Clustering Functional Data with Application to Electronic Medication Adherence Monitoring in HIV Prevention Trials
Maintaining high medication adherence is essential for achieving desired efficacy in clinical trials, especially prevention trials. However, adherence is traditionally measured by self-reports that are subject to reporting biases and measurement error. Recently, electronic medication dispenser devices have been adopted in several HIV pre-exposure prophylaxis prevention studies. These devices are capable of collecting objective, frequent, and timely drug adherence data. The device opening signals generated by such devices are often represented as regularly or irregularly spaced discrete functional data, which are challenging for statistical analysis. In this paper, we focus on clustering the adherence monitoring data from such devices. We first pre-process the raw discrete functional data into smoothed functional data. Parametric mixture models with change-points, as well as several non-parametric and semi-parametric functional clustering approaches, are adapted and applied to the smoothed adherence data. Simulation studies were conducted to evaluate finite sample performances, on the choices of tuning parameters in the pre-processing step as well as the relative performance of different clustering algorithms. We applied these methods to the HIV Prevention Trials Network 069 study for identifying subgroups with distinct adherence behavior over the study period.
KeywordsDrug adherence HIV prevention Clustering Functional data Latent class model
This was partially supported by two grants from the National Institutes of Health (NIH), R01AI121259 and R01HL130483.
- 5.Gable AR, Lagakos SW (2008) Methodological challenges in biomedical HIV prevention trials. National Academies Press, Washington, DCGoogle Scholar
- 6.Gibaldi M, Nagashima R, Levy G (1969) Relationship between drug concentration in plasma or serum and amount of drug in the body. J Pharm Sci 58(2):193–197Google Scholar
- 7.Grant RM, Anderson PL, McMahan V, Liu A, Amico KR, Mehrotra M, Hosek S, Mosquera C, Casapia M, Montoya O (2014) Uptake of pre-exposure propylaxis, sexual practices, and hiv incidence in men and transgender women who have sex with men: a cohort study. Lancet Infect Dis 14(9):820–829Google Scholar
- 8.Gulick RM, Wilkin TJ, Chen YQ, Landovitz RJ, Amico KR, Young AM, Richardson P, Marzinke MA, Hendrix CW, Eshleman SH (2016) Phase 2 study of the safety and tolerability of maraviroc-containing regimens to prevent hiv infection in men who have sex with men (hptn 069/actg a5305). J Infect Dis 215(2):238–246Google Scholar
- 9.Haberer JE, Kahane J, Kigozi I, Emenyonu N, Hunt P, Martin J, Bangsberg DR (2010) Real-time adherence monitoring for HIV antiretroviral therapy. AIDS Behav 14(6):1340–1346 PMCID: PMC2974938Google Scholar
- 20.Rand WM (1971) Objective criteria for the evaluation of clustering methods. J Amn Stat Assoc 66(336):846–850Google Scholar
- 23.Vermunt JK (2010) Latent class modeling with covariates: two improved three-step approaches. Polit Anal 18(4):450–469Google Scholar
- 24.Vrijens B, Vincze G, Kristanto P, Urquhart J, Burnier M (2008) Adherence to prescribed antihypertensive drug treatments: longitudinal study of electronically compiled dosing histories. BMJ 336(7653):1114–1117Google Scholar