In praise of Prais-Winsten: An evaluation of methods used to account for autocorrelation in interrupted time series.

ABSTRACT: Interrupted time series are increasingly being used to assess the population impact of public health interventions. These data are usually correlated over time (auto correlated) and this must be accounted for in the analysis. Typically, this is done using either the Prais-Winsten method, the Newey-West method, or autoregressive-moving-average (ARMA) modeling. In this paper, we illustrate these methods via a study of pneumococcal vaccine introduction and explore their performance under 20 simulated autocorrelation scenarios with sample sizes ranging between 20 and 300. We show that in terms of mean square error, the Prais-Winsten and ARMA methods perform best, while in terms of coverage the Prais-Winsten method generally performs better than other methods. All three methods are unbiased. As

SUBMITTER: Bottomley C 

PROVIDER: S-EPMC10946734 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature