Repeated measures random forests (RMRF): Identifying factors associated with nocturnal hypoglycemia

• Published in: Biometrics Vol. 77; no. 1; pp. 343 - 351
• Main Authors: Calhoun, Peter, Levine, Richard A., Fan, Juanjuan
• Format: Journal Article
• Language: English
• Published: United States Blackwell Publishing Ltd 01.03.2021
• Subjects: Algorithms; Blood glucose; Decision trees; Diabetes; Diabetes mellitus (insulin dependent); exercise; Generalized linear models; Glucose; glycemic control; Hypoglycemia; Insulin; insulin-dependent diabetes mellitus; longitudinal data; Model accuracy; patient care; prediction; random forest; repeated measures; risk; Statistical models; type 1 diabetes; variable importance; Within-subjects design; variable importance; type 1 diabetes; random forest; hypoglycemia; longitudinal data; repeated measures
• ISSN: 0006-341X; 1541-0420; 1541-0420
• DOI: 10.1111/biom.13284

Nocturnal hypoglycemia is a common phenomenon among patients with diabetes and can lead to a broad range of adverse events and complications. Identifying factors associated with hypoglycemia can improve glucose control and patient care. We propose a repeated measures random forest (RMRF) algorithm that can handle nonlinear relationships and interactions and the correlated responses from patients evaluated over several nights. Simulation results show that our proposed algorithm captures the informative variable more often than naïvely assuming independence. RMRF also outperforms standard random forest and extremely randomized trees algorithms. We demonstrate scenarios where RMRF attains greater prediction accuracy than generalized linear models. We apply the RMRF algorithm to analyze a diabetes study with 2524 nights from 127 patients with type 1 diabetes. We find that nocturnal hypoglycemia is associated with HbA1c, bedtime blood glucose (BG), insulin on board, time system activated, exercise intensity, and daytime hypoglycemia. The RMRF can accurately classify nights at high risk of nocturnal hypoglycemia.