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Bayesian Analysis: Using Prior Information to Interpret the Results of Clinical Trials

In this issue of JAMA, Laptook et al1 report the results of a clinical trial investigating the effect of hypothermia administered between 6 and 24 hours after birth on death and disability from hypoxicischemic encephalopathy (HIE). Hypothermia is beneficial for HIE when initiated within 6 hours of birth but administering hypothermia that soon after birth is impractical.2 The study by Laptook et al1 addressed the utility of inducing hypothermia 6 or more hours after birth because this is a more realistic time window given the logistics of providing this therapy. Performing this study was difficult because of the limited number of infants expected to be enrolled. To overcome this limitation, the investigators used a Bayesian analysis of the treatment effect to ensure that a clinically useful result would be obtained even if traditional approaches for defining statistical significance were impractical. The Bayesian approach allows for the integration or updating of prior information with newly obtained data to yield a final quantitative summary of the information. Laptook et al1 considered several options for the representation of prior information—termed neutral, skeptical, and optimistic priors—generating different final summaries of the evidence.