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This article develops new closed-form variance expressions for power analysesfor commonly used difference-in-differences (DID) and comparative interruptedtime series (CITS) panel data estimators. The main contribution is toincorporate variation in treatment timing into the analysis. The power formulasalso account for other key design features that arise in practice:autocorrelated errors, unequal measurement intervals, and clustering due to theunit of treatment assignment. We consider power formulas for bothcross-sectional and longitudinal models and allow for covariates. Anillustrative power analysis provides guidance on appropriate sample sizes. Thekey finding is that accounting for treatment timing increases required samplesizes. Further, DID estimators have considerably more power than standard CITSand ITS estimators. An available Shiny R dashboard performs the sample sizecalculations for the considered estimators.

Statistical Power for Estimating Treatment Effects Using  Difference-in-Differences and Comparative Interrupted Time Series Designs  with Variation in Treatment Timing