Analyzing multiple outcomes in clinical research using multivariate multilevel models.
OBJECTIVE
Multilevel models have become a standard data analysis approach in intervention research. Although the vast majority of intervention studies involve multiple outcome measures, few studies use multivariate analysis methods. The authors discuss multivariate extensions to the multilevel model that can be used by psychotherapy researchers.


RESULTS
Using simulated longitudinal treatment data, the authors show how multivariate models extend common univariate growth models and how the multivariate model can be used to examine multivariate hypotheses involving fixed effects (e.g., does the size of the treatment effect differ across outcomes?) and random effects (e.g., is change in one outcome related to change in the other?). An online supplemental appendix provides annotated computer code and simulated example data for implementing a multivariate model.


CONCLUSIONS
Multivariate multilevel models are flexible, powerful models that can enhance clinical research.
Department of Psychology, Brigham Young University.
Scott A
SA
Baldwin
Department of Educational Psychology, University of Utah.
Zac E
ZE
Imel
Department of Psychology, Brigham Young University.
Scott R
SR
Braithwaite
Department of Psychiatry and Behavioral Sciences, University of Washington.
David C
DC
Atkins
D006801:Humans; D008297:Male; D015233:Models, Statistical; D055361:Multilevel Analysis; D015999:Multivariate Analysis; D017063:Outcome Assessment, Health Care; D011613:Psychotherapy / Q000379:methods