Finite Sample Properties of Quantile Interrupted Time Series Analysis: A Simulation Study

Interrupted time series (its) analysis represents a powerful quasi-experimental design in which a discontinuity is enforced at a specific intervention point in a time series, and separate regression functions are fitted before and after the intervention point. segmented linear/quantile regression can be used in its designs to isolate intervention eects by estimating the sudden/level change (change in intercept) and/or the gradual change (change in slope). to our knowledge, the finite-sample properties of quantile segmented regression for detecting level and gradual change remains unaddressed. in this study, we compared the performance of segmented quantile regression and segmented linear regression using a monte carlo simulation study where the error distributions were: iid gaussian, heteroscedastic iid gaussian, correlated ar(1), and t (with 1, 2 and 3 degrees of freedom, respectively). we also compared segmented quantile regresison and segmented linear regression when applied to a real dataset, employing an its design to estimate intervention eects on daily-mean patient prescription volumes. both the simulation study and applied example illustrate the usefulness of quantile segmented regression as a complementary statistical methodology for assessing the impacts of interventions in its designs.