A new mixture-based fixed-effect model for a biometrical case-study related to immunogenecity with highly censored data

TL;DR

This paper introduces a novel mixture regression model combining Birnbaum-Saunders and Bernoulli distributions to effectively analyze highly censored biometry data, demonstrated through a measles vaccine case-study in Haiti.

Contribution

It presents a new continuous-discrete mixture model tailored for highly censored data, incorporating specific distributions and maximum likelihood estimation, with validation via simulations and real data.

Findings

Model accurately captures censored antibody data

Simulation results confirm good performance

Application to vaccine data demonstrates practical utility

Abstract

We propose a new continuous-discrete mixture regression model which is useful for describing highly censored data. We motivate our investigation based on a case-study in biometry related to measles vaccines in Haiti. In this case-study, the neutralization antibody level is explained by the type of vaccine used, level of the dosage and gender of the patient. This mixture model allows us to account for excess of censored observations and consists of the Birnbaum-Saunders and Bernoulli distributions. These distributions describe the antibody level and the point mass of the censoring observations. We estimate the model parameters with the maximum likelihood method. Numerical evaluation of the model is performed by Monte Carlo simulations and by an illustration with biometrical data, both of which show its good performance and its potential applications.