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. 2021 Mar 22;21(1):58.
doi: 10.1186/s12874-021-01235-8.

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

Andrea L Schaffer  1 , Timothy A Dobbins  2 , Sallie-Anne Pearson  3   4
Affiliations

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

Andrea L Schaffer et al. BMC Med Res Methodol. .

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 Integrated Moving Average (ARIMA) model is an alternative method that can accommodate these issues.

Methods: We describe the underlying theory behind ARIMA models and how they can be used to evaluate population-level interventions, such as the introduction of health policies. We discuss how to select the shape of the impact, the model selection process, transfer functions, checking model fit, and interpretation of findings. We also provide R and SAS code to replicate our results.

Results: We illustrate ARIMA modelling using the example of a policy intervention to reduce inappropriate prescribing. In January 2014, the Australian government eliminated prescription refills for the 25 mg tablet strength of quetiapine, an antipsychotic, to deter its prescribing for non-approved indications. We examine the impact of this policy intervention on dispensing of quetiapine using dispensing claims data.

Conclusions: ARIMA modelling is a useful tool to evaluate the impact of large-scale interventions when other approaches are not suitable, as it can account for underlying trends, autocorrelation and seasonality and allows for flexible modelling of different types of impacts.

Keywords: Autoregressive integrated moving average models; Interrupted time series analysis; Intervention analysis; Policy evaluation.

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Conflict of interest statement

The Centre for Big Data Research in Health, UNSW Sydney has received funding from AbbVie Australia to conduct research unrelated to the present study. AbbVie did not have any knowledge of, or involvement in, the present study. SAP is a member of the Drug Utilisation Sub Committee of the Pharmaceutical Benefits Advisory Committee. The views expressed in this paper do not represent those of the Committee.

Figures

Fig. 1
Fig. 1
Flow chart for ARIMA model selection. Adapted from Hyndman and Athanasopoulos [10].
Fig. 2
Fig. 2
Monthly dispensings of the 25 mg strength quetiapine (A) and the series after first order and seasonal differencing (B)
Fig. 3
Fig. 3
Autocorrelation and partial autocorrelation function (ACF and PACF) plots, prior to differencing (A and B) and after differencing (C and D)
Fig. 4
Fig. 4
Residual check for final model, ARIMA (2,1,0)(0,1,1)12
Fig. 5
Fig. 5
Observed values and predicted values in absence of intervention based on ARIMA model
See this image and copyright information in PMC

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