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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

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Title
Advantages of Bayesian monitoring methods in deciding whether and when to stop a clinical trial: an example of a neonatal cooling trial
Published in
Trials, July 2016
DOI 10.1186/s13063-016-1480-4
Pubmed ID
Authors

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

Abstract

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. 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. 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. 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. ClinicalTrials.gov NCT01192776 . Registered on 31 August 2010.

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Geographical breakdown

Country Count As %
Unknown 90 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 14 16%
Other 11 12%
Student > Ph. D. Student 10 11%
Professor 9 10%
Student > Master 8 9%
Other 14 16%
Unknown 24 27%
Readers by discipline Count As %
Medicine and Dentistry 32 36%
Nursing and Health Professions 9 10%
Agricultural and Biological Sciences 4 4%
Pharmacology, Toxicology and Pharmaceutical Science 3 3%
Mathematics 3 3%
Other 10 11%
Unknown 29 32%