Latent mixture models for multivariate and longitudinal outcomes

被引：47

作者：

Pickles, Andrew ^{[1
]}

Croudace, Tim ^{[2
]}

机构：

[1] Univ Manchester, Hlth Methodol Res Grp, Manchester M13 9PL, Lancs, England

[2] Univ Cambridge, Addenbrookes Hosp, Dept Psychiat, Cambridge CB2 2QQ, England

来源：

STATISTICAL METHODS IN MEDICAL RESEARCH | 2010年 / 19卷 / 03期

基金：

美国国家卫生研究院;

关键词：

COGNITIVE-BEHAVIORAL THERAPY; EARLY SCHIZOPHRENIA; MISSING-DATA; TRAJECTORIES; NUMBER; HETEROGENEITY; MEMBERSHIP; CAREERS; TRIALS;

D O I：

10.1177/0962280209105016

中图分类号：

R19 [保健组织与事业（卫生事业管理）];

学科分类号：

摘要：

Repeated measures and multivariate outcomes are all 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.

引用

页码：271 / 289

页数：19

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