Adherence trajectories in oral therapy for chronic myeloid leukemia: Overview of a research protocol

Abstract

Over a quarter of chemotherapy regimens now include oral agents. Individuals living with cancer are now responsible for administering this lifesaving therapy at home by taking every dose as prescribed. One type of oral chemotherapy, tyrosine kinase inhibitors (TKIs), is the current recommended treatment for chronic myeloid leukemia. This targeted therapy has markedly improved survival but comes with significant side effects and financial costs. In the study described in this protocol, the investigators seek to understand the dynamic nature of TKI adherence experienced by individuals diagnosed with CML. Using a mixed‐method approach in this prospective observational study, funded by the National Cancer Institute, we seek to describe subjects' adherence trajectories over 1 year. We aim to characterize adherence trajectories in individuals taking TKIs using model‐based cluster analysis. Next, we will determine how side effects and financial toxicity influence adherence trajectories. Then we will examine the influence of TKI adherence trajectories on disease outcomes. Additionally, we will explore the experience of patients taking TKIs by interviewing a subset of participants in different adherence trajectories. The projected sample includes 120 individuals taking TKIs who we will assess monthly for 12 months, measuring adherence with an objective measure (Medication Event Monitoring System). Identifying differential trajectories of adherence for TKIs is important for detecting subgroups at the highest risk of nonadherence and will support designing targeted interventions. Results from this study can potentially translate to other oral agents to improve care across different types of cancer.

Multi-cohort and longitudinal Bayesian clustering study of stage and subtype in Alzheimer’s disease

“…The multivariate mixture of generalized mixed effects clustering model 32 incorporates Bayesian inference to explore heterogeneity in the longitudinal brain data. We applied this model on brain volume/thickness but it has already been used in other applications 31,[33][34][35][36][37][38][39][40][41][42] . The Bayesian approach allowed the implementation of a complex hierarchical model for each cluster, where probabilistic mixture modeling (number of clusters) is combined with mixed effect modeling (number of brain regions) in one model definition.…”