Overview

This repository contains the simulated data and code necessary to run the analyses and produce the figures for the manuscript, "Impact of unequal testing on vaccine effectiveness estimates across two study designs: a simulation study".

Getting Started

The project includes eight R scripts, all of which are located in the "code" folder. It also includes two R Markdown files:

1. MainData_Documentation.Rmd: describes the main variables used to create the manuscript's figures and analyses.

2. TestingBias_simulationDocumentation.Rmd – provides a detailed overview of the agent-based model (ABM) used to generate the simulated data.

The following three R scripts contain the code to run the ABM and create the simulated data for the main analyses:

1. ABM_Main.R: contains the main code to run the ABM and generate simulated datasets. This script calls on both ABM_mainFunctions.R and Aggdf_mainFunctions.R.

2. ABM_mainFunctions.R: contains code for the main ABM functions to generate the transmission network, assign healthcare engagement status, conduct transmission dynamics for SARS-CoV-2, conduct symptomatic SARS-CoV-2 testing, etc.

3. Aggdf_mainFunctions.R: contains code to aggregate individual-level information to population-level data measurements.

The following three R scripts contain the code to run the ABM and create the simulated data for the sensitivity analyses that accounts for time when estimating symptomatic vaccine effectiveness:

1. ABM_App_withTime.R: contains the additional code to run the ABM and generate the simulated datasets that explicitly account for time. This script calls on ABM_mainFunctions.R, IndTestdf_appFunctions.R, and Aggdf_appFunctions_withTime.R.

2. IndTestdf_appFunctions.R: contains code that tracks all simulated individuals who tested at each time step.

3. Aggdf_appFunctions_withTime.R: contains code to sample individuals, run statistical models that account for time, and aggregate individual-level information to population-level data measurements.

The following two R scripts are used to conduct the analyses and generate figures:

4. MainAnalyses_and_Figures.R: contains the code to conduct the main analyses pertaining to objectives 1-3 and to generate figures 3-6.

5. AppendixAnalyses_and_Figures.R: contains the code to conduct the supporting analyses found in the supplementary information and to generate supplementary figures.

All simulated data necessary to recreate the analyses and the figures is located in the "data" folder; copies of all figures and tables can be found in the "figures_and_tables" folder.

Dependencies

• Simulations and analyses were performed using R (R Core Team 2023) (version 4.3.1) with computations performed on the Niagara supercomputer at the SciNet HPC Consortium (Loken et al. 2010; Ponce et al. 2019).
• R packages necessary to recreate the analyses, figures and tables include data.table (Matt Dowle and Arun Srinivasan 2023) (version 1.14.8), dplyr (Wickham et al. 2023) (version 1.1.2), flextable (Gohel et al. 2024) (version 0.9.7), ggborderline (Scott 2022) (version 0.2.0), ggplot2 (Wickham et al. 2016) (version 3.5.1), ggpubr (Kassambara 2023) (version 0.6.0), graph4lg (Savary et al. 2021) (version 1.8.0), igraph (Csardi and Nepusz 2006) (version 1.5.1), officer (Gohel et al. 2025) (version 0.6.8), and survival (Therneau et al. 2023)(version 3.5.5).