Using Group-based Trajectory Models and Propensity Score Weighting to Detect Heterogeneous Treatment Effects The Case Study of Generic Hormonal Therapy for Women With Breast CancerWinn, Aaron N., PhD, MPP; Fergestrom, Nicole M., MS; Neuner, Joan M., MD, MPHMedical Care: January 2019 - Volume 57 - Issue 1 - p 85–93 doi: 10.1097/MLR.0000000000001019 Original Article Abstract Author InformationAuthors Article MetricsMetrics Background: We extend an interrupted time series study design to identify heterogenous treatment effects using group-based trajectory models (GBTMs) 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 GBTMs, 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: GBTMs 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. Medical College of Wisconsin, Milwaukee, WI Supported by the American Cancer Society RSG-11-098-01-CPHPS and National Institute on Minority Health and Health Disparities(NIMHD) grant number 1R01MD010728-01. The authors declare no conflict of interest. Reprints: Aaron N. Winn, PhD, MPP, Medical College of Wisconsin, 8701 Watertown Plank Road, Milwaukee, WI 53226. E-mail: email@example.com. Copyright © 2019 Wolters Kluwer Health, Inc. All rights reserved.