This package implements interrupted time series analysis for both continuous and count outcomes, and quantifies the associated effect size, as described in Effect size quantification for interrupted time series analysis: Implementation in R and analysis for Covid-19 research. The main functions fit an ITS regression model, and then use the fitted values and the model-based counterfactual values to quantify the effect size (Cohen’s d for continuous outcomes and relative risk for count outcomes). An example describing how to install and use this package is described below. A more detailed tutorial, including the data analysis described in the paper, is also available with this package (Rmd + pdf file).
You can install the package from its GitHub repository. You first need to install the devtools package.
install.packages("devtools",repos = "http://cran.us.r-project.org")Then install its2es using the install_github function in the
devtools package.
library(devtools)
install_github("Yael-Travis-Lumer/its2es")- Load library and Israel all-cause mortality data (discussed in paper)
library(its2es)
data <- Israel_mortality- Define formula and intervention start index for the Covid-19 period
form <- as.formula("percent ~ time")
intervention_start_ind <- which(data$Year==2020 & data$Month==3)- Fit a linear regression ITS model to the mortality percent
fit <- its_lm(data=data,form=form,time_name = "time",intervention_start_ind=intervention_start_ind, freq=12,seasonality= "full", impact_model = "full",counterfactual = TRUE, print_summary = FALSE)## Cohen's d 2.5% CI 97.5% CI P-value
## 1.038391 0.332192 1.715101 0.002500
- Plot predicted values and counterfactual values
p <- plot_its_lm(data=fit$data,intervention_start_ind=intervention_start_ind, y_lab="All-cause mortality percent", response="percent", date_name= "Date")
p