title: Advantages of Bayesian monitoring methods in deciding whether and when to stop a clinical trial: an example of a neonatal cooling trial.
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.
authors.affiliation: Center for Clinical Research and Evidence-Based Medicine, McGovern Medical School at The University of Texas Health Science Center at Houston, 6431 Fannin St, MSB 2.106, Houston, TX, 77030, USA. claudia.pedroza@uth.tmc.edu.
authors.forename: Claudia
authors.identifier: 0000-0003-4235-1282
authors.initials: C
authors.lastname: Pedroza
authors.affiliation: Center for Clinical Research and Evidence-Based Medicine, McGovern Medical School at The University of Texas Health Science Center at Houston, 6431 Fannin St, MSB 2.106, Houston, TX, 77030, USA.
authors.forename: Jon E
authors.initials: JE
authors.lastname: Tyson
authors.affiliation: Social, Statistical and Environmental Sciences Unit, RTI International, 6110 Executive Blvd., Suite 902, Rockville, MD, 20852-3903, USA.
authors.forename: Abhik
authors.initials: A
authors.lastname: Das
authors.affiliation: Department of Pediatrics, Women & Infants Hospital of Rhode Island, The Warren Alpert Medical School of Brown University, 101 Dudley Street, Providence, RI, 02905, USA.
authors.forename: Abbot
authors.initials: A
authors.lastname: Laptook
authors.affiliation: Department of Pediatrics, University of Iowa, 200 Hawkins Drive, Iowa City, IA, 52240, USA.
authors.forename: Edward F
authors.initials: EF
authors.lastname: Bell
authors.affiliation: Department of Pediatrics, Neonatal-Perinatal Medicine, Wayne State University, Children's Hospital of Michigan, 3901 Beaubien Blvd., 4H46, Detroit, MI, 48201, USA.
authors.forename: Seetha
authors.initials: S
authors.lastname: Shankaran
mesh_terms: D000367:Age Factors; D001499:Bayes Theorem; D001833:Body Temperature Regulation; D002985:Clinical Protocols; D057239:Early Termination of Clinical Trials; D017052:Hospital Mortality; D006801:Humans; D007036:Hypothermia, Induced / Q000009:adverse effects / Q000379:methods* / Q000401:mortality; D020925:Hypoxia-Ischemia, Brain / Q000175:diagnosis / Q000401:mortality / Q000503:physiopathology / Q000628:therapy*; D007223:Infant; D007226:Infant Mortality; D007231:Infant, Newborn; D018447:Medical Futility; D012107:Research Design; D018570:Risk Assessment; D012307:Risk Factors; D013997:Time Factors; D016896:Treatment Outcome; D014481:United States
keywords: Bayesian methods; Factorial trial; Hypothermia; Phase III trial; Stopping rules; Trial monitoring