Large sample sizes are needed for sublingual immunotherapy (SLIT) trials because of inherent data variability secondary to inconsistent allergen exposure. Obtaining large sample sizes for pediatric SLIT trials is challenging, but a Bayesian approach using prior adult data can reduce the necessary sample size.

We sought to describe how a Bayesian framework using prior information from adult trials can be used to improve pediatric SLIT clinical development.

Data were compiled by using a frequentist approach (conventional clinical trial approach independent of prior data) from trials conducted during the clinical development of timothy grass SLIT-tablets.

The treatment effect of timothy grass SLIT-tablets was considered similar between pediatric (n = 795) and adult (n = 2299) data pools, with relative total combined symptom plus medication score improvement versus placebo of 21% (95% CI, 11.0% to 30.4%) and 20% (95% CI, 14.6% to 24.4%), respectively. Phleum pratense–specific IgG₄ and IgE-blocking factor increased from baseline in both children and adults treated with timothy grass SLIT-tablets. Given the reasonable assumption in similarity of treatment response between adults and children, a Bayesian approach is described to demonstrate rigorous efficacy criteria for pediatric trials incorporating information from prior adult trials and thereby reduce the sample size.

Data support the similarity of efficacy and immunologic changes between children and adults treated with SLIT for allergic rhinoconjunctivitis. Therefore it is appropriate to use data from adult trials to design feasible trials in children, which might reduce unsafe off-label use by promoting more quickly proper labeling of approved products.