A novel representation of vaccine efficacy trial datasets for use in computer simulation of vaccination policy

We will use statistical analysis mainly in two steps of the proposed research: (i) machine-learning of Bayesian Network (BN) representation of vaccine efficacy (VE) trial datasets (ii) comparing the BN representation with the published VEs using agent-based simulation (ABS).Machine-learning of BNs from a VE trial dataset generally consists of three main steps: dataset preparation, learning BNs of the dataset using standard machine-learning algorithms, and evaluating BNs. We will use statistical analysis in the evaluation step to find the best BN representation of the dataset. We will use the best BN representation in the comparison step.1 MACHINE-LEARN BN REPRESENTATIONS1-1 Preparing VE Trial DatasetsWe will prepare each VE trial dataset to be used by machine-learning algorithms that can infer BN representation from the dataset.1-1-1 Handling Missing ValuesSome machine-learning algorithms for learning BNs cannot handle datasets with missing values. In order to use these algorithms, we should replace missing values (e.g., by mean or mode of existing values) or remove the data points that contain missing values.For handling missing values of a VE trial dataset we prefer the same approach taken by the VE trial to be able to machine-learn BN representation of the same dataset from which the VE trial has published VEs.1-1-2 Discretizing Continuous VariablesSome machine-learning algorithms for learning BNs cannot use datasets with continuous variables (e.g., age, vaccination time). We will discretize continuous variables using methods like binning. Binning groups the continuous values into a number of bins. Each bin represents a smaller interval in whole range of the continuous variable.1-2 Learning BNsFor each VE trial dataset we will use a number of standard machine-learning algorithms (e.g., PC, Greedy Thick Thinning, Bayesian Search) to obtain BN representations of the dataset. Each machine-learning algorithm takes a VE trial dataset as input and outputs a BN representation of the dataset.We will verify each BN representation by examining whether we can obtain published VEs from the BN representation of the VE trial dataset. 1-3 Evaluating BNsFor each VE trial dataset we will evaluate the obtained BN representations in order to find the best representation among them. In particular, we will plot the Receiver Operating Characteristic (ROC) curve for each BN and use the Area Under ROC curve (AUROC) as a measure for comparing the performance of BNs. Considering each BN as a binary classifier of an outcome variable such as disease, ROC curve demonstrates the sensitivity of each BN as a function of its specificity for different discrimination thresholds of the outcome variable. The AUROC illustrates how well a BN can distinguish between two values of the outcome variable. We will select the BN with the best AUROC as the BN representation of each VE trial dataset.2 COMPARE THE METHODSFor each VE trial dataset we will compare its selected BN representation with the VEs published by the VE trial. In particular, we will compare the simulated number of infections by ABS of a vaccination policy when using the VEs from the BN representation with the simulated number of infections by ABS of same vaccination policy when using published VEs.A statistically significant difference between the numbers of infections in two ABSs indicates that the result of vaccination-policy analysis can be different when using BN representation of a VE trial dataset instead published VEs.To analyze the difference between the numbers of infections in two ABSs, we will compare the Kaplan-Meier (K-M) curves of the simulations. We will use log-rank test to compare K-M curves with the null hypothesis that there is no difference regarding number of infections between two ABSs.ReferencesBrouwers, L., Mäkilä, K., & Camitz, M. (2006). Spridning av smittkoppor—Simulerings experiment [Spread of smallpox—Simulation experiment]. SMI-Rapport, 5, 2006T.Lee, B. Y., Brown, S. T., Korch, G. W., Cooley, P. C., Zimmerman, R. K., Wheaton, W. D., ... & Burke, D. S. (2010). A computer simulation of vaccine prioritization, allocation, and rationing during the 2009 H1N1 influenza pandemic. Vaccine, 28(31), 4875-4879.