Statistical considerations for vaccine immunogenicity trials. Part 2: Noninferiority and other statistical approaches to vaccine evaluation

• Published in: Vaccine 2005-02, Vol.23 (13), p.1606-1614
• Main Authors: PLIKAYTIS, Brian D, CARLONE, George M
• Format: Article
• Language: English
• Subjects: Animals; Applied microbiology; Bioassays; Biological and medical sciences; Clinical Trials as Topic - methods; Clinical Trials as Topic - standards; Clinical Trials as Topic - statistics & numerical data; Drug Evaluation - methods; Drug Evaluation - statistics & numerical data; Fundamental and applied biological sciences. Psychology; Humans; Immune response; Immune system; Immunization; Immunogenicity; Medical research; Microbiology; Statistical models; Vaccines; Vaccines - administration & dosage; Vaccines - immunology; Vaccines - standards; Vaccines, antisera, therapeutical immunoglobulins and monoclonal antibodies (general aspects)
• ISSN: 0264-410X; 1873-2518
• DOI: 10.1016/j.vaccine.2004.06.047

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.