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Record W2897170297 · doi:10.1186/s13063-018-2887-x

Choosing the target difference and undertaking and reporting the sample size calculation for a randomised controlled trial – the development of the DELTA2 guidance

2018· review· en· W2897170297 on OpenAlexaff
William Sones, Steven A. Julious, Joanne Rothwell, Craig Ramsay, Lisa V. Hampson, Richard Emsley, Stephen J. Walters, Catherine Hewitt, Martin Bland, Dean Fergusson, Jesse A. Berlin, Doug Altman, Luke Vale, Jonathan Cook

Bibliographic record

VenueTrials · 2018
Typereview
Languageen
FieldDecision Sciences
TopicMeta-analysis and systematic reviews
Canadian institutionsOttawa Hospital
FundersProgramme Grants for Applied ResearchMedical Research CouncilChief Scientist Office, Scottish Government Health and Social Care DirectorateNational Institute for Health and Care ResearchScottish GovernmentResearch for Patient Benefit ProgrammeUnited Kingdom Clinical Research Collaboration
KeywordsSample size determinationRandomized controlled trialDelphiSample (material)Delphi methodOutcome (game theory)Statistical powerMedicineMedical physicsResearch designComputer scienceStatisticsArtificial intelligenceSurgeryMathematics

Abstract

fetched live from OpenAlex

A key step in the design of a randomised controlled trial is the estimation of the number of participants needed. The most common approach is to specify a target difference in the primary outcome between the randomised groups and then estimate the corresponding sample size. The sample size is chosen to provide reassurance that the trial will have high statistical power to detect the target difference at the planned statistical significance level. Alternative approaches are also available, though most still require specification of a target difference. The sample size has many implications for the conduct of the study, as well as incurring scientific and ethical aspects. Despite the critical role of the target difference for the primary outcome in the design of a randomised controlled trial (RCT), the manner in which it is determined has received little attention. This article reports the development of the DELTA 2 guidance on the specification and reporting of the target difference for the primary outcome in a sample size calculation for a RCT. The DELTA 2 (Difference ELicitation in TriAls) project has five components comprising systematic literature reviews of recent methodological developments (stage 1) and existing funder guidance (stage 2), a Delphi study (stage 3), a 2-day consensus meeting bringing together researchers, funders and patient representatives (stage 4), and the preparation and dissemination of a guidance document (stage 5). The project started in April 2016. The literature search identified 28 articles of methodological developments relevant to a method for specifying a target difference. A Delphi study involving 69 participants, along with a 2-day consensus meeting were conducted. In addition, further engagement sessions were held at two international conferences. The main guidance text was finalised on April 18, 2018, after revision informed by feedback gathered from stages 2 and 3 and from funder representatives. The DELTA 2 Delphi study identified a number of areas (such as practical recommendations and examples, greater coverage of different trial designs and statistical approaches) of particular interest amongst stakeholders which new guidance was desired to meet. New relevant references were identified by the review. Such findings influenced the scope, drafting and revision of the guidance. While not all suggestions could be accommodated, it is hoped that the process has led to a more useful and practical document.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.636
metaresearch head score (Gemma)0.810
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: Methods
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.364
Threshold uncertainty score0.449

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.6360.810
Meta-epidemiology (narrow)0.0050.007
Meta-epidemiology (broad)0.0090.012
Bibliometrics0.0160.009
Science and technology studies0.0040.015
Scholarly communication0.0160.012
Open science0.0100.014
Research integrity0.0250.030
Insufficient payload (model declined to judge)0.0120.012

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 head0.887
GPT teacher head0.605
Teacher spread0.282 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
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

Citations12
Published2018
Admission routes1
Has abstractyes

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