Great diversity in the utilization and reporting of latent growth modeling approaches in type 2 diabetes: A literature review. Issue 9 (September 2022) - O'Connor, Sarah; Blais, Claudia; Mésidor, Miceline; Talbot, Denis; Poirier, Paul; Leclerc, Jacinthe
Abstract: Introduction: The progression of complications of type 2 diabetes (T2D) is unique to each patient and can be depicted through individual temporal trajectories. Latent growth modeling approaches (latent growth mixture models [LGMM] or latent class growth analysis [LCGA]) can be used to classify similar individual trajectories in a priori non-observed groups (latent groups), sharing common characteristics. Although increasingly used in the field of T2D, many questions remain regarding the utilization of these methods. Objective: To review the literature of longitudinal studies using latent growth modeling approaches to study T2D. Methods: MEDLINE (Ovid), EMBASE, CINAHL and Wb of Science were searched through August 25 th, 2021. Data was collected on the type of latent growth modeling approaches (LGMM or LCGA), characteristics of studies and quality of reporting using the GRoLTS-Checklist and presented as frequencies. Results: From the 4, 694 citations screened, a total of 38 studies were included. The studies were published beetween 2011 and 2021 and the length of follow-up ranged from 8 weeks to 14 years. Six studies used LGMM, while 32 studies used LCGA. The fields of research varied from clinical research, psychological science, healthcare utilization research and drug usage/pharmaco-epidemiology. Data sources included primary data (clinical trials, prospective/retrospective cohorts, surveys), or secondary data (health records/registries, medico-administrative). Fifty percent of studies evaluated trajectory groups as exposures for a subsequent clinical outcome, while 24% used predictive models of group membership and 5% used both. Regarding the quality of reporting, trajectory groups were adequately presented, however many studies failed to report important decisions made for the trajectory group identification. Conclusion: Although LCGA were preferred, the contexts of utilization were diverse and unrelated to the type of methods. We recommend future authors to clearly report the decisions made regarding trajectory groups identification. Highlights: There is a growing body of literature on trajectory modeling in type 2 diabetes. Latent class growth analysis can be used in many different contexts. The current reporting of methods used should be improved. Abstract : Group-based trajectory modeling; Latent class growth modeling; Epidemiology; Care trajectory; Health-care utilization; Diabetes mellitus.