Interrupted time series analysis using autoregressive integrated moving average (ARIMA) models: a guide for evaluating large-scale health interventions

Interrupted time series analysis using autoregressive integrated moving average (ARIMA) models: a guide for evaluating large-scale health interventions

Abstract Background Interrupted time series analysis is increasingly used to evaluate the impact of large-scale health interventions. While segmented regression is a common approach, it is not always adequate, especially in the presence of seasonality and autocorrelation. An Autoregressive Integrate...

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Bibliographic Details
Main Authors: Andrea L. Schaffer, Timothy A. Dobbins, Sallie-Anne Pearson
Format: Article
Language:English
Published: BMC 2021-03-01
Series:BMC Medical Research Methodology
Subjects:
Interrupted time series analysis
Autoregressive integrated moving average models
Policy evaluation
Intervention analysis
Online Access:https://doi.org/10.1186/s12874-021-01235-8

Internet

https://doi.org/10.1186/s12874-021-01235-8

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