Statistical Considerations for NonInferiority/Equivalence Trials in Vaccine Development

Abstract

Noninferiority/equivalence designs are often used in vaccine clinical trials. The goal of these designs is to demonstrate that a new vaccine, or new formulation or regimen of an existing vaccine, is similar in terms of effectiveness to the existing vaccine, while offering such advantages as easier manufacturing, easier administration, lower cost, or improved safety profile. These noninferiority/equivalence designs are particularly useful in four common types of immunogenicity trials: vaccine bridging trials, combination vaccine trials, vaccine concomitant use trials, and vaccine consistency lot trials. In this paper, we give an overview of the key statistical issues and recent developments for noninferiority/equivalence vaccine trials. Specifically, we cover the following topics: (i) selection of study endpoints; (ii) formulation of the null and alternative hypotheses; (iii) determination of the noninferiority/equivalence margin; (iv) selection of efficient statistical methods for the statistical analysis of noninferiority/equivalence vaccine trials, with particular emphasis on adjustment for stratification factors and missing pre- or post-vaccination data; and (v) the calculation of sample size and power.

Key Words:

• Equivalence
• Minimum risk weights
• Missing data
• Noninferiority
• Stratified analysis
• Vaccine clinical trial

ACKNOWLEDGMENTS

We thank the referees for their thoughtful comments that led to an improved manuscript.

Notes

(f ₁ = .3, f ₂ = .7 ); 10000 simulations;

CMH_OBS = CMH weights with observed variance;

CMH_MN = CMH weights with null variance (MN Method);

MR_OBS = Minimum Risk weights with observed variance