Abstract

Longitudinal cohort studies are pivotal in medical research for understanding disease progression over time. These studies track a group of individuals across multiple time points, enabling the identification of risk factors and the evaluation of interventions. Traditional methods like linear mixed models, generalized estimating equations, and survival analysis often fall short in capturing the complex, non-linear patterns of disease progression. Trajectory analysis, a statistical technique that identifies distinct paths within longitudinal data, offers a more nuanced approach. This review delves into the methodological foundations of trajectory analysis, including data preparation, model selection, parameter estimation, model evaluation, and interpretation. It highlights the advantages of trajectory analysis, such as its ability to capture heterogeneity, handle various data types, and enhance predictive power. The application of trajectory analysis in nephrology, particularly in chronic kidney disease and diabetic nephropathy, demonstrates its utility in identifying distinct subgroups with different disease trajectories. Studies have shown that trajectory analysis can uncover patterns of renal function decline and proteinuria progression, providing insights that inform personalized treatment strategies. Despite its strengths, trajectory analysis requires advanced statistical knowledge, computational resources, and large sample sizes, which can be barriers for some researchers. Nevertheless, its ability to reveal complex disease patterns and improve predictive accuracy makes it a valuable tool in longitudinal studies. This review underscores the potential of trajectory analysis to enhance our understanding of disease progression and improve patient outcomes in nephrology and beyond.

Graphical abstract