Effect size quantification for interrupted time series analysis: implementation in R and analysis for Covid-19 research.

ABSTRACT:

Background

Interrupted time series (ITS) analysis is a time series regression model that aims to evaluate the effect of an intervention on an outcome of interest. ITS analysis is a quasi-experimental study design instrumental in situations where natural experiments occur, gaining popularity, particularly due to the Covid-19 pandemic. However, challenges, including the lack of a control group, have impeded the quantification of the effect size in ITS. The current paper proposes a method and develops a user-friendly R package to quantify the effect size of an ITS regression model for continuous and count outcomes, with or without seasonal adjustment.

Results

The effect size presented in this work, together with its corresponding 95% confidence interval (CI) and P-value, is bas

SUBMITTER: Travis-Lumer Y 

PROVIDER: S-EPMC9652048 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature