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Correlation between phase 2 clinical trial design and subsequent phase 3 outcome.

2014· article· en· W2599407426 on OpenAlexaff
Annette E. Hay, Jose Gerard Monzon, Gail T. McDonald, Joseph L. Pater, Ralph M. Meyer, Eric Xueyu Chen, Bingshu E. Chen, Janet Dancey

Bibliographic record

VenueJournal of Clinical Oncology · 2014
Typearticle
Languageen
FieldMathematics
TopicStatistical Methods in Clinical Trials
Canadian institutionsPrincess Margaret Cancer CentreJuravinski Cancer CentreQueen's UniversityBaker Hughes (Canada)
Fundersnot available
KeywordsMedicineClinical trialOutcome (game theory)DiscontinuationClinical endpointInternal medicineRandomized controlled trialSurrogate endpoint

Abstract

fetched live from OpenAlex

2549 Background: Randomized phase 2 (RP2) trials have been postulated to better predict phase 3 (P3) outcome than single arm trials. The aim of this study was to determine which characteristics of P2 trials that led to the conduct of P3 trials are associated with a positive P3 outcome. Methods: Randomized P3 trials testing systemic therapy in patients with malignancy, published in English from 2007 to 2012, were identified through a Medline search. Those of superiority design which cited P2 trials contributing to development of the experimental arm were included. Detailed information regarding design and outcome of P2 and P3 trials was extracted in duplicate. Discrepancies were adjudicated by a third investigator. Positivity was defined as statistical difference between arms for primary endpoint in P3. P2 trials reporting that a pre-stated numeric or statistical target had been met were deemed positive. Statistical analysis was performed using the Generalized Estimating Equation model correlating P2 features with P3 outcome, accounting for any P3 duplication. Results: Of 189 eligible P3 trials 19% were in hematologic malignancies and 81% in solid tumors. The primary outcome was positive in 79 (42%). These were supported by 336 P2 trials (range 1 - 9 per P3 trial); 66 of which were RP2s (29 selection, 21 control, 3 discontinuation, 7 phase 2/3 and a combination of these in 6). RP2 trials were not associated with P3 positivity (see Table, p = 0.44). Positive P2 outcome correlated with positive P3 outcome (p = 0.031). P2 trial features found not to be predictive of P3 outcome included primary endpoint, sponsorship, sample size, similarity in patient population and therapy. Conclusions: We did not find RP2 more predictive than single arm trials. When designing P2 trials consideration should be given to the increased resources required to conduct RP2 studies and anticipated gain for a given disease. 336 phase 2 trials 66 randomized phase 2 270 single-arm phase 2 35 positive 31 not positive* 102 positive 168 not positive* P3 positive P3 negative P3 positive P3 negative P3 positive P3 negative P3 positive P3 negative 14 (40%) 21 (60%) 8 (26%) 23 (74%) 50 (49%) 52 (51%) 61 (36%) 107 (64%) * Numeric or statistical target not stated or not met.

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)0.126
metaresearch head score (Gemma)0.666
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesMetaresearch, Research integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.856
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.1260.666
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0050.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.003
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 head0.894
GPT teacher head0.736
Teacher spread0.158 · 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 designOther design
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

Citations0
Published2014
Admission routes1
Has abstractyes

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