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Record W2889900146 · doi:10.2217/cer-2018-0035

Some issues for the evaluation of noninferiority trials

2018· article· en· W2889900146 on OpenAlexaff
Xuanqian Xie, Myra Wang, Vivian Ng, Nancy Sikich

Bibliographic record

VenueJournal of Comparative Effectiveness Research · 2018
Typearticle
Languageen
FieldMathematics
TopicStatistical Methods in Clinical Trials
Canadian institutionsMcGill University Health Centre
FundersH2020 European Research Council
KeywordsMedicineConfidence intervalMargin (machine learning)Adjuvant therapyPlaceboMedical physicsCancerInternal medicineAlternative medicine

Abstract

fetched live from OpenAlex

Although published noninferiority trials (NITs) generally conclude that the experimental intervention being studied is noninferior compared with standard therapy or active control, NIT quality is often not satisfactory. We have proposed 14 questions to assist in evaluating the clinical evidence of the experimental versus standard therapy. The aim of these questions is to critically appraise NITs and support proper interpretation of study results. Readers should not only consider whether the confidence interval of the primary effect measure falls within the prespecified noninferiority margin (thus concluding noninferiority), but also assess the similarities between primary and secondary outcomes for the experimental and standard therapy. To conclude noninferiority conceptually is to synthesize evidence from both the current NIT comparing experimental therapy with standard therapy and historical data comparing standard therapy with placebo control. Therefore, readers should use external data sources (e.g., historical data) to validate the study design (e.g., selection of standard therapy, effect measure and the noninferiority margin), and assess the uncertainty of findings due to differences between the observed and expected incidence rates, follow-up time, effects of adjuvant therapy and the secondary outcomes of therapies. Following an explanation of the 14 questions, we then apply the questions to a NIT on intraoperative radiation therapy for early stage breast cancer, as an example.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Methods
About the Canadian research system: no · About a Canadian topic: no
Theoretical or conceptuallow
gptMetaresearch
Domain: Methods · Genre: Methods
About the Canadian research system: no · About a Canadian topic: no
Theoretical or conceptuallow
models splitAgreement compares identical category sets and study designs across arms.

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)0.284
metaresearch head score (Gemma)0.409
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.485
Threshold uncertainty score0.737

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.2840.409
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.961
GPT teacher head0.800
Teacher spread0.161 · 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

Labeled directly by 2 models reading the full record.

Metaresearch

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

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

Citations2
Published2018
Admission routes1
Has abstractyes

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