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Handling Poor Accrual in Pediatric Trials: A Simulation Study Using a Bayesian Approach

In the conduction of trials, a common situation is related to potential difficulties in recruiting the planned sample size as provided by the study design. A Bayesian analysis of such trials might provide a framework to combine prior evidence with current evidence, and it is an accepted approach by...

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Autores principales: Azzolina, Danila, Lorenzoni, Giulia, Bressan, Silvia, Da Dalt, Liviana, Baldi, Ileana, Gregori, Dario
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7924849/
https://www.ncbi.nlm.nih.gov/pubmed/33669985
http://dx.doi.org/10.3390/ijerph18042095
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author Azzolina, Danila
Lorenzoni, Giulia
Bressan, Silvia
Da Dalt, Liviana
Baldi, Ileana
Gregori, Dario
author_facet Azzolina, Danila
Lorenzoni, Giulia
Bressan, Silvia
Da Dalt, Liviana
Baldi, Ileana
Gregori, Dario
author_sort Azzolina, Danila
collection PubMed
description In the conduction of trials, a common situation is related to potential difficulties in recruiting the planned sample size as provided by the study design. A Bayesian analysis of such trials might provide a framework to combine prior evidence with current evidence, and it is an accepted approach by regulatory agencies. However, especially for small trials, the Bayesian inference may be severely conditioned by the prior choices. The Renal Scarring Urinary Infection (RESCUE) trial, a pediatric trial that was a candidate for early termination due to underrecruitment, served as a motivating example to investigate the effects of the prior choices on small trial inference. The trial outcomes were simulated by assuming 50 scenarios combining different sample sizes and true absolute risk reduction (ARR). The simulated data were analyzed via the Bayesian approach using 0%, 50%, and 100% discounting factors on the beta power prior. An informative inference (0% discounting) on small samples could generate data-insensitive results. Instead, the 50% discounting factor ensured that the probability of confirming the trial outcome was higher than 80%, but only for an ARR higher than 0.17. A suitable option to maintain data relevant to the trial inference is to define a discounting factor based on the prior parameters. Nevertheless, a sensitivity analysis of the prior choices is highly recommended.
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spelling pubmed-79248492021-03-03 Handling Poor Accrual in Pediatric Trials: A Simulation Study Using a Bayesian Approach Azzolina, Danila Lorenzoni, Giulia Bressan, Silvia Da Dalt, Liviana Baldi, Ileana Gregori, Dario Int J Environ Res Public Health Article In the conduction of trials, a common situation is related to potential difficulties in recruiting the planned sample size as provided by the study design. A Bayesian analysis of such trials might provide a framework to combine prior evidence with current evidence, and it is an accepted approach by regulatory agencies. However, especially for small trials, the Bayesian inference may be severely conditioned by the prior choices. The Renal Scarring Urinary Infection (RESCUE) trial, a pediatric trial that was a candidate for early termination due to underrecruitment, served as a motivating example to investigate the effects of the prior choices on small trial inference. The trial outcomes were simulated by assuming 50 scenarios combining different sample sizes and true absolute risk reduction (ARR). The simulated data were analyzed via the Bayesian approach using 0%, 50%, and 100% discounting factors on the beta power prior. An informative inference (0% discounting) on small samples could generate data-insensitive results. Instead, the 50% discounting factor ensured that the probability of confirming the trial outcome was higher than 80%, but only for an ARR higher than 0.17. A suitable option to maintain data relevant to the trial inference is to define a discounting factor based on the prior parameters. Nevertheless, a sensitivity analysis of the prior choices is highly recommended. MDPI 2021-02-21 2021-02 /pmc/articles/PMC7924849/ /pubmed/33669985 http://dx.doi.org/10.3390/ijerph18042095 Text en © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Azzolina, Danila
Lorenzoni, Giulia
Bressan, Silvia
Da Dalt, Liviana
Baldi, Ileana
Gregori, Dario
Handling Poor Accrual in Pediatric Trials: A Simulation Study Using a Bayesian Approach
title Handling Poor Accrual in Pediatric Trials: A Simulation Study Using a Bayesian Approach
title_full Handling Poor Accrual in Pediatric Trials: A Simulation Study Using a Bayesian Approach
title_fullStr Handling Poor Accrual in Pediatric Trials: A Simulation Study Using a Bayesian Approach
title_full_unstemmed Handling Poor Accrual in Pediatric Trials: A Simulation Study Using a Bayesian Approach
title_short Handling Poor Accrual in Pediatric Trials: A Simulation Study Using a Bayesian Approach
title_sort handling poor accrual in pediatric trials: a simulation study using a bayesian approach
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7924849/
https://www.ncbi.nlm.nih.gov/pubmed/33669985
http://dx.doi.org/10.3390/ijerph18042095
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