Overview

This vignette demonstrates how to use the citsr package for conducting Controlled Interrupted Time Series (CITS) analysis. The package provides functions for model fitting using generalized least squares (GLS), visualizing fitted trajectories with confidence intervals, and generating counterfactual predictions for treatment groups.

1. Example Data

The package includes a built-in simulated dataset named df_cits_example.

data("df_cits_example", package = "citsr")
head(df_cits_example)

C:368029e7d2d_examples.R

2. Fit CITS Model

res <- cits(
  data = df_cits_example,
  y_col = "y",
  T_col = "T",
  I_col = "I",
  E_col = "E"
)

summary(res$model)

C:368029e7d2d_examples.R

3. Visualize Fitted Values with 95% Confidence Intervals

plot_fitted <- plot_cits_result(
  res,
  y_col = "y",
  T_col = "T",
  E_col = "E"
)
plot_fitted

C:368029e7d2d_examples.R

4. Visualize Counterfactual Trajectory for Treatment Group

plot_cf <- plot_cits_result_cf(
  res,
  y_col = "y",
  T_col = "T",
  I_col = "I",
  E_col = "E",
  intervention_time = 50
)
plot_cf

C:368029e7d2d_examples.R