Evidence map›Paper›PMID 42575721›Full record

ArticlePharmaceutical statistics

A Fast, Flexible Simulation Framework for Bayesian Adaptive Designs-The R Package BATSS.

Dominique-Laurent Couturier, Rainer Puhr, Stephane Heritier, Thomas Jaki, Elizabeth G Ryan

Abstract read
In one paragraph

Article in Pharmaceutical statistics. 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
0cells of the map it votes in
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.

Dominique-Laurent CouturierMRC Biostatistics Unit, University of Cambridge, Cambridge, UK.
Rainer PuhrSchool of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia.
Stephane HeritierSchool of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia.
Thomas JakiMRC Biostatistics Unit, University of Cambridge, Cambridge, UK.
Elizabeth G RyanSchool of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia.

Funding

Medical Research Council MC_UU_00040/03
6 · The paper itself

Abstract

The use of Bayesian adaptive designs for randomised controlled trials has been hindered by the lack of software readily available to statisticians. We have developed a new software package (Bayesian Adaptive Trials Simulator Software-BATSS) for the statistical software R, which provides a flexible structure for the fast simulation of Bayesian adaptive designs for clinical trials. We illustrate how the BATSS package can be used to define and evaluate the operating characteristics of Bayesian adaptive designs for various different types of primary outcomes (e.g., those that follow a normal, binary, Poisson, or negative binomial distribution) and can incorporate the most common types of adaptations: stopping treatments (or the entire trial) for efficacy or futility, and Bayesian response adaptive randomisation-based on user-defined adaptation rules. Other important features of this highly modular package include: the use of (Integrated Nested) Laplace approximations to compute posterior distributions, parallel processing on a computer or a cluster, customisability, adjustment for covariates and a wide range of available conditional distributions for the response.

Indexed as

Computer SimulationRandomized Controlled Trials as TopicResearch DesignSoftwareBayes TheoremHumansModels, Statisticaladaptive trialBayesian adaptive trialBayesian response adaptive randomisationclinical trial simulationINLAmulti‐arm multistageR package

Identifiers

PMID42575721
PMCPMC13456979

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Registered trials

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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.