A Novel Approach to Better Characterize Medication Adherence in Oral Anticancer Treatments.

Abstract

Purpose: This study aims to describe a 12-month medication adherence with oral anticancer medications (OAMs) in a routine care medication adherence program, and to better characterize non-persistence.Patients and methods:In this observational, one-centered, longitudinal study, medication adherence was monitored electronically while patients were taking part in a medication adherence program for 12 months or until treatment stop. Patients were >18 years and starting or taking one of the following OAMs: letrozole, exemestane, imatinib, sunitinib, capecitabine, or temozolomide. Non-persistence was defined as any premature treatment interruption due to patient's unilateral decision or to a medical decision because of adverse effects. The Kaplan Meier survival function estimate was used to characterize persistence, and Generalized Estimating Equations (GEE) were adopted to fit implementation. Statistical analyses were performed using the R software package.Results: Forty-three outpatients with various tumor entities were enrolled. Reasons for quitting the medication adherence program and/or OAM medication were characterized as OAM discontinuation due to adverse effects or toxicity (n = 5), planned OAM completion time (n = 10), OAM failure (cancer relapse) (n = 5) and non-compliance to the adherence program (n = 3). In persistent patients, the implementation rates were high (from 98% at baseline to 97% at 12 months). The probability of being persistent at 12 months was estimated at 85%.Conclusion: A better characterization of both persistence and implementation to OAMs in real life settings is crucial for understanding and optimizing medication adherence to OAMs. The complex identification of non-persistence underlines the need to carefully and prospectively assess OAM interruption or treatment switch reasons. The GEE analysis for describing implementation to OAMs will allow researchers and professionals to take advantage of the richness of longitudinal real-time data, to avoid reducing such data through thresholds and to put them into perspective with OAM blood levels.