Bayesian inference for vaccine efficacy and prediction of survival probability in prime-boost vaccination regimes.

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This dissertation consists of two major topics on applying Bayesian statistical methods in vaccine development. Chapter two concerns the estimation of vaccine efficacy from validation samples with selection bias. Since there exists a selection bias in the validated group, traditional assumptions about the non-validated group being missing at random do not hold. A selection bias parameter is introduced to handle this problem. Extending the methods of et al. scharfstein (2006), we construct and validate a data generating mechanism that simulates real-world data and allows evaluation of their model. We implement the Bayesian model in JAGS and assess its performance via simulation. Chapter three introduces a two-level Bayesian model which can be used in predicting survival probability from administrated dose concentrations. This research is motivated by the need to use limited information to infer the probability of survival for the next Ebola outbreak under a heterologous prime-boost vaccine regimen. The first level models the relationship between dose and induced antibody count. We use a two-stage response surface to model this relationship. The second level models the association between the antibody count and the probability of survival using a logistic regression. We combine these models to predict survival probability from administrated dosage. We illustrate application of the model with three examples in this chapter and evaluate its performance in Chapter four.

Bayesian modeling. Interaction index. Vaccine studies.