Group-based multi-trajectory modeling

Statistical Methods in Medical Research

Volumn 27, Issue 7, 2018, Pages 2015-2023

  Nagin, Daniel S ^a   Jones, Bobby L ^b   Passos, Valéria Lima ^c   Tremblay, Richard E ^d  

^a CARNEGIE MELLON UNIVERSITY   (United States)

^b UNIVERSITY OF PITTSBURGH   (United States)

^c MAASTRICHT UNIVERSITY   (Netherlands)

^d UNIVERSITY COLLEGE DUBLIN   (Ireland)

Author keywords

group based trajectory modeling; latent class analysis; Longitudinal analysis of multiple outcomes; trajectories of multiple disease biomarkers

Indexed keywords

ARTICLE; BODY MASS; CARBON DIOXIDE BLOOD LEVEL; CHRONIC KIDNEY FAILURE; CLUSTER ANALYSIS; CONTROLLED STUDY; DISEASE COURSE; ESTIMATED GLOMERULAR FILTRATION RATE; FEVER; FINITE ELEMENT ANALYSIS; GROUP-BASED TRAJECTORY MODELING; HEALTH STATUS; HEMOGLOBIN BLOOD LEVEL; HUMAN; MULTI-TRAJECTORY MODELING; MULTIVARIATE ANALYSIS; OUTCOME ASSESSMENT; POSTOPERATIVE COMPLICATION; STATISTICAL MODEL; ADOLESCENT; ADULT; ALGORITHM; CHILD; HEALTH SURVEY; LATENT CLASS ANALYSIS; LONGITUDINAL STUDY; MALE; PRESCHOOL CHILD; YOUNG ADULT;

BIOLOGICAL MARKER;

ADOLESCENT; ADULT; ALGORITHMS; BIOMARKERS; CHILD; CHILD, PRESCHOOL; HUMANS; LATENT CLASS ANALYSIS; LONGITUDINAL STUDIES; MALE; MODELS, STATISTICAL; POPULATION SURVEILLANCE; YOUNG ADULT;

EID: 85046378088     PISSN: 09622802     EISSN: 14770334     Source Type: Journal    
DOI: 10.1177/0962280216673085     Document Type: Article

References (15)

1. Ritchie MD, Holzinger ER, Li R, et al. Methods of integrating data to uncover genotype-phenotype interactions. Nat Rev Genet 2015; 6: 85–97
2. Verbeke G, Fieuws S, Molenberghs G, The analysis of multivariate longitudinal data: a review. Stat Methods Med Res 2014; 1: 42–59.
3. Lai D, Xu H, Koller D, A multivariate finite mixture latent trajectory model with application to dementia studies. J Appl Stat 2016; 43: 2503–2523.
4. Nagin DS, Tremblay RE. Analyzing developmental trajectories of distinct but related behaviors: a group-based method. Psychol Meth 2001; 6: 18–34.
5. Nagin DS. Group-based modeling of development, Cambridge, MA: Harvard University Press, 2005.
6. Pickles A, Croudace T. Latent mixture models for multivariate and longitudinal outcomes. Stat Methods Med Res 2010; 19: 271–289.
7. Muthén B, Latent variable analysis: growth mixture modeling and related techniques for longitudinal data. In: Kaplan D, (ed). Handbook of quantitative methodology for the social sciences, Newbury Park, CA: Sage, 2004, pp. 345–368.
8. Jones BL, Nagin DS. Advances in group-based trajectory modeling and an SAS procedure for estimating them. Sociol Meth Res 2007; 35: 542–572.
9. Jones BL, Nagin DS. A note on a stata plugin for estimating group-based trajectory models. Sociol Meth Res 2013; 42: 608–613.
10. Elmer J, Gianakas JJ, Rittenberger JC, et al. Group-based trajectory modeling of suppression ratio after cardiac arrest. Neurocrit Care 2016; 25: 415–423
11. Shah N, Vegl V, Dhingra A, What is a “normal” postoperative temperature? Group based trajectory modeling in postoperative knee arthroplasty patients in a large health system. AMIA-DMMI 2015. http://www.dmmh.org/dmmi15.
12. McLachlan G, Peel D. Finite mixture models, New York: Wiley, 2000.
13. Heckman J, Singer B. A method for minimizing the impact of distributional assumptions in econometric models for duration data. Econometrica 1984; 1: 271–320.
14. Nagin DS, Land KC. Age, criminal careers, and population heterogeneity—specification and estimation of a nonparametric, mixed poisson model. Criminology 1993; 31: 327–362.
15. Klijn SL, Weijenberg MP, Lemmens P, Introducing the fit-criteria assessment plot—a visualisation tool to assist class enumeration in group-based trajectory modelling. Stat Methods Med Res 2015. 1–15. http://smm.sagepub.com/content/early/2015/07/31/0962280215598665.full.pdf+html.