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Record W2039658745 · doi:10.1177/1740774510368301

Trends in the application of dynamic allocation methods in multi-arm cancer clinical trials

2010· article· en· W2039658745 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

VenueClinical Trials · 2010
Typearticle
Languageen
FieldMathematics
TopicStatistical Methods in Clinical Trials
Canadian institutionsPrincess Margaret Cancer CentreLondon Health Sciences CentreMcMaster University
Fundersnot available
KeywordsClinical trialMedicineInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Dynamic allocation (DA) methods which attempt to balance baseline prognostic factors between treatment arms, can be used in multi-arm clinical trials to sequentially allocate patients to treatment. Although some experts express concern regarding the validity of inference from trials using DA, others believe DA methods produce more credible results. PURPOSE: A review of published multi-arm cancer clinical trials was conducted to explore the frequency of DA use in oncology. METHODS: Multi-arm phase III clinical trials of at least 100 patients per arm, published in 13 major oncology journals from 1995-2005 were manually reviewed. Information about reported use of DA methods, or randomization via random permuted blocks (PB), was extracted along with trial characteristics. RESULTS: Of 476 published clinical trials, 112 (23.5%) reported using some form of DA method, while 103 (21.6%) reported using PB methods. Most trials (403 or 84.7%) reported stratifying on at least one baseline factor. The mean number of stratification factors was 2.70 per trial, and 78.6% of DA trials reported 3 or more stratification factors compared with 30.2% of non-DA trials (p < 0.001). The frequency of DA use increased over time, with 20.2%, 21.3%, 25.8%, 28.8% and 38.9% of trials reported use in 1995-2001, 2002, 2003, 2004, and 2005, respectively. Use of DA methods was more frequently reported in trials involving an academic co-operative group (28.4% vs. 13.8%), however, no difference was observed between industry-funded and other-funded trials (24.0% vs. 23.2%) or geographical region (19.7% of North American trials, 26.2% of European trials and 21.7% of multinational/other trials). LIMITATIONS: As a retrospective analysis, the true frequency of DA use is likely underreported. Few trials gave complete details of the allocation method used, thus it is possible some manuscripts reported incorrect allocation methods. Journals were selected which were assumed to publish most large, multi-arm clinical trials in cancer from 1995-2005, however, some trials were likely reported in journals other than what was reviewed. CONCLUSIONS: DA methods are frequently used in multi-arm cancer clinical trials. The use of DA appears to becoming more common over time and are used more frequently when an academic cooperative group is involved. No relationship between industry funded trials or geographic region and allocation method was observed. Clinical Trials 2010; 7: 227-234. http://ctj.sagepub.com.

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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)0.594
metaresearch head score (Gemma)0.882
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity
Consensus categoriesMetaresearch, Research integrity
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.953
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.5940.882
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0070.002
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0010.000

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 head0.905
GPT teacher head0.789
Teacher spread0.116 · 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