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Optimal Three-Treatment Response-Adaptive Designs for Phase III Clinical Trials with Binary Responses

2007· book-chapter· en· W38677103 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueContributions to statistics · 2007
Typebook-chapter
Languageen
FieldDecision Sciences
TopicOptimal Experimental Design Methods
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsOptimal designAdaptive designComputationBinary numberComputer scienceMathematical optimizationClinical trialPhase (matter)AlgorithmMathematicsMedicineMachine learningArithmetic

Abstract

fetched live from OpenAlex

Response-adaptive designs may be used in phase III clinical trials to allocate a larger number of patients to the better treatment. Optimal response-adaptive designs are used for the same purpose, but the design is derived from some optimal points of view. The available optimal response-adaptive designs are only for two treatment trials. In the present paper, we extend this idea and derive some optimal response-adaptive designs for phase III clinical trials for more than two treatments. In particular, we work on three treatments. The extension is not trivial, as the designs for three treatments are often iterative, and they need specific algorithms for computation. The proposed approaches are numerically illustrated.

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.

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)0.051
metaresearch head score (Gemma)0.076
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.689
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0510.076
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.610
GPT teacher head0.618
Teacher spread0.008 · 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