ∼80% (IA2 01 May 2017; 3.7 y) of events; final analysis included 1,606 events (06 Sep 2018; 4.9 y median study follow-up). Purpose Explore REDUCE-IT efficacy and safety across prespecified IAs for insight into progression of robustness and consistency of conclusions. Methods The interim statistical analysis plan guided study continuation decisions by a prespecified decision-making process, including assessment of safety, treatment arm performance, primary composite endpoint formal analyses, and informal robustness analyses, with no futility or efficacy stopping requirements. Prior to DMC IA study continuation decisions, the need for a mature dataset to support the robustness of final efficacy and safety findings was discussed. Sponsor, Steering Committee, and Clinical Endpoint Committee were blinded throughout. Results Primary and key secondary endpoints achieved statistical significance at IA1 and IA2 that persisted at final analyses (p-value below final adjusted 2-sided alpha of 0.0437); hazard ratios also remained consistent and similar robustness was observed across individual endpoint components; clarity of findings across endpoints and subgroups improved with more events. Stopping for overwhelming efficacy was discussed at each IA; prior to IA study continuation recommendations, the DMC considered historical examples of failed CV outcome studies for TG-lowering and mixed omega-3 therapies, reflected on the potential for overestimating final demonstrated benefit using incomplete data, and weighed societal impacts of fuller datasets relative to patient therapy access. Conclusions Consistent, potent efficacy emerged early and persisted across the two prespecified interim and final analyses. The mature dataset demonstrated highly statistically significant reductions in the primary (25%; p=0.00000001) and key secondary (26%; p=0.0000006) endpoints and allowed robust analyses to support overall efficacy and safety conclusions. Allowing the REDUCE-IT dataset to fully mature provided clinicians with robust, consistent, and reliable data upon which to base clinical decisions for IPE in CV risk reduction. Funding Acknowledgement Type of funding source: Other. Main funding source(s): The study was funded by Amarin Pharma, Inc.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.177
metaresearch head score (Gemma)0.288
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.177
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1770.288
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0040.009
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0020.003
Research integrity0.0010.006
Insufficient payload (model declined to judge)0.0120.003

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.939
GPT teacher head0.653
Teacher spread0.286 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2020
Admission routes1
Has abstractyes

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