The performance of interrupted time series designs with a limited number of time points: Learning losses due to school closures during the COVID-19 pandemic.

ABSTRACT: Interrupted time series (ITS) designs are increasingly used for estimating the effect of shocks in natural experiments. Currently, ITS designs are often used in scenarios with many time points and simple data structures. This research investigates the performance of ITS designs when the number of time points is limited and with complex data structures. Using a Monte Carlo simulation study, we empirically derive the performance-in terms of power, bias and precision- of the ITS design. Scenarios are considered with multiple interventions, a low number of time points and different effect sizes based on a motivating example of the learning loss due to COVID school closures. The results of the simulation study show the power of the step change depends mostly on the sample size, while the power

SUBMITTER: van Leeuwen FD 

PROVIDER: S-EPMC11305537 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature