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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 Cancer. Med Care 2019 01;57(1):85-93



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Scopus ID

2-s2.0-85057760197   2 Citations


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.

Author List

Winn AN, Fergestrom NM, Neuner JM


Joan Neuner MD, MPH Professor in the Medicine department at Medical College of Wisconsin
Aaron Winn PhD Assistant Professor in the School of Pharmacy Administration department at Medical College of Wisconsin

MESH terms used to index this publication - Major topics in bold

Antineoplastic Agents, Hormonal
Breast Neoplasms
Drugs, Generic
Health Expenditures
Medicare Part D
Medication Adherence
Propensity Score
SEER Program
Time Factors
Treatment Outcome
United States
jenkins-FCD Prod-482 91ad8a360b6da540234915ea01ff80e38bfdb40a