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Effect size quantification for interrupted time series analysis: implementation in R and analysis for Covid-19
Yael Travis-Lumer1, Yair Goldberg2, Stephen Z Levine3
1Faculty of Industrial Engineering and Management, Israel Institute of Technology, 3200003, Haifa, Israel. travis-lumer@campus.technion.ac.il.
Emerging Themes in Epidemiology
|November 11, 2022
Summary
Interrupted time series (ITS) analysis now quantifies intervention effect size. A new R package estimates this measure, demonstrating increased mortality during the Covid-19 pandemic.
Area of Science:
- Epidemiology
- Biostatistics
- Public Health
Background:
- Interrupted time series (ITS) analysis is a valuable quasi-experimental design for evaluating interventions, particularly in natural experiments like the Covid-19 pandemic.
- Quantifying the effect size in ITS has been challenging due to the absence of a control group.
- This study addresses the need for a standardized method to measure intervention impact in ITS.
Purpose of the Study:
- To propose a novel method for quantifying the effect size in interrupted time series (ITS) regression models.
- To develop a user-friendly R package for estimating ITS effect sizes for continuous and count outcomes, with or without seasonal adjustments.
- To demonstrate the application of the developed method and package using real-world data.
Main Methods:
- Developed a method to quantify ITS effect size using model-based fitted and counterfactual predicted values.
- Created an R package to facilitate the fitting of ITS models and estimation of effect sizes.
- Applied the method to a nationwide population-based study on all-cause mortality in Israel from 2001-2021 to assess Covid-19's impact.
Main Results:
- The developed R package enables the estimation of effect size, 95% confidence intervals, and P-values for ITS models.
- Analysis of Israeli mortality data revealed an increase in all-cause mortality associated with Covid-19 exposure (relative risk = 1.11, 95% CI = 1.04-1.18, P < 0.001).
- The counterfactual prediction indicated that mortality rates were expected to decrease without the pandemic.
Conclusions:
- This work presents the first quantification of effect size in interrupted time series (ITS) analysis.
- The user-friendly R package allows researchers and policymakers to easily estimate ITS effect sizes.
- The findings highlight a significant increase in mortality linked to the Covid-19 pandemic, underscoring the utility of ITS effect size for public health policy.
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