A mixture model to assess the the immunogenicity of an oral rotavirus vaccine among healthy infants in Niger

Abstract

Analysis of immunogenicity data is a critical component of vaccine development, providing a biological basis to support any observed protection from vaccination. Conventional methods for analyzing immunogenicity data use either post-vaccination titer or change in titer, often defined as a binary variable using a threshold. These methods are simple to implement but can be limited especially in populations experiencing natural exposure to the pathogen. A mixture model can overcome the limitations of the conventional approaches by jointly modeling the probability of an immune response and the level of the immune marker among those who respond. We apply a mixture model to analyze the immunogenicity of an oral, pentavalent rotavirus vaccine in a cohort of children enrolled into a placebo-controlled vaccine efficacy trial in Niger. Among children with undetectable immunoglobulin A (IgA) at baseline, vaccinated children had 5.2-fold (95% credible interval (CrI) 3.7, 8.3) higher odds of having an IgA response than placebo children, but the mean log IgA among vaccinated responders was 0.9-log lower (95% CrI 0.6, 1.3) than among placebo responders. This result implies that the IgA response generated by vaccination is weaker than that generated by natural infection. Multivariate logistic regression of seroconversion defined by ≥ 3-fold rise in IgA similarly found increased seroconversion among vaccinated children, but could not demonstrate lower IgA among those who seroresponded. In addition, we found that the vaccine was less immunogenic among children with detectable IgA pre-vaccination, and that pre-vaccination infant serum IgG and mother's breast milk IgA modified the vaccine immunogenicity. Increased maternal antibodies were associated with weaker IgA response in placebo and vaccinated children, with the association being stronger among vaccinated children. The mixture model is a powerful and flexible method for analyzing immunogenicity data and identifying modifiers of vaccine response and independent predictors of immune response.

Keywords: Immunogenicity; Rotavirus; Statistical model.