Application of Regression-Discontinuity Analysis in Pharmaceutical Health Services Research

OBJECTIVE: To demonstrate how a relatively underused design, regression-discontinuity (RD), can provide robust estimates of intervention effects when stronger designs are impossible to implement. DATA SOURCES/STUDY SETTING: Administrative claims from a Mid-Atlantic state Medicaid program were used to evaluate the effectiveness of an educational drug utilization review intervention. STUDY DESIGN: Quasi-experimental design. DATA COLLECTION/EXTRACTION METHODS: A drug utilization review study was conducted to evaluate a letter intervention to physicians treating Medicaid children with potentially excessive use of short-acting β(2)-agonist inhalers (SAB). The outcome measure is change in seasonally-adjusted SAB use 5 months pre- and postintervention. To determine if the intervention reduced monthly SAB utilization, results from an RD analysis are compared to findings from a pretest–posttest design using repeated-measure ANOVA. PRINCIPAL FINDINGS: Both analyses indicated that the intervention significantly reduced SAB use among the high users. Average monthly SAB use declined by 0.9 canisters per month (p<.001) according to the repeated-measure ANOVA and by 0.2 canisters per month (p<.001) from RD analysis. CONCLUSIONS: Regression-discontinuity design is a useful quasi-experimental methodology that has significant advantages in internal validity compared to other pre–post designs when assessing interventions in which subjects' assignment is based on cutoff scores for a critical variable