Interrupted time-series analysis yielded an effect estimate concordant with the cluster-randomized controlled trial result

J Clin Epidemiol. 2013 Aug;66(8):883-7. doi: 10.1016/j.jclinepi.2013.03.016.


Objective: We reanalyzed the data from a cluster-randomized controlled trial (C-RCT) of a quality improvement intervention for prescribing antihypertensive medication. Our objective was to estimate the effectiveness of the intervention using both interrupted time-series (ITS) and RCT methods, and to compare the findings.

Study design and setting: We first conducted an ITS analysis using data only from the intervention arm of the trial because our main objective was to compare the findings from an ITS analysis with the findings from the C-RCT. We used segmented regression methods to estimate changes in level or slope coincident with the intervention, controlling for baseline trend. We analyzed the C-RCT data using generalized estimating equations. Last, we estimated the intervention effect by including data from both study groups and by conducting a controlled ITS analysis of the difference between the slope and level changes in the intervention and control groups.

Results: The estimates of absolute change resulting from the intervention were ITS analysis, 11.5% (95% confidence interval [CI]: 9.5, 13.5); C-RCT, 9.0% (95% CI: 4.9, 13.1); and the controlled ITS analysis, 14.0% (95% CI: 8.6, 19.4).

Conclusion: ITS analysis can provide an effect estimate that is concordant with the results of a cluster-randomized trial. A broader range of comparisons from other RCTs would help to determine whether these are generalizable results.

Publication types

  • Comparative Study
  • Randomized Controlled Trial
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Antihypertensive Agents / therapeutic use
  • Cluster Analysis*
  • Data Interpretation, Statistical
  • Drug Prescriptions / statistics & numerical data
  • Drug Utilization / standards*
  • Family Practice / education
  • Guideline Adherence / statistics & numerical data*
  • Health Services Research / statistics & numerical data*
  • Humans
  • Norway
  • Practice Patterns, Physicians' / standards*
  • Quality Improvement
  • Regression Analysis
  • Time Factors


  • Antihypertensive Agents