Generalized boosted modeling to identify subgroups where effect of dabigatran versus warfarin may differ: An...

To explore generalized boosted modeling (GBM) as a method for identifying subgroups with greater benefit or harm with dabigatran versus warfarin for treatment of atrial fibrillation.

We identified new initiators of warfarin or dabigatran with nonvalvular atrial fibrillation in 2 healthcare claims databases (2009-2013) and used GBM within 1 data source (development cohort) to explore subgroups where their effect on thromboembolism and major bleeding may differ. Identified subgroups were evaluated in the second data source (validation cohort) with stabilized-inverse-probability-of-treatment weights to adjust for confounding.

Development and validation cohorts included 13 624 (28% dabigatran) and 62 596 (29% dabigatran) initiators, respectively. In development data, the strongest exposure interactions were prior thromboembolism and renal disease. In validation data, reduction in thromboembolism with dabigatran was greater for patients with versus without a history of thromboembolism by 2.8 (95% CI, -0.5 to 5.4) events per 100 patient-years. Major bleeding was reduced by 1.6/100 patient-years for dabigatran compared to warfarin initiators, without evidence of variation by renal disease.

We explored use of GBM to identify potential subgroups with different treatment effect. Dabigatran's superiority to warfarin at prevention of thromboembolism may be greater in secondary than primary prevention. In practice, secondary prevention patients are more often treated with warfarin.