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Composite End Points in Clinical Trials of Heart Failure Therapy

2017· article· en· W2575904277 on OpenAlexafffund
Paul M. Brown, Justin A. Ezekowitz

Bibliographic record

VenueCirculation Heart Failure · 2017
Typearticle
Languageen
FieldMathematics
TopicStatistical Methods in Clinical Trials
Canadian institutionsCanadian VIGOUR CentreUniversity of Alberta
FundersServierCanadian Institutes of Health ResearchAlberta InnovatesAlberta Innovates - Health SolutionsAmgen
KeywordsStatisticsComposite indexClinical trialRank (graph theory)Heart failureConsistency (knowledge bases)Mann–Whitney U testMedicineComposite numberIndex (typography)MathematicsPhysical therapyComputer scienceEconometricsComposite indicatorInternal medicineArtificial intelligenceAlgorithm

Abstract

fetched live from OpenAlex

Composite end points are popular outcomes in clinical trials of heart failure therapies. For example, a global rank composite is typically analyzed using a Mann-Whitney U test, and the results are summarized by the mean of ranks and a corresponding P value. The mean of ranks is uninformative, and a clinically meaningful estimate of the treatment effect is needed to communicate study results and facilitate an assessment of heterogeneity (the consistency of the effect across outcomes). The probability index is intuitive for clinicians, easy to calculate, and may be applied to various composites. We suggest a simple and familiar plot to assess heterogeneity across outcomes, which should be routine when analyzing composites. We think that the probability index provides an immediate and simple solution to an overt problem.

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)0.296
metaresearch head score (Gemma)0.568
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.704
Threshold uncertainty score0.868

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2960.568
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.004
Bibliometrics0.0060.006
Science and technology studies0.0010.004
Scholarly communication0.0060.005
Open science0.0020.005
Research integrity0.0040.009
Insufficient payload (model declined to judge)0.0090.003

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 head0.646
GPT teacher head0.607
Teacher spread0.038 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designTheoretical or conceptual
DomainMethods
GenreMethods

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".

Quick stats

Citations19
Published2017
Admission routes2
Has abstractyes

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