Understanding Meta-Analysis Through Data Simulation With Applications to Power Analysis

被引:0
|
作者
Gambarota, Filippo [1 ]
Altoe, Gianmarco [1 ]
机构
[1] Univ Padua, Dept Dev & Social Psychol, Padua, Italy
关键词
meta-analysis; Monte Carlo simulations; power analysis; EFFECTS META-REGRESSION; CONFIDENCE-INTERVALS; HETEROGENEITY; TESTS;
D O I
10.1177/25152459231209330
中图分类号
B84 [心理学];
学科分类号
04 ; 0402 ;
摘要
Meta-analysis is a powerful tool to combine evidence from existing literature. Despite several introductory and advanced materials about organizing, conducting, and reporting a meta-analysis, to our knowledge, there are no introductive materials about simulating the most common meta-analysis models. Data simulation is essential for developing and validating new statistical models and procedures. Furthermore, data simulation is a powerful educational tool for understanding a statistical method. In this tutorial, we show how to simulate equal-effects, random-effects, and metaregression models and illustrate how to estimate statistical power. Simulations for multilevel and multivariate models are available in the Supplemental Material available online. All materials associated with this article can be accessed on OSF (https://osf.io/54djn/).
引用
收藏
页数:18
相关论文
共 50 条
  • [41] Comparing the Overall Result and Interaction in Aggregate Data Meta-Analysis and Individual Patient Data Meta-Analysis
    Huang, Yafang
    Tang, Jinling
    Tam, Wilson Wai-san
    Mao, Chen
    Yuan, Jinqiu
    Di, Mengyang
    Yang, Zuyao
    [J]. MEDICINE, 2016, 95 (14)
  • [42] Finding clues through meta-analysis
    Weintraub, M
    [J]. CURRENT THERAPEUTIC RESEARCH-CLINICAL AND EXPERIMENTAL, 2003, 64 (08): : 632 - 633
  • [43] Understanding heterogeneity in meta-analysis: the role of meta-regression
    Baker, W. L.
    White, C. Michael
    Cappelleri, J. C.
    Kluger, J.
    Coleman, C. I.
    [J]. INTERNATIONAL JOURNAL OF CLINICAL PRACTICE, 2009, 63 (10) : 1426 - 1434
  • [44] Multivariate meta-analysis for data consortia, individual patient meta-analysis, and pooling projects
    Ritz, John
    Demidenko, Eugene
    Spiegelman, Donna
    [J]. JOURNAL OF STATISTICAL PLANNING AND INFERENCE, 2008, 138 (07) : 1919 - 1933
  • [45] Inaccurate data in meta-analysis 'Dietary patterns and colorectal cancer risk: a meta-analysis'
    Tabung, Fred K.
    [J]. EUROPEAN JOURNAL OF CANCER PREVENTION, 2019, 28 (01) : 58 - 59
  • [46] A Framework for Meta-Analysis of Cytometry Data
    Jujjavarapu, Chethan
    Hughey, Jacob
    Gherardini, Federico
    Bruggner, Robert
    Nolan, Garry
    Bhattacharya, Sanchita
    Butte, Atul
    [J]. JOURNAL OF IMMUNOLOGY, 2016, 196
  • [47] NETWORK META-ANALYSIS OF LONGITUDINAL DATA
    Vieira, M. C.
    Cope, S.
    Jansen, J. P.
    [J]. VALUE IN HEALTH, 2013, 16 (03) : A15 - A15
  • [48] Individual participant data in meta-analysis
    Spineli, Loukia M.
    Pandis, Nikolaos
    [J]. AMERICAN JOURNAL OF ORTHODONTICS AND DENTOFACIAL ORTHOPEDICS, 2021, 159 (06) : 868 - 870
  • [49] Bayesian Meta-Analysis of Observational Data
    McCandless, Lawrence C.
    [J]. PHARMACOEPIDEMIOLOGY AND DRUG SAFETY, 2011, 20 : S355 - S355
  • [50] Missing outcome data in meta-analysis
    Mavridis, Dimitris
    Chaimani, Anna
    Efthimiou, Orestis
    Salanti, Georgia
    [J]. EVIDENCE-BASED MENTAL HEALTH, 2018, 21 (03) : 123 - 123