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Updated: Oct 18, 2025

A Component-resolved Diagnostic Approach for a Study on Grass Pollen Allergens in Chinese Southerners with Allergic Rhinitis and/or Asthma
Published on: June 4, 2017
A three-stage design for allergen immunotherapy trials
Xinyu Tang1, Ronald L Rabin2, Lihan K Yan1
1Office of Biostatistics and Epidemiology, Center for Biologics Evaluation and Research (CBER), U.S. Food and Drug Administration (FDA), Silver Spring, Maryland, USA.
Background:
Clinical trials of allergen immunotherapy (AIT) may require up to 5 years to complete. These lengthy trials may be complicated by high and potentially differential dropouts, especially among participants who perceive that they are receiving placebo. We propose a three-stage design in which the placebo group in Stage 1 crosses over to receive active treatment in Stage 2. In Stage 3, AIT is discontinued to determine whether benefit is maintained post-treatment. We apply inferential statistics to support the three-stage design for clinical trials to determine clinical efficacy, treatment response over time, and sustained response to AIT.
Methods:
The proposed framework constitutes a series of hypothesis tests for comparing treatment responses at the end of each stage. A simulation study was performed to illustrate the statistical properties under varying statistical missing mechanisms and effect sizes.
Results:
The statistical properties in terms of bias and statistical power were consistent with what are expected from conventional analyses. Specifically, the extent of bias depended on the missing mechanism and magnitude. The statistical powers were largely driven by effect and sample sizes as well as prespecified success margins. As an illustration, assuming relative treatment differences of 25% and stagewise dropout rate of 15%, a sample size of 200 per group may achieve 93% power to demonstrate a treatment effect and 60% power to demonstrate a maintained response post-treatment.
Conclusions:
Inferential statistics support our proposed study design for evaluating benefits of AIT over time and inform clinical understanding and decisions.
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