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Liu W, Ye S, Barton BA, et al. Simulation-based power and sample size calculation for designing interrupted time series analyses of count outcomes in evaluation of health policy interventions. Contemp Clin Trials Commun. 2019;17:100474doi: 10.1016/j.conctc.2019.100474.
Liu, W., Ye, S., Barton, B. A., Fischer, M. A., Lawrence, C., Rahn, E. J., Danila, M. I., Saag, K. G., Harris, P. A., Lemon, S. C., Allison, J. J., & Zhang, B. (2020). Simulation-based power and sample size calculation for designing interrupted time series analyses of count outcomes in evaluation of health policy interventions. Contemporary clinical trials communications, 17100474. https://doi.org/10.1016/j.conctc.2019.100474
Liu, Wei, et al. "Simulation-based power and sample size calculation for designing interrupted time series analyses of count outcomes in evaluation of health policy interventions." Contemporary clinical trials communications vol. 17 (2020): 100474. doi: https://doi.org/10.1016/j.conctc.2019.100474
Liu W, Ye S, Barton BA, Fischer MA, Lawrence C, Rahn EJ, Danila MI, Saag KG, Harris PA, Lemon SC, Allison JJ, Zhang B. Simulation-based power and sample size calculation for designing interrupted time series analyses of count outcomes in evaluation of health policy interventions. Contemp Clin Trials Commun. 2019 Oct 16;17:100474. doi: 10.1016/j.conctc.2019.100474. eCollection 2020 Mar. PMID: 31886433; PMCID: PMC6920506.
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Contemp Clin Trials Commun. 2019 Oct 16;17:100474. doi: 10.1016/j.conctc.2019.100474. eCollection 2020 Mar.

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

Contemporary clinical trials communications

Wei Liu, Shangyuan Ye, Bruce A Barton, Melissa A Fischer, Colleen Lawrence, Elizabeth J Rahn, Maria I Danila, Kenneth G Saag, Paul A Harris, Stephenie C Lemon, Jeroan J Allison, Bo Zhang

Affiliations

  1. School of Management, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China.
  2. Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, MA, 02115, USA.
  3. Department of Internal Medicine, University of Massachusetts Medical School, Worcester, MA, 01605, USA.
  4. Meyers Primary Care Institute, University of Massachusetts Medical School, Fallon Foundation, and Fallon Community Health Plan, Worcester, MA, 01605, USA.
  5. Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, TN, 37232, USA.
  6. Division of Clinical Immunology and Rheumatology, University of Alabama at Birmingham, Birmingham, AL, 35294, USA.
  7. Department of Biomedical Informatics and Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, 37203, USA.
  8. Department of Neurology and ICCTR Biostatistics and Research Design Center, Boston Children's Hospital and Harvard Medical School, Boston, MA, 02215, USA.

PMID: 31886433 PMCID: PMC6920506 DOI: 10.1016/j.conctc.2019.100474

Abstract

OBJECTIVE: 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.

METHODS: 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.

RESULTS: 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.

CONCLUSION: 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.

© 2019 The Authors.

Keywords: Count outcomes; Interrupted time series; Policy evaluation; Power; Quasi-experimental design; Sample size calculation; Segmented regression

Conflict of interest statement

None.

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