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Record W2887772315 · doi:10.1155/2018/8134132

Bayesian Equivalence Testing and Meta-Analysis in Two-Arm Trials with Binary Data

2018· article· en· W2887772315 on OpenAlexafffund
Cynthia Kpekpena, Saman Muthukumarana

Bibliographic record

VenueComputational and Mathematical Methods in Medicine · 2018
Typearticle
Languageen
FieldMathematics
TopicStatistical Methods in Clinical Trials
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMarkov chain Monte CarloBayesian probabilityEquivalence (formal languages)Posterior probabilityBayesian inferenceStatisticsComputer scienceBinary dataInferenceMarkov chainMathematicsBinary numberArtificial intelligence

Abstract

fetched live from OpenAlex

We consider a Bayesian approach for assessing hypotheses of equivalence in two-arm trials with binary Data. We discuss the development of likelihood, the prior, and the posterior distributions of parameters of interest. We then examine the suitability of a normal approximation to the posterior distribution obtained via a Taylor series expansion. The Bayesian inference is carried out using Markov Chain Monte Carlo (MCMC) methods. We illustrate the methods using actual data arising from two-arm clinical trials on preventing mortality after myocardial infarction.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.044
metaresearch head score (Gemma)0.293
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.296
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0440.293
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.879
GPT teacher head0.688
Teacher spread0.191 · 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; both teacher heads agree on what is shown here.

Study designTheoretical or conceptual
Domainnot available
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

Citations1
Published2018
Admission routes2
Has abstractyes

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