Methodological considerations for interrupted time series analysis in radiation epidemiology: an overview

Abstract

Interrupted time series analysis (ITSA) is a method that can be applied to evaluate health outcomes in populations exposed to ionizing radiation following major radiological events. Using aggregated time series data, ITSA evaluates whether the time trend of a health indicator shows a change associated with the radiological event. That is, ITSA checks whether there is a statistically significant discrepancy between the projection of a pre-event trend and the data empirically observed after the event. Conducting ITSA requires one to consider specific methodological issues due to unique threats to internal validity that make ITSA prone to bias. We here discuss the strengths and limitations of ITSA with respect to bias and confounding, data quality, and statistical aspects. We provide recommendations to strengthen the robustness of ITSA studies and reduce their susceptibility to producing spurious results as a consequence of arbitrary modelling decisions.

Special issues and computational techniques: the Bernard Wheatley Award for 2021

“…The second epidemiologically related paper of interest is that by Wollschläger et al (2021). This addresses the issue of what can legitimately be inferred from an interrupted time-series analysis (ITSA) in which the time trend of a health indicator is studied to determine whether there is a statistically significant discrepancy between the projection of a trend that existed prior to a radiologically relevant event and the empirically observed trend after that event.…”