Design, analysis, power, and sample size calculation for three‐phase interrupted time series analysis in evaluation of health policy interventions. Issue 3 (19th August 2019)

• Record Type: Journal Article
• Authors: Zhang, Bo; Liu, Wei; Lemon, Stephenie C.; Barton, Bruce A.; Fischer, Melissa A.; Lawrence, Colleen; Rahn, Elizabeth J.; Danila, Maria I.; Saag, Kenneth G.; Harris, Paul A.; Allison, Jeroan J.
• Is Part Of: Journal of evaluation in clinical practice. Volume 26:Issue 3(2020)
• Page Start: 826
• Page End: 841
• Publication Date: 2019-08-19
• Subjects: interrupted time series -- policy evaluation -- power -- quasi‐experimental design -- sample size calculation -- segmented regression; Clinical medicine -- Periodicals; 616.005
• DOI: 10.1111/jep.13266
• Languages: English
• ISSNs: 1356-1294

Abstract

Abstract: Objective: To discuss the study design and data analysis for three‐phase interrupted time series (ITS) studies to evaluate the impact of health policy, systems, or environmental interventions. Simulation methods are used to conduct power and sample size calculation for these studies. Methods: We consider the design and analysis of three‐phase ITS studies using a study funded by National Institutes of Health as an exemplar. The design and analysis of both one‐arm and two‐arm three‐phase ITS studies are introduced. Results: A simulation‐based approach, with ready‐to‐use computer programs, was developed to determine the power for two types of three‐phase ITS studies. Simulations were conducted to estimate the power of segmented autoregressive (AR) error models when autocorrelation ranged from −0.9 to 0.9 with various effect sizes. The power increased as the sample size or the effect size increased. The power to detect the same effect sizes varied largely, depending on testing level change, trend changes, or both. Conclusion: This article provides a convenient tool for investigators to generate sample sizes to ensure sufficient statistical power when three‐phase ITS study design is implemented.