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Bayesian evidence synthesis in case of multi-cohort datasets: An illustration by multi-informant differences in self-control.

ABSTRACT: The trend toward large-scale collaborative studies gives rise to the challenge of combining data from different sources efficiently. Here, we demonstrate how Bayesian evidence synthesis can be used to quantify and compare support for competing hypotheses and to aggregate this support over studies. We applied this method to study the ordering of multi-informant scores on the ASEBA Self Control Scale (ASCS), employing a multi-cohort design with data from four Dutch cohorts. Self-control reports were collected from mothers, fathers, teachers and children themselves. The available set of reporters differed between cohorts, so in each cohort varying components of the overarching hypotheses were evaluated. We found consistent support for the partial hypothesis that parents reported more self-control problems than teachers. Furthermore, the aggregated results indicate most support for the combined hypothesis that children report most problem behaviors, followed by their mothers and fathers, and that teachers report the fewest problems. However, there was considerable inconsistency across cohorts regarding the rank order of children's reports. This article illustrates Bayesian evidence synthesis as a method when some of the cohorts only have data to evaluate a partial hypothesis. With Bayesian evidence synthesis, these cohorts can still contribute to the aggregated results.

SUBMITTER: Kevenaar ST

PROVIDER: S-EPMC7807202 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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Bayesian evidence synthesis in case of multi-cohort datasets: An illustration by multi-informant differences in self-control.

Kevenaar Sofieke T ST Zondervan-Zwijnenburg Maria A J MAJ Blok Elisabet E Schmengler Heiko H Fakkel M Ties MT de Zeeuw Eveline L EL van Bergen Elsje E Onland-Moret N Charlotte NC Peeters Margot M Hillegers Manon H J MHJ Boomsma Dorret I DI Oldehinkel Albertine J AJ

Developmental cognitive neuroscience 20201226

The trend toward large-scale collaborative studies gives rise to the challenge of combining data from different sources efficiently. Here, we demonstrate how Bayesian evidence synthesis can be used to quantify and compare support for competing hypotheses and to aggregate this support over studies. We applied this method to study the ordering of multi-informant scores on the ASEBA Self Control Scale (ASCS), employing a multi-cohort design with data from four Dutch cohorts. Self-control reports we ...[more]

PMID: 33434882

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Project description:BackgroundBayesian adaptive designs can be more efficient than traditional methods for multi-arm randomised controlled trials. The aim of this work was to demonstrate how Bayesian adaptive designs can be constructed for multi-arm phase III clinical trials and assess potential benefits that these designs offer.MethodsWe constructed several alternative Bayesian adaptive designs for the Collaborative Ankle Support Trial (CAST), which was a randomised controlled trial that compared four treatments for severe ankle sprain. These designs incorporated response adaptive randomisation (RAR), arm dropping, and early stopping for efficacy or futility. We studied the operating characteristics of the Bayesian designs via simulation. We then virtually re-executed the trial by implementing the Bayesian adaptive designs using patient data sampled from the CAST study to demonstrate the practical applicability of the designs.ResultsWe constructed five Bayesian adaptive designs, each of which had high power and recruited fewer patients on average than the original designs target sample size. The virtual executions showed that most of the Bayesian designs would have led to trials that declared superiority of one of the interventions over the control. Bayesian adaptive designs with RAR or arm dropping were more likely to allocate patients to better performing arms at each interim analysis. Similar estimates and conclusions were obtained from the Bayesian adaptive designs as from the original trial.ConclusionsUsing CAST as an example, this case study shows how Bayesian adaptive designs can be constructed for phase III multi-arm trials using clinically relevant decision criteria. These designs demonstrated that they can potentially generate earlier results and allocate more patients to better performing arms. We recommend the wider use of Bayesian adaptive approaches in phase III clinical trials.Trial registrationCAST study registration ISRCTN, ISRCTN37807450. Retrospectively registered on 25 April 2003.

Dataset Information

Bayesian evidence synthesis in case of multi-cohort datasets: An illustration by multi-informant differences in self-control.

Publications

Bayesian evidence synthesis in case of multi-cohort datasets: An illustration by multi-informant differences in self-control.

Similar Datasets

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