General growth mixture modeling for randomized preventive interventions.
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.
Muthén
Bengt
B
Graduate School of Education & Information Studies, University of California, Moore Hall, Box 951521, Los Angeles, CA 90095-1521, USA. bmuthen@ucla.edu
Brown
C Hendricks
CH
Masyn
Katherine
K
Jo
Booil
B
Khoo
Siek-Toon
ST
Yang
Chih-Chien
CC
Wang
Chen-Pin
CP
Kellam
Sheppard G
SG
Carlin
John B
JB
Liao
Jason
J