Beyond Adherence Thresholds: A Simulation Study of the Optimal Classification of Longitudinal Adherence Trajectories From Medication Refill Histories - Allemann, Samuel S.; Dediu, Dan; Dima, Alexandra Lelia (Frontiers in Pharmacology, 2019)
Background: The description of adherence based on medication refill histories relies on the estimation of continuous medication availability (CMA) during an observation period. Thresholds to distinguish adherence from non-adherence typically refer to an aggregated value across the entire observation period, disregarding differences in adherence over time that may have an impact on clinical outcomes. Sliding windows to divide the observation period into smaller portions, estimating adherence for these increments, and classify individuals with similar trajectories into clusters can retain this temporal information. Optimal methods to estimate longitudinal CMA (LCMA) and ideal parametrization of sliding windows to identify underlying patterns have not yet been established. This simulation study aimed to provide guidance for future studies by analyzing the effect of different LCMA estimates, sliding window parameters, and sample characteristics on the performance of a longitudinal clustering algorithm. Methods: We generated samples of 250-25,000 individuals with one of 6 longitudinal refill patterns over a 2-year period. We used two LCMA estimates (LCMA1 and LCMA2) and their dichotomized variants (with a threshold of 80%) to create adherence trajectories. LCMA1 assumes full adherence until the supply ends while LCMA2 assumes constant adherence between refills. We assessed scenarios with different LCMA estimates and sliding window parameters for 350 independent samples. Individual trajectories were clustered with kml, an implementation of k-means for longitudinal data in R. We compared performance between the 4 LCMA estimates using the adjusted Rand Index (cARI). Results: Cluster analysis with LCMA2 outperformed other estimates, irrespective of sliding window parameters, in overall performance, correct identification of groups, and classification accuracy. Pairwise comparison between LCMA estimates showed a relative cARI-advantage of 0.12 - 0.22 (p < 0.001) for LCMA2. Sample size did not affect overall performance. Conclusions: The choice of LCMA estimate and sliding window parameters has a major impact on the performance of a clustering algorithm to identify distinct longitudinal adherence trajectories. We recommend a) assuming constant adherence between refills, b) avoiding dichotomization based on a threshold, and c) exploring optimal sliding windows parameters in simulation studies or selecting shorter non-overlapping windows for the identification of different adherence patterns from medication refill data.