Evidence map›Paper›PMID 41068854›Full record

ArticleTrials2025

A practical guide to simulation for an adaptive trial design with a single interim analysis.

Kaushala S Jayawardana, Michael Dymock, Robert K Mahar, Julie A Marsh, Katherine J Lee

Abstract read
In one paragraph

Article in Trials, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
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0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Kaushala S JayawardanaClinical Epidemiology and Biostatistics Unit, Murdoch Children's Research Institute, Parkville, Victoria, Australia. kaush_07@yahoo.com.ORCID http://orcid.org/0000-0002-7500-9814
Michael DymockWesfarmers Centre of Vaccines and Infectious Diseases, Telethon Kids Institute, Nedlands, WA, Australia.
Robert K MaharClinical Epidemiology and Biostatistics Unit, Murdoch Children's Research Institute, Parkville, Victoria, Australia.
Julie A MarshWesfarmers Centre of Vaccines and Infectious Diseases, Telethon Kids Institute, Nedlands, WA, Australia.
Katherine J LeeClinical Epidemiology and Biostatistics Unit, Murdoch Children's Research Institute, Parkville, Victoria, Australia.

Funding

National Health and Medical Research Council 2017498National Health and Medical Research Council APP1171422
6 · The paper itself

Abstract

backgroundThe demand for adaptive trial designs is growing because of their flexibility and the potential for efficiency gains over traditional fixed designs. Adaptive trials allow planned modifications to the design based on accumulating data. Simulation is imperative in designing adaptive trials because analytical power formulae cannot account for data-driven adaptations. Despite their popularity, the uptake of adaptive trials has been slowed by the lack of expertise and availability of training resources.

methodsIn this tutorial, we demonstrate how to simulate data from a simple adaptive trial with a single interim analysis, summarise the simulations, and use these results to balance the type I error and power to inform the study design and to determine the expected sample size. The simulation code, based on a real trial in hyponatraemia in children, is provided in both R and Stata programming languages. The code is written in modules to improve comprehensibility and enable simple changes to generate a range of adaptive designs. DISCUSSION: When using simulation to design an adaptive trial, the simulations must be tailored to the unique design requirements of the trial at hand. This tutorial provides a foundational framework designed to make the simulation process more accessible to both statisticians and clinicians.

Indexed as

Adaptive Clinical Trials as TopicComputer SimulationResearch DesignChildData Interpretation, StatisticalHumansHyponatremiaSample SizeAdaptive trialsSample sizeSimulationTrial design

Identifiers

PMID41068854
PMCPMC12512321

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LicenceCC BY
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.