Bayesian Approach Helps Integrate Adults MS Trial Data to Children, Providing Clarity on Efficacy of Treatments
After failing to show statistical significance in a cohort of children, teriflunomide, the disease-modifying medication observed in the analysis, had compelling evidence to suggest it was efficacious in that age group following the Bayesian approach to analysis.
Maria Pia Sormani, PhD, MS
Given the rarity of multiple sclerosis (MS) in children, new findings suggest that a Bayesian approach may allow clinicians to effectively integrate results from trials evaluating therapeutics in both children and adults when interpreting pediatric trials, and thus overcoming the difficulties in conducting large-scale studies.1
Senior investigator Maria Pia Sormani, PhD, MS, professor of Biostatistics, University of Genoa, and colleagues applied the approach for estimating the plausible effect of teriflunomide (Aubagio; Sanofi), a disease-modifying therapy indicated for patients with relapsing MS. They integrated the results of the TERIKIDS study (NCT02201108), a negative trial of teriflunomide in children, with TEMSO (NCT00134563) and TOWER (NCT00751881), 2 randomized clinical trials that included adult participants.
"In this case, results of our analysis suggest compelling evidence of the efficacy of the drug in children, with the most plausible association an approximately one-third reduction in relapses; the probability that teriflunomide has no treatment effect in children was very low (<3% or even <3/1000),” they concluded.
In the original findings of TERIKIDS, there was no difference in time to first confirmed clinical relapse for those on teriflunomide (n = 109) compared with placebo (n = 57; hazard ratio [HR], 0.66; 95% CI, 0.39-1.11; P = .29).2 The 34% reduction in the incidence of relapses observed in the teriflunomide treatment group failed to achieve statistical significance; therefore, according to standard frequentist approaches, it did not provide evidence of efficacy in children, Sormani et al noted. "However, no compelling biological or clinical reasons indicate that evidence obtained in adults should be ignored when deciding treatment strategies for pediatric MS," they concluded.1
Investigators pooled HRs and 95% CIs on time-to-first relapse from the 3 trials by inverse of variance weighting. The log values were assumed to be normally distributed, and Bayes rule was applied to obtain the distribution of plausible effects of the therapy in children with MS (posterior distribution). Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses reporting guidelines, the prior distributions were downweighted by 50% or 75% to account for differences between adult and pediatric populations.
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Between the TEMSO, TOWER, and TERIKIDS, the previous observed HRs were 0.72 (95% CI, 0.58-0.90), 0.63 (95% CI, 0.50-0.79), and 0.66 (95% CI, 0.39-1.11), respectively. The prior distribution obtained by pooling the results of the 2 trials in adults was centered at an HR of 0.68 (95% CI, 0.58-0.79). After combining the prior distribution and integrating the Bayesian approach weighted at 50%, the HR with TERIKIDS included was 0.67 (95% CI, 0.51-0.87) with a probability of 0.0027 that teriflunomide has no effect in children. With a weight of 25%, the posterior HR estimate was 0.67 (95% CI, 0.44-0.99) and the probability of no effect was 0.0287.
"Two possible criticisms of the use of the Bayesian framework in this setting are (1) the subjectivity involved in the generation of the prior distributions (which in this case were straightforward and derived from a standard meta-analysis) and (2) extrapolation when translating results from adults to children (children are not just small adults)," Sormani et al wrote. "To account for the possibility that results observed in adults cannot be directly applied to children, 2 down-weighting factors were applied (50% and 25%) to limit its influence on the results of the analyses with little effect on conclusions."1
