Bo Zhang1, Wei Liu2, Stephenie C Lemon1, Bruce A Barton1, Melissa A Fischer3,4, Colleen Lawrence5, Elizabeth J Rahn6, Maria I Danila6, Kenneth G Saag6, Paul A Harris7, Jeroan J Allison1. 1. Department of Population and Quantitative Health Sciences, University of Massachusetts Medical School, Worcester, Massachusetts. 2. School of Management, Harbin Institute of Technology, Harbin, Heilongjiang, China. 3. Department of Internal Medicine, University of Massachusetts Medical School, Worcester, Massachusetts. 4. Meyers Primary Care Institute, University of Massachusetts Medical School, Fallon Foundation, and Fallon Community Health Plan, Worcester, Massachusetts. 5. Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee. 6. Division of Clinical Immunology and Rheumatology, University of Alabama at Birmingham, Birmingham, Alabama. 7. Department of Biomedical Informatics and Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee.
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.
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.
Authors: Atle Fretheim; Stephen B Soumerai; Fang Zhang; Andrew D Oxman; Dennis Ross-Degnan Journal: J Clin Epidemiol Date: 2013-08 Impact factor: 6.437
Authors: Nigel A Scott; Kara K Lee; Claire Sadowski; Ekaterina V Kurbatova; Stefan V Goldberg; Pheona Nsubuga; Rene Kitshoff; Colleen Whitelaw; Hanh Nguyen Thuy; Kumar Batra; Cynthia Allen-Blige; Howard Davis; Jay Kim; Mimi Phan; Pamela Fedrick; Kuo Wei Chiu; Charles M Heilig; Erin Sizemore Journal: Contemp Clin Trials Date: 2021-03-29 Impact factor: 2.261