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Record W4327862509 · doi:10.1017/9781108917919

Introduction to Adaptive Trial Designs and Master Protocols

2023· book· en· W4327862509 on OpenAlexaff
Jay Park, Edward J. Mills, J. Kyle Wathen

Bibliographic record

VenueCambridge University Press eBooks · 2023
Typebook
Languageen
FieldMathematics
TopicStatistical Methods in Clinical Trials
Canadian institutionsMcMaster University
Fundersnot available
KeywordsClinical trialAdaptive designProtocol (science)Resource (disambiguation)Computer scienceClinical study designClinical PracticeMedical researchResearch designMedical physicsAlternative medicineEngineering ethicsMedicineEngineeringNursingSociologyPathologySocial science

Abstract

fetched live from OpenAlex

This practical high-level guidebook offers an in-depth understanding of the newly emerging clinical trial designs in adaptive trial designs and master protocols. Both concise and readable without shying away from technical discussion, the book introduces the most innovative approaches in clinical trial research such as adaptive trial designs, master protocols, platform trial, basket trial, and umbrella trial designs. Featuring a revisionist history of clinical research before moving on to case-study based discussion and practical considerations from collective experience. The book enables readers to understand the strengths and limitations of these novel designs as well as their application to individual areas of research and clinical practice. Supplemented by real-world examples from the recent developments in medical research efficiency instigated by both personalized medicine and high-profile diseases like COVID-19 and cancer. The first book of its kind, it is the go-to resource for medical students and researchers working in clinical trial research.

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)0.001
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.491
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.574
GPT teacher head0.456
Teacher spread0.118 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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

Citations6
Published2023
Admission routes1
Has abstractyes

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