Group-based trajectory modeling in clinical research.

By Dan Nagin, CandiceL. Odgers. Annual Review of Clinical Psychology, 2010

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Pathways to Integrated Resilience Science (2015, methodology): “Recent advances in statistical strategies for modeling pathways through trajectory analysis and growth curve modeling have made it more feasible to conduct empirical studies to extract pathways from data or test theoretical pathway models (Jung & Wickrama, 2008; Nagin & Odgers, 2010).”
Trajectories of psychological need satisfaction from early to late adolescence as a predictor of adjustment in school (2014, background): “…such as hierarchical linear modeling (Raudenbush & Bryk, 2002) and latent growth curve modeling (McArdle, 1986; Meredith & Tisak, 1990), which model population variability but do not allow identifying distinct developmental trajectories that can be opposed in slopes (see Nagin & Odgers, 2010).”
A Note on a Stata Plugin for Estimating Group-based Trajectory Models (2012, methodology): “For a recent review of applications of group-based trajectory modeling, see Nagin and Odgers (2010); and for an extended discussion of the method, including technical details, see Nagin (2005).”
Clustering Longitudinal Clinical Marker Trajectories from Electronic Health Data: Applications to Phenotyping and Endotype Discovery (2015, methodology): “Latent class mixed models (LCMMs) are a family of methods designed to discover subgroup structure in longitudinal datasets using fixed and random effects (e.g., Muthén and Shedden 1999, McCulloch et al. 2002, and Nagin and Odgers 2010).”