Bayesian model selection for multilevel mediation models

被引:2
作者
Ariyo, Oludare [1 ,2 ]
Lesaffre, Emmanuel [1 ]
Verbeke, Geert [1 ]
Huisman, Martijn [3 ]
Heymans, Martijn [3 ,4 ]
Twisk, Jos [3 ]
机构
[1] Katholieke Univ Leuven, Interuniv Inst Biostat & Stat Bioinformat I BioSt, Leuven, Belgium
[2] Fed Univ Agr, Dept Stat, Abeokuta, Nigeria
[3] Amsterdam UMC, Amsterdam Publ Hlth Res Inst, Dept Epidemiol & Data Sci, Amsterdam, Netherlands
[4] Vrije Univ Amsterdam, Dept Sociol, Amsterdam, Netherlands
关键词
deviance information criterion; marginalized likelihood; multilevel mediation models; pseudo Bayes factor; Watanabe-Akaike information criterion; DEVIANCE INFORMATION CRITERION; MODERATED MEDIATION; CONFIDENCE-LIMITS; EQUIVALENCE; ADVANTAGES; PRODUCT;
D O I
10.1111/stan.12256
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
Mediation analysis is often used to explore the complex relationship between two variables through a third mediating variable. This paper aims to illustrate the performance of the deviance information criterion, the pseudo-Bayes factor, and the Watanabe-Akaike information criterion in selecting the appropriate multilevel mediation model. Our focus will be on comparing the conditional criteria (given random effects) versus the marginal criteria (averaged over random effects) in this respect. Most of the previous work on the multilevel mediation models fails to report the poor behavior of the conditional criteria. We demonstrate here the superiority of the marginal version of the selection criteria over their conditional counterpart in the mediated longitudinal settings through simulation studies and via an application to data from the Longitudinal Aging Study of the Amsterdam study. In addition, we demonstrate the usefulness of our self-written R function for multilevel mediation models.
引用
收藏
页码:219 / 235
页数:17
相关论文
共 50 条
  • [31] Multilevel SEM Strategies for Evaluating Mediation in Three-Level Data
    Preacher, Kristopher J.
    MULTIVARIATE BEHAVIORAL RESEARCH, 2011, 46 (04) : 691 - 731
  • [32] Bayesian criterion-based variable selection
    Maity, Arnab Kumar
    Basu, Sanjib
    Ghosh, Santu
    JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS, 2021, 70 (04) : 835 - 857
  • [33] A Tutorial in Bayesian Mediation Analysis With Latent Variables
    Miocevic, Milica
    METHODOLOGY-EUROPEAN JOURNAL OF RESEARCH METHODS FOR THE BEHAVIORAL AND SOCIAL SCIENCES, 2019, 15 (04) : 137 - 146
  • [34] Model selection for fish growth patterns based on a Bayesian approach: A case study of five freshwater fish species
    Zhang, Kui
    Zhang, Jun
    Li, Jiajun
    Liao, Baochao
    AQUATIC LIVING RESOURCES, 2020, 33
  • [35] Power in Bayesian Mediation Analysis for Small Sample Research
    Miocevic, Milica
    MacKinnon, David P.
    Levy, Roy
    STRUCTURAL EQUATION MODELING-A MULTIDISCIPLINARY JOURNAL, 2017, 24 (05) : 666 - 683
  • [36] Bayesian Model Selection Maps for Group Studies Using M/EEG Data
    Harris, Clare D.
    Rowe, Elise G.
    Randeniya, Roshini
    Garrido, Marta I.
    FRONTIERS IN NEUROSCIENCE, 2018, 12
  • [37] Bayesian Analysis of Nonlinear Reproductive Dispersion Mixed Models for Longitudinal Data with Nonignorable Missing Covariates
    Tang, Nian-Sheng
    Zhao, Hui
    COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, 2014, 43 (06) : 1265 - 1287
  • [38] Bayesian inference of asymmetric stochastic conditional duration models
    Men, Zhongxian
    Kolkiewicz, Adam W.
    Wirjanto, Tony S.
    JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION, 2016, 86 (07) : 1295 - 1319
  • [39] Bayesian analysis of periodic asymmetric power GARCH models
    Aknouche, Abdelhakim
    Demmouche, Nacer
    Dimitrakopoulos, Stefanos
    Touche, Nassim
    STUDIES IN NONLINEAR DYNAMICS AND ECONOMETRICS, 2020, 24 (04)
  • [40] Increasing Statistical Power in Mediation Models Without Increasing Sample Size
    Fritz, Matthew S.
    Cox, Matthew G.
    MacKinnon, David P.
    EVALUATION & THE HEALTH PROFESSIONS, 2015, 38 (03) : 343 - 366