1932

Abstract

Group-based trajectory modeling (GBTM) identifies groups of individuals following similar trajectories of one or more repeated measures. The categorical nature of GBTM is particularly well suited to clinical psychology and medicine, where patients are often classified into discrete diagnostic categories. This review highlights recent advances in GBTM and key capabilities that remain underappreciated in clinical research. These include accounting for nonrandom subject attrition, joint trajectory and multitrajectory modeling, the addition of the beta distribution to modeling options, associating trajectories with future outcomes, and estimating the probability of future outcomes. Also discussed is an approach to selecting the number of trajectory groups.

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2024-07-12
2025-02-06
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Literature Cited

  1. Albers DJ, Elhadad N, Claassen J, Perotte R, Goldstein A, Hripcsak G. 2018.. Estimating summary statistics for electronic health record laboratory data for use in high-throughput phenotyping algorithms. . J. Biomed. Inform. 78::87101
    [Crossref] [Google Scholar]
  2. Baumol W. 1992.. On my attitudes: sociopolitical and methodological. . In Eminent Economists: Their Life Philosophies, ed. M Szenberg , pp. 5159. Cambridge, UK:: Cambridge Univ. Press
    [Google Scholar]
  3. Belsky J, Steinberg L, Draper P. 1991.. Childhood experience, interpersonal development, and reproductive strategy: an evolutionary theory of socialization. . Child Dev. 62::64770
    [Crossref] [Google Scholar]
  4. Burckhardt P, Nagin DS, Padman R. 2016.. Multi-trajectory models of chronic kidney disease progression. . AMIA Annu. Symp. Proc. 2016::173746
    [Google Scholar]
  5. Elmer J, Jones BL, Nagin DS. 2018.. Using the beta distribution in group-based trajectory models. . BMC Med. Res. Methodol. 18:(1):15257
    [Crossref] [Google Scholar]
  6. Farrington DP, West DJ. 1990.. The Cambridge Study in Delinquent Development: A Long-Term Follow-Up of 411 London Males. Berlin:: Springer
    [Google Scholar]
  7. Ferrari S, Cribari-Neto F. 2004.. Beta regression for modelling rates and proportions. . J. Appl. Stat. 31::799815
    [Crossref] [Google Scholar]
  8. Follman DA, Lambert D. 1989.. Generalizing logistic regression by nonparametric mixing. . J. Am. Stat. Assoc. 84::295300
    [Crossref] [Google Scholar]
  9. Hasan S. 2012.. Group based trajectories of network formation and dynamics. . Soc. Netw. 34::50614
    [Crossref] [Google Scholar]
  10. Haviland A, Jones B, Nagin DS. 2011.. Group-based trajectory modeling extended to account for non-random subject attrition. . Sociol. Methods Res. 41::36790
    [Crossref] [Google Scholar]
  11. Heckman J, Singer B. 1984.. A method for minimizing the impact of distributional assumptions in econometric models for duration data. . Econometrica 52::271320
    [Crossref] [Google Scholar]
  12. Kandel D. 1975.. Stages in adolescent involvement in drug use. . Science 190::91214
    [Crossref] [Google Scholar]
  13. Katz S, Ford AB, Moskowitz RW, Jackson BA, Jaffe MW. 1963.. Studies of illness in the aged: the index of ADL: a standardized measure of biological and psychosocial function. . JAMA 185::91419
    [Crossref] [Google Scholar]
  14. Klijn SL, Weijenberg MP, Lemmens P, van den Brandt PA, Passos VL. 2015.. Introducing the fit-criteria assessment plot—a visualisation tool to assist class enumeration in group-based trajectory modelling. . Stat. Methods Med. Res. 26:(5):242436
    [Crossref] [Google Scholar]
  15. Lindsay BG. 1995.. Mixture Models: Theory, Geometry, and Applications. Hayward, CA:: Inst. Math. Stat.
    [Google Scholar]
  16. Loeber R. 1991.. Questions and advances in the study of developmental pathways. . In Rochester Symposium on Developmental Psychopathology, ed. D Cicchetti, S Toth , pp. 97116. Rochester, NY:: Univ. Rochester Press
    [Google Scholar]
  17. Loughran T, Larroulet P, Thornberry T. 2017.. Definitional elasticity in the measurement of intergenerational continuity in substance use. Work. Pap. , Univ. South Fla., Tampa:
    [Google Scholar]
  18. McLachlan G, Peel D. 2004.. Finite Mixture Models. New York:: Wiley-Interscience
    [Google Scholar]
  19. Moffitt TE. 1993.. Adolescence-limited and life-course-persistent antisocial behavior: a developmental taxonomy. . Psychol. Rev. 100::674701
    [Crossref] [Google Scholar]
  20. Muthén B. 2001.. Second-generation structural equation modeling with a combination of categorical and continuous latent variables: new opportunities for latent class/latent growth modeling. . In New Methods for the Analysis of Change, ed. A Sayers, L Collins , pp. 291322. Washington, DC:: Am. Psychol. Assoc.
    [Google Scholar]
  21. Muthén B, Shedden K. 1999.. Finite mixture modeling with mixture outcomes using the EM algorithm. . Biometrics 55::46369
    [Crossref] [Google Scholar]
  22. Nagin DS. 1999.. Analyzing developmental trajectories: a semi-parametric, group-based approach. . Psychol. Methods 4::13977
    [Crossref] [Google Scholar]
  23. Nagin DS. 2005.. Group-Based Modeling of Development. Cambridge, MA:: Harvard Univ. Press
    [Google Scholar]
  24. Nagin DS, Jones BL, Passos VL, Tremblay RE. 2016.. Group-based multi-trajectory modeling. . Stat. Methods Med. Res. 27:(7):201523
    [Crossref] [Google Scholar]
  25. Nagin DS, Land KC. 1993.. Age, criminal careers, and population heterogeneity: specification and estimation of a nonparametric, mixed Poisson model. . Criminology 31::32762
    [Crossref] [Google Scholar]
  26. Nagin DS, Odgers CL. 2010.. Group-based trajectory modeling in clinical research. . Annu. Rev. Clin. Psychol. 6::10938
    [Crossref] [Google Scholar]
  27. Nagin DS, Tremblay RE. 1999.. Trajectories of boys’ physical aggression, opposition, and hyperactivity on the path to physically violent and nonviolent juvenile delinquency. . Child Dev. 70::118196
    [Crossref] [Google Scholar]
  28. Nagin DS, Tremblay RE. 2001.. Analyzing developmental trajectories of distinct but related behaviors: a group-based method. . Psychol. Methods 6::1834
    [Crossref] [Google Scholar]
  29. Patterson GR, Debaryshe BD, Ramsey E. 1989.. A developmental perspective on antisocial behavior. . Am. Psychol. 44::32935
    [Crossref] [Google Scholar]
  30. Ritchie MD, Holzinger ER, Li R, Pendergrass SA, Kim D. 2015.. Methods of integrating data to uncover genotype-phenotype interactions. . Nat. Rev. Genet. 6::8597
    [Crossref] [Google Scholar]
  31. van der Nest G, Lima Passos V, Candel MJJM, van Breukelen GJP. 2022.. Model fit criteria curve behaviour in class enumeration—a diagnostic tool for model (mis)specification in longitudinal mixture modelling. . J. Stat. Comput. Simul. 92:(8):164072
    [Crossref] [Google Scholar]
  32. Vergunst F, Chadi N, Orri M, Brousseau-Paradis C, Castellanos-Ryan N, et al. 2022.. Trajectories of adolescent poly-substance use and their long-term social and economic outcomes for males from low-income backgrounds. . Eur. Child Adolesc. Psychiatry 31:(11):172938
    [Crossref] [Google Scholar]
  33. Weisburd D, Bushway S, Lum C, Yang SM. 2004.. Trajectories of crime at places: a longitudinal study of street segments in the city of Seattle. . Criminology 42::283320
    [Crossref] [Google Scholar]
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