Group-based multi-trajectory modeling

Identifying and monitoring multiple disease biomarkers and other clinically important factors affecting the course of a disease, behavior or health status is of great clinical relevance. Yet conventional statistical practice generally falls far short of taking full advantage of the information available in multivariate longitudinal data for tracking the course of the outcome of interest. We demonstrate a method called multi-trajectory modeling that is designed to overcome this limitation. The method is a generalization of group-based trajectory modeling. Group-based trajectory modeling is designed to identify clusters of individuals who are following similar trajectories of a single indicator of interest such as post-operative fever or body mass index. Multi-trajectory modeling identifies latent clusters of individuals following similar trajectories across multiple indicators of an outcome of interest (e.g., the health status of chronic kidney disease patients as measured by their eGFR, hemoglobin, blood CO2 levels). Multi-trajectory modeling is an application of finite mixture modeling. We lay out the underlying likelihood function of the multi-trajectory model and demonstrate its use with two examples

Causal inferences with group based trajectory models

Causal inference, finite mixture models, propensity scores,

Body Mass Index Trajectories in Relation to Change in Lean Mass and Physical Function: The Health, Aging and Body Composition Study

Objectives To examine body mass index (BMI) trajectories with change in lean mass and physical function in old age. Design Prospective cohort study. Setting Health, Aging and Body Composition Study. Participants Black and white men (n = 482) and women (n = 516) aged 73.1 ± 2.7 and initially free of disability. Measurements A group-based trajectory model was used to determine BMI trajectories, the path a person's BMI followed over 9 years. Lean mass, gait speed, grip strength, and knee extension strength were assessed at baseline and after 9 years, and relative changes were calculated. Multivariable linear regression was used to determine associations between trajectories and relative change in lean mass and physical function. Results Four BMI trajectories were identified for men and four for women. Although all demonstrated a decline in BMI, the rate of decline differed according to trajectory for women only. Men in Trajectory 4 (mean BMI at baseline 33.9 ± 2.3 kg/m2) declined more than those in Trajectory 1 (mean BMI at baseline 22.9 ± 1.6 kg/m2) in gait speed (-9.91%, 95% confidence interval (CI) = -15.15% to -4.67%) and leg strength (-8.63%, 95% CI = -15.62% to -1.64%). Women in Trajectory 4 (mean BMI at baseline 34.9 ± 3.0 kg/m2) had greater losses than those in Trajectory 1 (mean BMI at baseline 20.5 ± 1.6 kg/m2) in lean mass in the arms (-3.19%, 95% CI = -6.16% to -0.23%). No other associations were observed. Conclusion Obese men had the highest risk of decline in physical function despite similar weight loss between trajectories, whereas overweight and obese women who lost the most weight had the greatest risk of lean mass loss. The weight at which a person enters old age is informative for predicting loss in lean mass and physical function, illustrating the importance of monitoring weight

Regularized Finite Mixture Models for Probability Trajectories

finite mixture models, growth mixture models, latent class analysis, functional data analysis, penalized likelihood, longitudinal data analysis, trajectory analysis,