MétaCan
Menu
Back to cohort
Record W4226051615 · doi:10.1093/jnci/djac067

The Futility of Futility Analyses in Adjuvant Trials in Hormone Receptor–Positive Breast Cancer

2022· article· en· W4226051615 on OpenAlexaff
Ana Elisa Lohmann, Marguerite Ennis, Wendy R. Parulekar, Bingshu E. Chen, George Tomlinson, Pamela J. Goodwin

Bibliographic record

VenueJNCI Journal of the National Cancer Institute · 2022
Typearticle
Languageen
FieldMathematics
TopicStatistical Methods in Clinical Trials
Canadian institutionsUniversity of TorontoUniversity Health NetworkQueen's UniversityStatistics CanadaLunenfeld-Tanenbaum Research InstituteMount Sinai HospitalWestern University
Fundersnot available
KeywordsInterim analysisInterimBreast cancerClinical trialMedicineAdjuvantOncologyCancerHormone receptorInternal medicineIntensive care medicineGynecologyPolitical science

Abstract

fetched live from OpenAlex

An interim analysis is commonly used in phase III superiority trials to compare treatment arms, with the goal of terminating exposure of patients to ineffective or unsafe drugs or to identify highly effective therapies for earlier public disclosure. Traditionally, interim analyses have been designed to identify early evidence of extremely large benefit of the experimental approach, potentially leading to early dissemination of effective treatments. Increasingly, interim analysis has also involved analysis of futility, which may lead to early termination of a trial that will not yield additional useful information. This presents an important challenge in early stage hormone receptor-positive breast cancer, where recurrence often occurs late, with a steady annual event rate up to 20 years. Early analysis of events may miss late treatment effects that can be observed only with longer follow-up. We discuss approaches to futility analysis in adjuvant clinical trials in hormone receptor-positive breast cancer, the role of the Data Safety Monitoring Committee in such analyses, considerations of the potential harms vs benefits of treatment, and the risks of continuing vs early termination of a trial.

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)0.027
metaresearch head score (Gemma)0.077
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.374
Threshold uncertainty score0.942

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0270.077
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.728
GPT teacher head0.637
Teacher spread0.092 · 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 designTheoretical or conceptual
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".

Quick stats

Citations1
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueJNCI Journal of the National Cancer InstituteSame topicStatistical Methods in Clinical TrialsFrench-language works237,207