A Bayesian approach for the estimation of the mitigated�fraction for ordinal response

Music City Center 

The efficacy of an intervention, such as a vaccine, can be established through the estimation
of several numerical measures. Mitigated fraction is one of the contemporary numerical measures,
and it serves the purpose of reducing the severity of a specific disease rather than completely
preventing its occurrence. In this paper, an efficient approach to calculating the mitigated fraction
is presented, with Bayesian approach which involves utilizing the values of latent variables within a generalized linear mixed model (GLMM). This proposed Bayesian method works with many link functions efficiently compared to traditional frequentist approach. The concept of the mitigated fraction was
introduced in veterinary medicine to quantify the reduction in the severity of disease occurring in
vaccinated animals as compared to non-vaccinated animals. The USDA's Center for Veterinary
Biologics (CVB) recommends a form of the mitigated fraction when
the disease severity is generally graded by some continuous measure or by some discrete assessment
resulting in unambiguous ranks.
Our Bayesian approach works effectively when observations are ordinal and measured longitudinally.

MCMC computation

Ordinal data

link function

Bayesian approach

GLMM 

Section on Bayesian Statistical Science