Analysing interrupted time series with a control

Journal article

Interrupted time series are increasingly being used to evaluate the population-wide implementation of public health interventions. However, the resulting estimates of intervention impact can be severely biased if underlying disease trends are not adequately accounted for. Control series offer a potential solution to this problem, but there is little guidance on how to use them to produce trend-adjusted estimates. To address this lack of guidance, we show how interrupted time series can be analysed when the control and intervention series share confounders, i. e. when they share a common trend. We show that the intervention effect can be estimated by subtracting the control series from the intervention series and analysing the difference using linear regression or, if a log-linear model is assumed, by including the control series as an offset in a Poisson regression with robust standard errors. The methods are illustrated with two examples.

Authors: Bottomley, C; Scott, JAG; Isham, V

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: De Gruyter
• Journal: Epidemiologic Methods
• Volume: 8
• Issue: 1
• Article number: 20180010
• Publication date: 2019-05-29
• Acceptance date: 2019-02-24
• DOI: 10.1515/em-2018-0010
• EISSN: 2194-9263
• ISSN: 2161-962X
• Language: English
• Keywords: common trend model; FFR; segmented regression; interrupted time series