Using Group-based Trajectory Models and Propensity Score Weighting to Detect Heterogenous Treatment Effects: The Case Study of Generic Hormonal Therapy for Women With Breast Cancer

BACKGROUND:We extend an interrupted time series study design to identify heterogenous treatment effects using group-based trajectory models (GBTM) to identify groups before a new policy and then examine if the effects of the policy has consistent impacts across groups using propensity score weighting to balance individuals within trajectory groups who are and are not exposed to the policy change. We explore this by examining how adherence to endocrine therapy (ET) for women with breast cancer was impacted by reducing copayments for medications by the introduction of generic ETs among women who do not receive a subsidy (the “treatment” group) to those that do receive a subsidy and are not exposed to any changes in copayments (the “control” group). METHODS:We examined monthly adherence to ET using the proportion of days covered for women diagnosed with breast cancer between 2008 and 2009 using SEER-Medicare data. To account for baseline trends, we characterize adherence for 1 year before generic approval of ET using GBTM, within each groups we generate inverse probability treatment weights of not receiving a subsidy. We compared adherence after generic entry within each GBTM using a modified Poisson model. RESULTS:GBTM for adherence in the 1-year pregeneric identified 6 groups. When comparing patients who did and did not receive a subsidy we found no overall effect of generic introduction. However, 1 of the 6 identified adherence groups postgeneric adherence increased [the “consistently low” (risk ratio=1.91; 95% confidence interval=1.34–2.72)]. CONCLUSIONS:This study describes a new approach to identify heterogenous effects when using an interrupted time series research design.