Evidence map›Paper›PMID 36919403›Full record

ArticleStatistical methods in medical research2023

Using dichotomized survival data to construct a prior distribution for a Bayesian seamless Phase II/III clinical trial.

Benjamin Duputel, Nigel Stallard, François Montestruc, Sarah Zohar, Moreno Ursino

Abstract read
In one paragraph

Article in Statistical methods in medical research, 2023. 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.

Benjamin DuputelUniversitè Paris Citè, Sorbonne Université, Inserm, Centre de Recherche des Cordeliers, Paris, France.ORCID 0000-0002-9044-7433
Nigel StallardWarwick Clinical Trials Unit, Warwick Medical School, University of Warwick, Coventry, UK.
François MontestruceXYSTAT, Malakoff, France.ORCID 0000-0002-7425-6653
Sarah ZoharUniversitè Paris Citè, Sorbonne Université, Inserm, Centre de Recherche des Cordeliers, Paris, France.ORCID 0000-0002-8429-2340
Moreno UrsinoUniversitè Paris Citè, Sorbonne Université, Inserm, Centre de Recherche des Cordeliers, Paris, France.ORCID 0000-0002-5709-4322

Funding

Medical Research Council MR/W021013/1
6 · The paper itself

Abstract

Master protocol designs allow for simultaneous comparison of multiple treatments or disease subgroups. Master protocols can also be designed as seamless studies, in which two or more clinical phases are considered within the same trial. They can be divided into two categories: operationally seamless, in which the two phases are separated into two independent studies, and inferentially seamless, in which the interim analysis is considered an adaptation of the study. Bayesian designs are scarcely studied. Our aim is to propose and compare Bayesian operationally seamless Phase II/III designs using a binary endpoint for the first stage and a time-to-event endpoint for the second stage. At the end of Phase II, arm selection is based on posterior (futility) and predictive (selection) probabilities. The results of the first phase are then incorporated into prior distributions of a time-to-event model. Simulation studies showed that Bayesian operationally seamless designs can approach the inferentially seamless counterpart, allowing for an increasing simulated power with respect to the operationally frequentist design.

Indexed as

Research DesignBayes TheoremClinical Trials, Phase II as TopicClinical Trials, Phase III as TopicComputer SimulationBayesian confirmatory trialsdifferent outcomesoperating characteristicstreatment selection

Identifiers

PMID36919403
PMCPMC10521165

What OpenQuestion holds

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LicenceCC BY-NC
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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.