Comparison of six statistical methods for interrupted time series studies: empirical evaluation of 190 published series.

ABSTRACT:

Background

The Interrupted Time Series (ITS) is a quasi-experimental design commonly used in public health to evaluate the impact of interventions or exposures. Multiple statistical methods are available to analyse data from ITS studies, but no empirical investigation has examined how the different methods compare when applied to real-world datasets.

Methods

A random sample of 200 ITS studies identified in a previous methods review were included. Time series data from each of these studies was sought. Each dataset was re-analysed using six statistical methods. Point and confidence interval estimates for level and slope changes, standard errors, p-values and estimates of autocorrelation were compared between methods.

Results

From the 200 ITS studies, including 230 time

SUBMITTER: Turner SL 

PROVIDER: S-EPMC8235830 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature