Flexible multivariate marginal models for analyzing multivariate longitudinal data, with applications in R

被引:3
|
作者
Asar, Oezguer [1 ]
Ilk, Ozlem [2 ]
机构
[1] Lancaster Med Sch, CHICAS, Fac Hlth & Med, Lancaster LA1 4YG, England
[2] Middle E Tech Univ, Fac Arts & Sci, Dept Stat, TR-06800 Ankara, Turkey
关键词
Clustered data; Multiple outcomes; Parsimonious model building; Statistical software; Quasi-likelihood inference; BINARY DATA; OUTCOMES;
D O I
10.1016/j.cmpb.2014.04.005
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Most of the available multivariate statistical models dictate on fitting different parameters for the covariate effects on each multiple responses. This might be unnecessary and inefficient for some cases. In this article, we propose a modelling framework for multivariate marginal models to analyze multivariate longitudinal data which provides flexible model building strategies. We show that the model handles several response families such as binomial, count and continuous. We illustrate the model on the Kenya Morbidity data set. A simulation study is conducted to examine the parameter estimates. An R package mmm2 is proposed to fit the model. (C) 2014 Elsevier Ireland Ltd. All rights reserved.
引用
收藏
页码:135 / 146
页数:12
相关论文
共 50 条
  • [21] Marginal effects in multivariate probit models
    John Mullahy
    [J]. Empirical Economics, 2017, 52 : 447 - 461
  • [22] Flexible Multivariate Density Estimation With Marginal Adaptation
    Giordani, Paolo
    Mun, Xiuyan
    Minh-Ngoc Tran
    Kohn, Robert
    [J]. JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS, 2013, 22 (04) : 814 - 829
  • [23] Evaluation of Multivariate Classification Models for Analyzing NMR Metabolomics Data
    Thao Vu
    Siemek, Parker
    Bhinderwala, Fatema
    Xu, Yuhang
    Powers, Robert
    [J]. JOURNAL OF PROTEOME RESEARCH, 2019, 18 (09) : 3282 - 3294
  • [24] A transition copula model for analyzing multivariate longitudinal data with missing responses
    Ahmadi, A.
    Baghfalaki, T.
    Ganjali, M.
    Kabir, A.
    Pazouki, A.
    [J]. JOURNAL OF APPLIED STATISTICS, 2022, 49 (12) : 3164 - 3177
  • [25] Analyzing multivariate longitudinal binary data: a generalized estimating equations approach
    Sutradhar, BC
    Farrell, PJ
    [J]. CANADIAN JOURNAL OF STATISTICS-REVUE CANADIENNE DE STATISTIQUE, 2004, 32 (01): : 39 - 55
  • [26] Marginal modelling of multivariate categorical data
    Molenberghs, G
    Lesaffre, E
    [J]. STATISTICS IN MEDICINE, 1999, 18 (17-18) : 2237 - 2255
  • [27] Marginal semiparametric transformation models for clustered multivariate competing risks data
    He, Yizeng
    Kim, Soyoung
    Mao, Lu
    Ahn, Kwang Woo
    [J]. STATISTICS IN MEDICINE, 2022, 41 (26) : 5349 - 5364
  • [28] Marginal proportional hazards models for multivariate interval-censored data
    Xu, Yangjianchen
    Zeng, Donglin
    Lin, D. Y.
    [J]. BIOMETRIKA, 2023, 110 (03) : 815 - 830
  • [29] Hidden Markov Latent Variable Models with Multivariate Longitudinal Data
    Song, Xinyuan
    Xia, Yemao
    Zhu, Hongtu
    [J]. BIOMETRICS, 2017, 73 (01) : 313 - 323
  • [30] Semiparametric Bayesian joint models of multivariate longitudinal and survival data
    Tang, Nian-Sheng
    Tang, An-Min
    Pan, Dong-Dong
    [J]. COMPUTATIONAL STATISTICS & DATA ANALYSIS, 2014, 77 : 113 - 129