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Record W3008510792 · doi:10.1186/s12874-020-0899-1

A systematic survey of randomised trials that stopped early for reasons of futility

2020· review· en· W3008510792 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueBMC Medical Research Methodology · 2020
Typereview
Languageen
FieldMathematics
TopicStatistical Methods in Clinical Trials
Canadian institutionsMount Sinai HospitalMcMaster UniversityImpact
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsInterimInterim analysisEarly stoppingSample size determinationClinical trialMedicineData monitoring committeeProtocol (science)Sample (material)Psychological interventionSet (abstract data type)Generalizability theoryResearch designMedical physicsStatisticsAlternative medicineComputer scienceInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Randomised trial protocols may incorporate interim analyses, with the potential to stop the study for futility if early data show insufficient promise of a treatment benefit. Previously, we have shown that this approach will theoretically lead to mis-estimation of the treatment effect. We now wished to ascertain the importance of this phenomenon in practice. METHODS: We reviewed the methods and results in a set of trials that had stopped for futility, identified through an extensive literature search. We recorded clinical areas, interventions, study design, outcomes, trial setting, sponsorship, planned and actual treatment effects, sample sizes; power; and if there was a data safety monitoring board, or a published protocol. We identified: if interim analyses were pre-specified, and how many analyses actually occurred; what pre-specified criteria might define futility; if a futility analysis formed the basis for stopping; who made the decision to stop; and the conditional power of each study, i.e. the probability of statistically significant results if the study were to continue to its complete sample size. RESULTS: We identified 52 eligible trials, covering many clinical areas. Most trials had multiple centres, tested drugs, and 40% were industry sponsored. There were 75% where at least one interim analysis was planned a priori; a majority had only one interim analysis, typically with about half the target total sample size. A majority of trials did not pre-define a stopping rule, and a variety of reasons were given for stopping. Few studies calculated and reported low conditional power to justify the early stop. When conditional power could be calculated, it was typically low, especially under the current trend hypothesis. However, under the original design hypothesis, a few studies had relatively high conditional power. Data collection often continued after the interim analysis. CONCLUSIONS: Although other factors will typically be involved, we conclude that, from the perspective of conditional power, stopping early for futility was probably reasonable in most cases, but documentation of the basis for stopping was often missing or vague. Interpretation of truncated trials would be enhanced by improved reporting of stopping protocols, and of their actual execution.

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.

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)0.745
metaresearch head score (Gemma)0.998
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Meta-epidemiology (broad), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch, Research integrity
DomainCandidate signal: Methods · Consensus signal: Methods
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.652
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.7450.998
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0380.004
Bibliometrics0.0010.002
Science and technology studies0.0000.003
Scholarly communication0.0000.000
Open science0.0030.001
Research integrity0.0030.003
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 head0.990
GPT teacher head0.799
Teacher spread0.191 · 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