Advantages of Bayesian monitoring methods in deciding whether and when to stop a clinical trial: an example of a neonatal cooling trial

Claudia Pedroza; Jon E. Tyson; Abhik Das; Abbot Laptook; Edward F. Bell; Seetha Shankaran; Eunice Kennedy Shriver National Institute of Child Health and Human Development Neonatal Research Network

doi:10.1186/s13063-016-1480-4

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Advantages of Bayesian monitoring methods in deciding whether and when to stop a clinical trial: an example of a neonatal cooling trial

Journal article

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Advantages of Bayesian monitoring methods in deciding whether and when to stop a clinical trial: an example of a neonatal cooling trial

Claudia Pedroza, Jon E. Tyson, Abhik Das, Abbot Laptook, Edward F. Bell, Seetha Shankaran and Eunice Kennedy Shriver National Institute of Child Health and Human Development Neonatal Research Network

Trials, Vol.17(1), pp.335-335

07/22/2016

DOI: 10.1186/s13063-016-1480-4

PMCID: PMC4957277

PMID: 27450203

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Abstract

Background Decisions to stop randomized trials are often based on traditional P value thresholds and are often unconvincing to clinicians. To familiarize clinical investigators with the application and advantages of Bayesian monitoring methods, we illustrate the steps of Bayesian interim analysis using a recent major trial that was stopped based on frequentist analysis of safety and futility. Methods We conducted Bayesian reanalysis of a factorial trial in newborn infants with hypoxic-ischemic encephalopathy that was designed to investigate whether outcomes would be improved by deeper (32 °C) or longer cooling (120 h), as compared with those achieved by standard whole body cooling (33.5 °C for 72 h). Using prior trial data, we developed neutral and enthusiastic prior probabilities for the effect on predischarge mortality, defined stopping guidelines for a clinically meaningful effect, and derived posterior probabilities for predischarge mortality. Results Bayesian relative risk estimates for predischarge mortality were closer to 1.0 than were frequentist estimates. Posterior probabilities suggested increased predischarge mortality (relative risk > 1.0) for the three intervention groups; two crossed the Bayesian futility threshold. Conclusions Bayesian analysis incorporating previous trial results and different pre-existing opinions can help interpret accruing data and facilitate informed stopping decisions that are likely to be meaningful and convincing to clinicians, meta-analysts, and guideline developers.

Bayesian methods

Factorial trial

Hypothermia

Phase III trial

Stopping rules

Trial monitoring

Details

Title: Subtitle: Advantages of Bayesian monitoring methods in deciding whether and when to stop a clinical trial: an example of a neonatal cooling trial
Creators: Claudia Pedroza - The University of Texas Health Science Center at Houston
Jon E. Tyson - The University of Texas Health Science Center at Houston
Abhik Das - RTI International
Abbot Laptook - Brown University
Edward F. Bell - University of Iowa
Seetha Shankaran - Children's Hospital of Michigan
Eunice Kennedy Shriver National Institute of Child Health and Human Development Neonatal Research Network
Contributors: John M Dagle (Contributor) - University of Iowa, Stead Family Department of Pediatrics
Resource Type: Journal article
Publication Details: Trials, Vol.17(1), pp.335-335
DOI: 10.1186/s13063-016-1480-4
PMID: 27450203
PMCID: PMC4957277
NLM abbreviation: Trials
ISSN: 1745-6215
eISSN: 1745-6215
Publisher: BioMed Central
Grant note: ;
Language: English
Date published: 07/22/2016
Academic Unit: Stead Family Department of Pediatrics; Epidemiology; Biochemistry and Molecular Biology; Neonatology
Record Identifier: 9984293075402771

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