Interrupted time series designs in health technology assessment: lessons from two systematic reviews of behavior change strategies.
OBJECTIVE
In an interrupted time series (ITS) design, data are collected at multiple instances over time before and after an intervention to detect whether the intervention has an effect significantly greater than the underlying secular trend. We critically reviewed the methodological quality of ITS designs using studies included in two systematic reviews (a review of mass media interventions and a review of guideline dissemination and implementation strategies).


METHODS
Quality criteria were developed, and data were abstracted from each study. If the primary study analyzed the ITS design inappropriately, we reanalyzed the results by using time series regression.


RESULTS
Twenty mass media studies and thirty-eight guideline studies were included. A total of 66% of ITS studies did not rule out the threat that another event could have occurred at the point of intervention. Thirty-three studies were reanalyzed, of which eight had significant preintervention trends. All of the studies were considered "effective" in the original report, but approximately half of the reanalyzed studies showed no statistically significant differences.


CONCLUSIONS
We demonstrated that ITS designs are often analyzed inappropriately, underpowered, and poorly reported in implementation research. We have illustrated a framework for appraising ITS designs, and more widespread adoption of this framework would strengthen reviews that use ITS designs.
Ramsay
Craig R
CR
Health Services Research Unit, University of Aberdeen, Foresterhill, UK. crr@hsru.abdn.ac.uk
Matowe
Lloyd
L
Grilli
Roberto
R
Grimshaw
Jeremy M
JM
Thomas
Ruth E
RE
D003430:Cross-Sectional Studies; D004059:Diffusion of Innovation; D006296:Health Services / Q000706:statistics & numerical data; D008402:Mass Media; D017410:Practice Guidelines as Topic; D012044:Regression Analysis; D012107:Research Design; D013673:Technology Assessment, Biomedical / Q000379:methods*; D013997:Time Factors