Difference-in-Differences Method in Comparative Effectiveness Research: Utility with Unbalanced Groups

BACKGROUND Comparative effectiveness research (CER) often includes observational studies utilizing administrative data. Multiple conditioning methods can be used for CER to adjust for group differences, including difference-in-differences (DiD) estimation. OBJECTIVE This study presents DiD and demonstrates how to apply this conditioning method to estimate treatment outcomes in the CER setting by utilizing the MarketScan® Databases for multiple sclerosis (MS) patients receiving different therapies. METHODS The sample included 6762 patients, with 363 in the Test Cohort [glatiramer acetate (GA) switched to fingolimod (FTY)] and 6399 in the Control Cohort (GA only, no switch) from a US administrative claims database. A trend analysis was conducted to rule out concerns regarding regression to the mean and to compare relapse rates among treatment cohorts. DiD analysis was used to enable comparisons among the Test and Control Cohorts. Logistic regression was used to estimate the probability of relapse after switching from GA to FTY, and to compare group differences in the pre- and post-index periods. RESULTS Crude DiD analysis showed that in the pre-index period more patients in the Test Cohort experienced an MS relapse and had a higher mean number of relapses than in the Control Cohort. During the pre-index period, numeric and relative data for MS relapses in patients in the Test Cohort were significantly higher than in the Control Cohort, while no significant between-group differences emerged during the post-index period. Generalized linear modeling with DiD regression estimation showed that the mean number of MS relapses decreased significantly in the post-index period among patients in the Test Cohort compared with patients in the Control Cohort. CONCLUSION In this study, an MS population was utilized to demonstrate how DiD can be applied to estimate treatment effects in a heterogeneous population, where the Test and Control Cohorts varied greatly. The results show that DiD offers a robust method for comparing diverse cohorts when other risk-adjustment methods may not be adequate.