Heterogeneity of Treatment Effects Among Patients With Type 2 Diabetes and Elevated Body Mass Index in a Study Comparing Group Medical Visits Focused on Weight Management and Medication Intensification. Issue 11 (13th November 2021) - Kobe, Elizabeth A.; Crowley, Matthew J.; Jeffreys, Amy S.; Yancy, William S.; Zervakis, Jennifer; Edelman, David; Voils, Corrine I.; Maciejewski, Matthew L.; Coffman, Cynthia J.
Abstract : Supplemental Digital Content is available in the text. Abstract : Background: Illuminating heterogeneity of treatment effect (HTE) within trials is important for identifying target populations for implementation. Objective: The aim of this study was to examine HTE in a trial of group medical visits (GMVs) for patients with type 2 diabetes and elevated body mass index. Research Design and Measures: Participants (n=263) were randomized to GMV-based medication management plus low carbohydrate diet-focused weight management (WM/GMV; n=127) or GMV-based medication management alone (GMV; n=136) for diabetes control. We used QUalitative INteraction Trees, a tree-based clustering method, to identify subgroups with greater improvement in hemoglobin A1c (HbA1c) and weight from either WM/GMV or GMV. Subgroup predictors included 32 baseline demographic, clinical, and psychosocial factors. Internal validation was conducted to estimate bias in the range of mean outcome differences between arms. Results: QUalitative INteraction Trees analyses indicated that for patients who had not previously attempted weight loss, WM/GMV resulted in better glycemic control than GMV (mean difference in HbA1c improvement=1.48%). For patients who had previously attempted weight loss and had lower cholesterol and blood urea nitrogen, GMV was better than WM/GMV (mean difference in HbA1c improvement=1.51%). No treatment-subgroup effects were identified for weight. Internal validation resulted in moderate corrections in mean HbA1c differences between arms; however, differences remained in the clinically significant range. Conclusion: This work represents a novel step toward targeting care approaches for patients to maximize benefit based on individual patient characteristics.