Biostatistics 3:459-475 (2002)
© 2002 Oxford University Press
General growth mixture modeling for randomized preventive interventions
Bengt Muthén. Graduate School of Education & Information Studies, University of California, Moore Hall, Box 951521, Los Angeles, CA 90095-1521, USA bmuthen{at}ucla.edu
C. Hendricks Brown. University of South Florida, FL, USA
Katherine Masyn, Booil Jo. University of California, Los Angeles, CA, USA
Siek-Toon Khoo. Arizona State University, AZ, USA
Chih-Chien Yang. National Taichung Teachers College, Taiwan
Chen-Pin Wang. University of South Florida FL, USA
Sheppard G. Kellam. Johns Hopkins University and American Institutes for Research, Baltimore, MD, USA
John B. Carlin. University of Melbourne, Australia
Jason Liao. Medical University of South Carolina, SC, USA
To whom correspondence should be addressed
This paper proposes growth mixture modeling to assess intervention effects in longitudinal randomized trials. Growth mixture modeling represents unobserved heterogeneity among the subjects using a finite-mixture random effects model. The methodology allows one to examine the impact of an intervention on subgroups characterized by different types of growth trajectories. Such modeling is informative when examining effects on populations that contain individuals who have normative growth as well as non-normative growth. The analysis identifies subgroup membership and allows theory-based modeling of intervention effects in the different subgroups. An example is presented concerning a randomized intervention in Baltimore public schools aimed at reducing aggressive classroom behavior, where only students who were initially more aggressive showed benefits from the intervention.
Keywords: Growth modeling; Latent variables; Maximum likelihood; Randomized trials; Trajectory classes; Treatment-baseline interaction
CiteULike 
Connotea 
Del.icio.us What's this?
This article has been cited by other articles:
|
|
 |
|
  J. Braeken and F. Tuerlinckx A mixed model framework for teratology studies Biostat., October 1, 2009; 10(4): 744 - 755. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 I. Hajjar, F. Yang, F. Sorond, R. N. Jones, W. Milberg, L. A. Cupples, and L. A. Lipsitz A Novel Aging Phenotype of Slow Gait, Impaired Executive Function, and Depressive Symptoms: Relationship to Blood Pressure and Other Cardiovascular Risks J Gerontol A Biol Sci Med Sci, September 1, 2009; 64A(9): 994 - 1001. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 A. L. Pai and D. Drotar Treatment Adherence Impact: The Systematic Assessment and Quantification of the Impact of Treatment Adherence on Pediatric Medical and Psychological Outcomes J. Pediatr. Psychol., August 26, 2009; (2009) jsp073v1. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 K. Kelley and S. E. Maxwell Delineating the Average Rate of Change in Longitudinal Models Journal of Educational and Behavioral Statistics, September 1, 2008; 33(3): 307 - 332. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 S. Rabe-Hesketh and A. Skrondal Classical latent variable models for medical research Statistical Methods in Medical Research, February 1, 2008; 17(1): 5 - 32. [Abstract] [PDF]
|
|
|
|
 |
|
 Mo Wang and T. E. Bodner Growth Mixture Modeling: Identifying and Predicting Unobserved Subpopulations With Longitudinal Data Organizational Research Methods, October 1, 2007; 10(4): 635 - 656. [Abstract] [PDF]
|
|
|
|
|
|
D. B. Dunson Bayesian methods for latent trait modelling of longitudinal data Statistical Methods in Medical Research, October 1, 2007; 16(5): 399 - 415. [Abstract] [PDF]
|
|
|
|
 |
|
 J. W. Elias and M. V. Wagster Developing Context and Background Underlying Cognitive Intervention/Training Studies in Older Populations J. Gerontol. B. Psychol. Sci. Soc. Sci., June 1, 2007; 62(suppl_Special_Issue_1): 5 - 10. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
R. de la Cruz-Mesia and F. A. Quintana A model-based approach to Bayesian classification with applications to predicting pregnancy outcomes from longitudinal {beta}-hCG profiles Biostat., April 1, 2007; 8(2): 228 - 238. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
M. J. Daniels and S.-l. T. Normand Longitudinal profiling of health care units based on continuous and discrete patient outcomes Biostat., January 1, 2006; 7(1): 1 - 15. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 M. W.-L. Cheung and W. Chan Classifying Correlation Matrices Into Relatively Homogeneous Subgroups: A Cluster Analytic Approach Educational and Psychological Measurement, December 1, 2005; 65(6): 954 - 979. [Abstract] [PDF]
|
|
|
|
 |
|
 M. C. Lennon, W. McAllister, L. Kuang, and D. B. Herman Capturing Intervention Effects Over Time: Reanalysis of a Critical Time Intervention for Homeless Mentally Ill Men Am J Public Health, October 1, 2005; 95(10): 1760 - 1766. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 E. Segawa, J. E. Ngwe, Y. Li, B. R. Flay, and Aban Aya Coinvestigators Evaluation of the Effects of the Aban Aya Youth Project in Reducing Violence among African American Adolescent Males Using Latent Class Growth Mixture Modeling Techniques Eval Rev, April 1, 2005; 29(2): 128 - 148. [Abstract] [PDF]
|
|
|
|
|
|
B. Muthen and K. Masyn Discrete-Time Survival Mixture Analysis Journal of Educational and Behavioral Statistics, January 1, 2005; 30(1): 27 - 58. [Abstract] [PDF]
|
|
|
|
 |
|
 J. Audrain-McGovern, D. Rodriguez, K. P. Tercyak, J. Cuevas, K. Rodgers, and F. Patterson Identifying and Characterizing Adolescent Smoking Trajectories Cancer Epidemiol. Biomarkers Prev., December 1, 2004; 13(12): 2023 - 2034. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
D. Rodriguez and J. Audrain-McGovern Team Sport Participation and Smoking: Analysis with General Growth Mixture Modeling J. Pediatr. Psychol., June 1, 2004; 29(4): 299 - 308. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
G. A. Dagne, C. H. Brown, and G. W. Howe Bayesian Hierarchical Modeling of Heterogeneity in Multiple Contingency Tables: An Application to Behavioral Observation Data Journal of Educational and Behavioral Statistics, January 1, 2003; 28(4): 339 - 352. [Abstract] [PDF]
|
|