Simulation-based power and sample size calculation for designing interrupted time series analyses of count outcomes in evaluation of health policy interventions

• Published in: Contemporary clinical trials communications 2020-03, Vol.17, p.100474, Article 100474
• Main Authors: Liu, Wei, Ye, Shangyuan, Barton, Bruce A., Fischer, Melissa A., Lawrence, Colleen, Rahn, Elizabeth J., Danila, Maria I., Saag, Kenneth G., Harris, Paul A., Lemon, Stephenie C., Allison, Jeroan J., Zhang, Bo
• Format: Article
• Language: English
• Subjects: Count outcomes; Interrupted time series; Policy evaluation; Power; Quasi-experimental design; Sample size calculation; Segmented regression
• ISSN: 2451-8654; 2451-8654
• DOI: 10.1016/j.conctc.2019.100474

The purpose of this study was to present the design, model, and data analysis of an interrupted time series (ITS) model applied to evaluate the impact of health policy, systems, or environmental interventions using count outcomes. Simulation methods were used to conduct power and sample size calculations for these studies. We proposed the models and analyses of ITS designs for count outcomes using the Strengthening Translational Research in Diverse Enrollment (STRIDE) study as an example. The models we used were observation-driven models, which bundle a lagged term on the conditional mean of the outcome for a time series of count outcomes. A simulation-based approach with ready-to-use computer programs was developed to calculate the sample size and power of two types of ITS models, Poisson and negative binomial, for count outcomes. 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 to detect the same magnitude of parameters varied largely, depending on the testing level change, the trend change, or both. The relationships between power and sample size and the values of the parameters were different between the two models. This article provides a convenient tool to allow investigators to generate sample sizes that will ensure sufficient statistical power when the ITS study design of count outcomes is implemented.