Identifying Autocorrelation Generated by Various Error Processes in Interrupted Time-Series Regression Designs

Abstract

Regression models used in the analysis of interrupted time-series designs assume statistically independent errors. Four methods of evaluating this assumption are the Durbin-Watson (D-W), Huitema-McKean (H-M), Box-Pierce (B-P), and Ljung-Box (L-B) tests. These tests were compared with respect to Type I error and power under a wide variety of error models and sample sizes. Although the B-P and L-B tests are portmanteau methods that incorporate information from a large portion of the autocorrelation function, the more focused D-W and H-M first-order autoregressive tests are shown to be considerably more powerful. The popular L-B test has unacceptable Type I error and should not be used in the context of the intervention model applied in this study.

Keywords

autocorrelation tests interrupted time-series designs intervention models time-series regression with autoregressive errors

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