title: Segmented regression analysis of interrupted time series studies in medication use research.
abstract: Interrupted time series design is the strongest, quasi-experimental approach for evaluating longitudinal effects of interventions. Segmented regression analysis is a powerful statistical method for estimating intervention effects in interrupted time series studies. In this paper, we show how segmented regression analysis can be used to evaluate policy and educational interventions intended to improve the quality of medication use and/or contain costs.
authors.affiliation: Department of Ambulatory Care and Prevention, Harvard Medical School and Harvard Pilgrim Health Care, Boston, MA 02215, USA. awagner@hsph.harvard.edu
authors.forename: A K
authors.initials: AK
authors.lastname: Wagner
authors.forename: S B
authors.initials: SB
authors.lastname: Soumerai
authors.forename: F
authors.initials: F
authors.lastname: Zhang
authors.forename: D
authors.initials: D
authors.lastname: Ross-Degnan
mesh_terms: D003363:Cost Control; D016527:Drug Costs / Q000706:statistics & numerical data*; D004358:Drug Therapy; D006291:Health Policy; D006801:Humans; D008137:Longitudinal Studies; D010818:Practice Patterns, Physicians'; D012044:Regression Analysis; D012107:Research Design