Related Experiment Video
Updated: Aug 19, 2026

A Clinical Trial Assessing the Safety, Efficacy, and Delivery of Olive-Oil-Based Three-Chamber Bags for Parenteral Nutrition
Published on: September 20, 2019
Statistical considerations for vaccine immunogenicity trials. Part 2: Noninferiority and other statistical approaches
Brian D Plikaytis1, George M Carlone
1Biostatistics and Information Management Branch, Division of Bacterial and Mycotic Diseases, National Center for Infectious Diseases, Centers for Disease Control and Prevention, Mailstop C09, Atlanta, GA 30333, USA. brian.plikaytis@cdc.hhs.gov
Abstract:
Part 2 of this series investigates the statistical considerations of vaccine evaluation in an active-control trial. In particular, the strengths and weaknesses of the noninferiority methodology will be explored and contrasted for T-cell independent (does not elicit a memory response) and T-cell dependent (elicits a memory response) vaccines. At present, the noninferiority model is widely accepted as the primary tool for comparing the immunogenicity of a new or reformulated vaccine to an already existing licensed product. However, conclusions drawn from statistical hypothesis testing are dependent on the bioassay endpoint (e.g., antibody concentration) and the metric analyzed (e.g., geometric mean concentration, proportion fold-response, etc.). Competing vaccines may be highly immunogenic and still be judged inferior to licensed vaccines. T-cell dependent vaccines introduce new issues into the evaluation process regarding the analysis of short- and long-term immune response. Also, the kinetics of vaccine response is increasingly being recognized as an important variable in quantifying peak antibody levels after an immunization. This report will also illustrate a method for using multiple immunogenicity endpoints to measure vaccine effectiveness and protection through the use of statistical models and indicate the strengths and weaknesses of using these techniques.
Related Concept Videos
Bioequivalence Data: Statistical Interpretation
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast, controlled...
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance, comparing...
Clinical Trials
There are four phases in a clinical trial. A phase one...
Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs
Statistical Methods to Analyze Parametric Data: Student t-Test and Goodness-of-Fit Test
The Student's t-test is a statistical test that examines if there is a statistically significant difference between the means of two groups. This test is instrumental when dealing with data...

