Statistical Implications for Estimation of Genetic Traits in Human Vaccine Trials

The literature survey on genetic screening showed that people who had specific genotypes may be free from infection (CDC), e.g., malaria, human papillomavirus, Haemophilus influenzae and for which vaccines are either already developed or are being targeted. In such situation, the evaluation of the efficacy of vaccine at the community level needs to be examined with prior precaution. In this chapter, we have formulated various analytical methods for the estimation of vaccine efficacy (VE) in the presence of genetic traits or components of unknown parameter “θ” and the sample size determination. The present model demonstrated that the real data on vaccine trials and estimated unknown parameter and variance at 95% confidence interval besides relative width for the projection of vaccine efficacy in various human clinical trials. The study considering true efficacy ranges from 40 to 80% and the possible values of the genetic component “θ” ranges from 0 to 60%; the genetic variance σ2 was estimated at greatest accuracy. 95% confidence intervals (CI) on estimated σ2 with relative widths 1.0 and 0.1 were computed by single iteration method. The sample size is very much required on each of the unvaccinated and vaccinated cohorts. The parameter estimations were computed from our fitted model. The model results showed that the estimated incidence rate was found to be significantly correlated when compared to unvaccinated (Iu) cohort. In the presence of genetic traits, we found that the variance was consistently overestimated (R2 = 30%, low specificity). The resulted findings showed that the change in the location is not obtained prior information (probability) from the study sites; the parameter estimations were showed asymmetrical distribution at 95% CI (in case of point estimates). The sample size is very much required for estimating variance components resulted to improve vaccine trials and multiplicity of scientific ethos on human vaccine trials. The present study concludes that the estimated parameters were found to substantially reduce probable errors (PE) and also unknown genetic component “θ” restoring good sample size.