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

Eunice Kennedy Shriver National Institute of Child Health and Human Development Neonatal Research Network

Research output: Contribution to journalArticle

5 Citations (Scopus)

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. Trial registration: ClinicalTrials.gov NCT01192776. Registered on 31 August 2010.

Original languageEnglish (US)
Article number335
JournalTrials
Volume17
Issue number1
DOIs
StatePublished - Jul 22 2016
Externally publishedYes

Fingerprint

Bayes Theorem
Clinical Trials
Medical Futility
Mortality
Guidelines
Brain Hypoxia-Ischemia
Research Personnel
Newborn Infant
Safety

Keywords

  • Bayesian methods
  • Factorial trial
  • Hypothermia
  • Phase III trial
  • Stopping rules
  • Trial monitoring

ASJC Scopus subject areas

  • Medicine (miscellaneous)
  • Pharmacology (medical)

Cite this

Eunice Kennedy Shriver National Institute of Child Health and Human Development Neonatal Research Network (2016). Advantages of Bayesian monitoring methods in deciding whether and when to stop a clinical trial: An example of a neonatal cooling trial. Trials, 17(1), [335]. https://doi.org/10.1186/s13063-016-1480-4

Advantages of Bayesian monitoring methods in deciding whether and when to stop a clinical trial : An example of a neonatal cooling trial. / Eunice Kennedy Shriver National Institute of Child Health and Human Development Neonatal Research Network.

In: Trials, Vol. 17, No. 1, 335, 22.07.2016.

Research output: Contribution to journalArticle

Eunice Kennedy Shriver National Institute of Child Health and Human Development Neonatal Research Network 2016, 'Advantages of Bayesian monitoring methods in deciding whether and when to stop a clinical trial: An example of a neonatal cooling trial', Trials, vol. 17, no. 1, 335. https://doi.org/10.1186/s13063-016-1480-4
Eunice Kennedy Shriver National Institute of Child Health and Human Development Neonatal Research Network. Advantages of Bayesian monitoring methods in deciding whether and when to stop a clinical trial: An example of a neonatal cooling trial. Trials. 2016 Jul 22;17(1). 335. https://doi.org/10.1186/s13063-016-1480-4
Eunice Kennedy Shriver National Institute of Child Health and Human Development Neonatal Research Network. / Advantages of Bayesian monitoring methods in deciding whether and when to stop a clinical trial : An example of a neonatal cooling trial. In: Trials. 2016 ; Vol. 17, No. 1.
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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. Trial registration: ClinicalTrials.gov NCT01192776. Registered on 31 August 2010.",
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AU - Eunice Kennedy Shriver National Institute of Child Health and Human Development Neonatal Research Network

AU - Pedroza, Claudia

AU - Tyson, Jon E.

AU - Das, Abhik

AU - Laptook, Abbot

AU - Bell, Edward F.

AU - Shankaran, Seetha

AU - Keszler, Martin

AU - Hensman, Angelita M.

AU - Vierira, Elisa

AU - Little, Emilee

AU - Shah, Birju

AU - Guerina, Nicholas

AU - Bliss, Joseph

AU - Mirza, Hussnain

AU - Sommers, Ross

AU - Walsh, Michele C.

AU - Hibbs, Anna Maria

AU - Newman, Nancy S.

AU - Siner, Bonnie S.

AU - Zadell, Arlene

AU - Truog, William E.

AU - Pallotto, Eugenia K.

AU - Kilbride, Howard W.

AU - Gauldin, Cheri

AU - Holmes, Anne

AU - Johnson, Kathy

AU - Schibler, Kurt

AU - Kallapur, Suhas G.

AU - Alexander, Barbara

AU - Fischer, Estelle E.

AU - Gratton, Teresa L.

AU - Grisby, Cathy

AU - Jackson, Lenora

AU - Jennings, Jennifer

AU - Kirker, Kristin

AU - Muthig, Greg

AU - Wuertz, Sandra

AU - Michael Cotten, C.

AU - Goldberg, Ronald N.

AU - Finkle, Joanne

AU - Fisher, Kimberley A.

AU - Grimes, Sandra

AU - Laughon, Matthew M.

AU - Bose, Carl L.

AU - Bernhardt, Janice

AU - Clark, Cindy

AU - Stoll, Barbara J.

AU - Carlton, David P.

AU - Hamrick, Shannon E.G.

AU - Lakshminrusimha, Satyanarayana

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N2 - 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. Trial registration: ClinicalTrials.gov NCT01192776. Registered on 31 August 2010.

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KW - Hypothermia

KW - Phase III trial

KW - Stopping rules

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