Model-based standardization using multiple imputation: Code

Antonio Remiro-Azócar, Anna Heath, Gianluca Baio

remiroantonio@gmail.com

2023

This repository contains the R code used for my paper Model-based standardization using multiple imputation, co-authored with Prof. Gianluca Baio and Prof. Anna Heath.

Utilizing the Scripts

In order to use this repository, the user must first download a copy to their local machine. The user must set the working directory to the location where the download was made, and open and run the main.R script. This script specifies the settings of the simulation study, generates the data, performs the standardization methods (saving the point estimates, variances and interval estimates to the "./Results/" subdirectory), computes the relevant performance metrics, and graphs the results of the simulation study. The simulation_functions.R script contains user-defined functions, adapted from Phillippo et al. (2020), to generate the simulation study data. The performance_functions.R script contains user-defined functions to evaluate the performance measures of interest. Run the appendix.R script to reproduce the simulation study in Additional file 1 (Supplementary Appendix).

The doSNOW package is used to parallelize the performance of the methods in the simulation study, distributing the tasks to different cores of the computer.

The code was prepared in RStudio using R version 4.1.1 in a Windows architecture, with a 64-bit operating system. The following packages and versions were used:

• boot 1.3.28 for the non-parametric bootstrap in standard parametric model-based standardization (G-computation)
• copula 1.0.1 to simulate covariates from a multivariate Gaussian copula when simulating the data
• doSNOW 1.0.19 used in combination with foreach() to start up local clusters that distribute parallel tasks to different cores
• ggplot2 3.3.5 to plot the simulation study results (Figure 2 in the article)
• ggridges 0.5.3 to plot the simulation study results (Figure 2 in the article)
• gridExtra 2.3 to plot the simulation study results (Figure 2 in the article)
• parallel 4.1.1 to detect the number of CPU cores
• rstanarm 2.21.1 for the synthesis stage (fitting the first-stage outcome regression and drawing outcomes from the posterior predictive distribution) in multiple imputation marginalization