Distributed lag interrupted time series model for unclear intervention timing: effect of a statement of emergency during COVID-19 pandemic.

ABSTRACT:

Background

Interrupted time series (ITS) analysis has become a popular design to evaluate the effects of health interventions. However, the most common formulation for ITS, the linear segmented regression, is not always adequate, especially when the timing of the intervention is unclear. In this study, we propose a new model to overcome this limitation.

Methods

We propose a new ITS model, ARIMAITS-DL, that combines (1) the Autoregressive Integrated Moving Average (ARIMA) model and (2) distributed lag functional terms. The ARIMA technique allows us to model autocorrelation, which is frequently observed in time series data, and the decaying cumulative effect of the intervention. By contrast, the distributed lag functional terms represent the idea that the intervention effect d

SUBMITTER: Yoneoka D 

PROVIDER: S-EPMC9310355 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature