Determination of Vaccine Efficacy in Anti-Leprosy Vaccination Trial: A Regression Model Analysis

This chapter compares the estimation of vaccine efficacy between the conventional method, logistic model, and Cox regression model in order to improve estimation of vaccine efficacy instead of using conventional methods. It also aims to identify additional influencing factors in evaluating vaccine efficacy among various vaccines. Vaccination plays a vital role in eradicating and controlling the wide spread of diseases like smallpox, polio, measles, tetanus and leprosy throughout the world. In a prospective cohort study 1,71,400 individuals were enrolled and they were confirmed as healthy with routine medical checkups. Various regression models such as Cox regression, binomial regression, Poisson regression model have been applied to find the improvement in the estimation of Vaccine Efficacy. The participants were followed up through two subsequent surveys with two to three year interval. The regression models were built using the individuals' demographic and clinical information. We were able to calculate the odds ratios from the regression models and estimate the adjusted relative risk as well as vaccine effectiveness. Estimated relative risks obtained from Logistic regression and Poisson regression models give similar results in second resurvey. By comparing the estimated vaccine efficacy using conventional methods with regression models the Vaccines ICRC and BCG+Kml have maximum protection and Logistic regression models provides appropriate results than the Cox regression model. Vaccine ICRC and BCG+Kml gives better protection results ranging between 63 and 76 than vaccines BCG and M.w, which have vaccine efficacy between 24 and 35 in second resurvey.