Latent mixture models for multivariate and longitudinal outcomes.
Repeated measures and multivariate outcomes are an increasingly common feature of trials. Their joint analysis by means of random effects and latent variable models is appealing but patterns of heterogeneity in outcome profile may not conform to standard multivariate normal assumptions. In addition, there is much interest in both allowing for and identifying sub-groups of patients who vary in treatment responsiveness. We review methods based on discrete random effects distributions and mixture models for application in this field.
Pickles
Andrew
A
Biostatistics, Health Methodology Research Group, University of Manchester, University Place, Oxford Road, Manchester, M13 9PL, UK. andrew.pickles@manchester.ac.uk
Croudace
Tim
T
D000341:Affective Disorders, Psychotic / Q000628:therapy; D056808:Biostatistics; D002986:Clinical Trials as Topic / Q000706:statistics & numerical data*; D006801:Humans; D016014:Linear Models; D008137:Longitudinal Studies; D015233:Models, Statistical; D015999:Multivariate Analysis; D017063:Outcome Assessment, Health Care; D010349:Patient Compliance / Q000706:statistics & numerical data; D010352:Patient Dropouts / Q000706:statistics & numerical data; D016896:Treatment Outcome