Interindividual Differences in Treatment Effects Based on Structural Equation Models with Latent Variables: An EffectLiteR Tutorial

被引:12
作者
Mayer, Axel [1 ]
Zimmermann, Johannes [2 ]
Hoyer, Juergen [3 ]
Salzer, Simone [4 ,6 ]
Wiltink, Joerg [5 ]
Leibing, Eric [6 ]
Leichsenring, Falk [7 ]
机构
[1] Rhein Westfal TH Aachen, Aachen, Germany
[2] Univ Kassel, Kassel, Germany
[3] Tech Univ Dresden, Dresden, Germany
[4] Int Psychoanalyt Univ Berlin, Berlin, Germany
[5] Johannes Gutenberg Univ Mainz, Mainz, Germany
[6] Univ Gottingen, Gottingen, Germany
[7] Justus Liebig Univ Giessen, Giessen, Germany
关键词
Individual treatment effects; structural equation modeling; latent variables; moderation; interaction; causal effects; COGNITIVE-BEHAVIORAL THERAPY; SOCIAL ANXIETY DISORDER; PSYCHODYNAMIC THERAPY; PERSONALIZED MEDICINE; EFFECT HETEROGENEITY; DEPRESSION; PSYCHOTHERAPY; ATTACHMENT; REGRESSION; INVENTORY;
D O I
10.1080/10705511.2019.1671196
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
The investigation of interindividual differences in the effects of a treatment is challenging, because many constructs-of-interest in psychological research such as depression or anxiety are latent variables and modeling heterogeneity in treatment effects requires interactions and potentially non-linear relationships. In this paper, we present a tutorial of the EffectLiteR approach that allows for estimating individual treatment effects based on latent variable models. We describe step by step how to apply the approach using the EffectLiteR software package with data from the multicenter randomized controlled trial of the Social Phobia Psychotherapy Network (SOPHO-NET) and provide guidelines and recommendations for researchers. The focus of the paper is on explaining the results of a comprehensive effect analysis in an accessible language and on highlighting the opportunities the EffectLiteR approach offers for analyzing interindividual differences in treatment effects.
引用
收藏
页码:798 / 816
页数:19
相关论文
共 82 条
  • [51] Little R., 1987, Statistical Analysis with Missing Data
  • [52] Why the Items Versus Parcels Controversy Needn't Be One
    Little, Todd D.
    Rhemtulla, Mijke
    Gibson, Kimberly
    Schoemann, Alexander M.
    [J]. PSYCHOLOGICAL METHODS, 2013, 18 (03) : 285 - 300
  • [53] Sample Size Requirements for Multivariate Models to Predict Between-Patient Differences in Best Treatments of Major Depressive Disorder
    Luedtke, Alex
    Sadikova, Ekaterina
    Kessler, Ronald C.
    [J]. CLINICAL PSYCHOLOGICAL SCIENCE, 2019, 7 (03) : 445 - 461
  • [54] Why Item Parcels Are (Almost) Never Appropriate: Two Wrongs Do Not Make a Right-Camouflaging Misspecification With Item Parcels in CFA Models
    Marsh, Herbert W.
    Luedtke, Oliver
    Nagengast, Benjamin
    Morin, Alexandre J. S.
    Von Davier, Matthias
    [J]. PSYCHOLOGICAL METHODS, 2013, 18 (03) : 257 - 284
  • [55] Analysis of Variance Models with Stochastic Group Weights
    Mayer, Axel
    Thoemmes, Felix
    [J]. MULTIVARIATE BEHAVIORAL RESEARCH, 2019, 54 (04) : 542 - 554
  • [56] Effect Analysis Using Nonlinear Structural Equation Mixture Modeling
    Mayer, Axel
    Umbach, Nora
    Flunger, Barbara
    Kelava, Augustin
    [J]. STRUCTURAL EQUATION MODELING-A MULTIDISCIPLINARY JOURNAL, 2017, 24 (04) : 556 - 570
  • [57] The EffectLiteR Approach for Analyzing Average and Conditional Effects
    Mayer, Axel
    Dietzfelbinger, Lisa
    Rosseel, Yves
    Steyer, Rolf
    [J]. MULTIVARIATE BEHAVIORAL RESEARCH, 2016, 51 (2-3) : 374 - 391
  • [58] Analyzing average and conditional effects with multigroup multilevel structural equation models
    Mayer, Axel
    Nagengast, Benjamin
    Fletcher, John
    Steyer, Rolf
    [J]. FRONTIERS IN PSYCHOLOGY, 2014, 5
  • [59] MEASUREMENT INVARIANCE, FACTOR-ANALYSIS AND FACTORIAL INVARIANCE
    MEREDITH, W
    [J]. PSYCHOMETRIKA, 1993, 58 (04) : 525 - 543
  • [60] Counterfactuals, causal effect heterogeneity, and the catholic school effect on learning
    Morgan, SL
    [J]. SOCIOLOGY OF EDUCATION, 2001, 74 (04) : 341 - 374