title: Simulation-based power calculation for designing interrupted time series analyses of health policy interventions.
abstract: OBJECTIVE
  Interrupted time series is a strong quasi-experimental research design to evaluate the impacts of health policy interventions. Using simulation methods, we estimated the power requirements for interrupted time series studies under various scenarios.
  
  
  METHODS
  Simulations were conducted to estimate the power of segmented autoregressive (AR) error models when autocorrelation ranged from -0.9 to 0.9 and effect size was 0.5, 1.0, and 2.0, investigating balanced and unbalanced numbers of time periods before and after an intervention. Simple scenarios of autoregressive conditional heteroskedasticity (ARCH) models were also explored.
  
  
  RESULTS
  For AR models, power increased when sample size or effect size increased, and tended to decrease when autocorrelation increased. Compared with a balanced number of study periods before and after an intervention, designs with unbalanced numbers of periods had less power, although that was not the case for ARCH models.
  
  
  CONCLUSIONS
  The power to detect effect size 1.0 appeared to be reasonable for many practical applications with a moderate or large number of time points in the study equally divided around the intervention. Investigators should be cautious when the expected effect size is small or the number of time points is small. We recommend conducting various simulations before investigation.
authors.affiliation: Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA 02215, USA. fang_zhang@hphc.org
authors.forename: Fang
authors.initials: F
authors.lastname: Zhang
authors.forename: Anita K
authors.initials: AK
authors.lastname: Wagner
authors.forename: Dennis
authors.initials: D
authors.lastname: Ross-Degnan
mesh_terms: D005069:Evaluation Studies as Topic; D006291:Health Policy; D006801:Humans; D015233:Models, Statistical; D011050:Policy Making; D012044:Regression Analysis; D012107:Research Design; D018401:Sample Size; D013997:Time Factors