The analysis of multivariate longitudinal data: A review

被引：200
|
作者：
Verbeke, Geert [1 ,2 ]
Fieuws, Steffen [1 ]
Molenberghs, Geert [1 ,2 ]
Davidian, Marie [3 ]
机构：
[1] Katholieke Univ Leuven, Interuniv Inst Biostat & Stat Bioinformat, B-3000 Louvain, Belgium
[2] Univ Hasselt, Interuniv Inst Biostat & Stat Bioinformat, B-3590 Diepenbeek, Belgium
[3] N Carolina State Univ, Dept Stat, Raleigh, NC 27695 USA
来源：
STATISTICAL METHODS IN MEDICAL RESEARCH | 2014年 / 23卷 / 01期
关键词：
Mixed models; random effects; shared parameters; marginal models; conditional models; latent variables; ANALYZING DEVELOPMENTAL TRAJECTORIES; COMPOSITE LIKELIHOOD APPROACH; CORRELATED BINARY REGRESSION; RANDOM-EFFECTS MODELS; MIXED-EFFECTS MODELS; LATENT CURVE MODELS; COVARIANCE-STRUCTURES; HEARING THRESHOLDS; COMPONENT ANALYSIS; 3-MODE MODELS;
D O I：
10.1177/0962280212445834
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：
Longitudinal experiments often involve multiple outcomes measured repeatedly within a set of study participants. While many questions can be answered by modeling the various outcomes separately, some questions can only be answered in a joint analysis of all of them. In this article, we will present a review of the many approaches proposed in the statistical literature. Four main model families will be presented, discussed and compared. Focus will be on presenting advantages and disadvantages of the different models rather than on the mathematical or computational details.
引用
收藏
页码：42 / 59
页数：18
相关论文
共 50 条
  • [31] Biclustering multivariate discrete longitudinal data
    Alfo, M.
    Marino, M. F.
    Martella, F.
    STATISTICS AND COMPUTING, 2024, 34 (01)
  • [32] Dynamical correlation for multivariate longitudinal data
    Dubin, JA
    Müller, HG
    JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 2005, 100 (471) : 872 - 881
  • [33] Biclustering multivariate discrete longitudinal data
    M. Alfó
    M. F. Marino
    F. Martella
    Statistics and Computing, 2024, 34
  • [34] Boosted multivariate trees for longitudinal data
    Amol Pande
    Liang Li
    Jeevanantham Rajeswaran
    John Ehrlinger
    Udaya B. Kogalur
    Eugene H. Blackstone
    Hemant Ishwaran
    Machine Learning, 2017, 106 : 277 - 305
  • [35] A clustering algorithm for multivariate longitudinal data
    Bruckers, Liesbeth
    Molenberghs, Geert
    Drinkenburg, Pim
    Geys, Helena
    JOURNAL OF BIOPHARMACEUTICAL STATISTICS, 2016, 26 (04) : 725 - 741
  • [36] Boosted multivariate trees for longitudinal data
    Pande, Amol
    Li, Liang
    Rajeswaran, Jeevanantham
    Ehrlinger, John
    Kogalur, Udaya B.
    Blackstone, Eugene H.
    Ishwaran, Hemant
    MACHINE LEARNING, 2017, 106 (02) : 277 - 305
  • [37] MULTIVARIATE-ANALYSIS OF STRATIGRAPHIC DATA IN GEOLOGY - A REVIEW
    BIRKS, HJB
    CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 1987, 2 (1-3) : 109 - 126
  • [38] MULTIVARIATE DATA-ANALYSIS IN PALEOECOLOGY AND PALEOBIOGEOGRAPHY - A REVIEW
    SHI, GR
    PALAEOGEOGRAPHY PALAEOCLIMATOLOGY PALAEOECOLOGY, 1993, 105 (3-4) : 199 - 234
  • [39] A review on longitudinal data analysis with random forest
    Hu, Jianchang
    Szymczak, Silke
    BRIEFINGS IN BIOINFORMATICS, 2023, 24 (02)
  • [40] Book review: practical longitudinal data analysis
    Hand, D.
    Crowder, M.
    Technometrics, 1997, 39 (01):
12345