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, 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

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.

Keywords: Bayesian methods; Factorial trial; Hypothermia; Phase III trial; Stopping rules; Trial monitoring.

Figures

Fig. 1
Fig. 1
Probabilities of treatment benefit (log RR) for marginal comparisons of cooling on predischarge mortality. Negative values favor the experimental group. Left panel shows the marginal duration comparison (β2 + β3/2) and the right panel the marginal depth comparison (β1 + β3/2). Top (bottom) panel shows the neutral (enthusiastic) prior and corresponding posterior for the two-factor marginal comparisons
Fig. 2
Fig. 2
Probabilities of treatment benefit (log RR) on predischarge mortality for three experimental cooling groups. Negative values favor the experimental group. Deeper cooling (β1; left panel), longer cooling (β2; middle panel), and both (β1 + β2 + β3; right panel) are compared with standard cooling (33.5 °C for 72 h). Top (bottom) panel shows the neutral (enthusiastic) prior and corresponding posterior probabilities

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