Meta-analysis with missing data

被引:13
|
作者
White, Ian R. [1 ]
Higgins, Julian P. T. [1 ]
机构
[1] MRC Biostat Unit, Cambridge, England
来源
STATA JOURNAL | 2009年 / 9卷 / 01期
基金
英国医学研究理事会;
关键词
st0157; metamiss; meta-analysis; missing data; informative missingness odds ratio; UNCERTAINTY;
D O I
10.1177/1536867X0900900104
中图分类号
O1 [数学]; C [社会科学总论];
学科分类号
03 ; 0303 ; 0701 ; 070101 ;
摘要
A new command, metamiss, performs meta-analysis with binary outcomes when some or all studies have missing data. Missing values can be imputed as successes, as failures, according to observed event rates, or by a combination of these according to reported reasons for the data being missing. Alternatively, the user can specify the value of, or a prior distribution for, the informative missingness odds ratio.
引用
收藏
页码:57 / 69
页数:13
相关论文
共 50 条
  • [41] Missing data in randomised controlled trials evaluating palliative interventions: a systematic review and meta-analysis
    Hussain, Jamilla A.
    White, Ian R.
    Langan, Dean
    Johnson, Miriam J.
    Currow, David C.
    Torgerson, David
    Bland, Martin
    LANCET, 2016, 387 : 53 - 53
  • [42] How robust are findings of pairwise and network meta-analysis in the presence of missing participant outcome data?
    Loukia M. Spineli
    Chrysostomos Kalyvas
    Katerina Papadimitropoulou
    BMC Medicine, 19
  • [43] The meta-analysis on summary data
    Maison, Patrick
    RECHERCHE EN SOINS INFIRMIERS, 2010, (101): : 18 - 24
  • [44] Meta-analysis of neuroimaging data
    Kober, Hedy
    Wager, Tor D.
    WILEY INTERDISCIPLINARY REVIEWS-COGNITIVE SCIENCE, 2010, 1 (02) : 293 - 300
  • [45] Proper conditional analysis in the presence of missing data: Application to large scale meta-analysis of tobacco use phenotypes
    Jiang, Yu
    Chen, Sai
    McGuire, Daniel
    Chen, Fang
    Liu, Mengzhen
    Iacono, William G.
    Hewitt, John K.
    Hokanson, John E.
    Krauter, Kenneth
    Laakso, Markku
    Li, Kevin W.
    Lutz, Sharon M.
    McGue, Matthew
    Pandit, Anita
    Zajac, Gregory J. M.
    Boehnke, Michael
    Abecasis, Goncalo R.
    Vrieze, Scott I.
    Zhan, Xiaowei
    Jiang, Bibo
    Liu, Dajiang J.
    PLOS GENETICS, 2018, 14 (07):
  • [46] Quantifying the robustness of primary analysis results: A case study on missing outcome data in pairwise and network meta-analysis
    Spineli, Loukia M.
    Kalyvas, Chrysostomos
    Papadimitropoulou, Katerina
    RESEARCH SYNTHESIS METHODS, 2021, 12 (04) : 475 - 490
  • [47] Allowing for uncertainty due to missing data in meta-analysis - Part 1: Two-stage methods
    White, Ian R.
    Higgins, Julian P. T.
    Wood, Angela M.
    STATISTICS IN MEDICINE, 2008, 27 (05) : 711 - 727
  • [48] Performance of selected imputation techniques for missing variances in meta-analysis
    Idris, N. R. N.
    Abdullah, M. H.
    Tolos, S. M.
    INTERNATIONAL CONFERENCE ON ADVANCEMENT IN SCIENCE AND TECHNOLOGY 2012 (ICAST): CONTEMPORARY MATHEMATICS, MATHEMATICAL PHYSICS AND THEIR APPLICATIONS, 2013, 435
  • [49] Assessment of Imputation Methods for Missing Gene Expression Data in Meta-Analysis of Distinct Cohorts of Tuberculosis Patients
    Bobak, Carly A.
    McDonnell, Lauren
    Nemesure, Matthew D.
    Lin, Justin
    Hill, Jane E.
    PACIFIC SYMPOSIUM ON BIOCOMPUTING 2020, 2020, : 307 - 318
  • [50] Imputation of systematically missing predictors in an individual participant data meta-analysis: a generalized approach using MICE
    Jolani, Shahab
    Debray, Thomas P. A.
    Koffijberg, Hendrik
    van Buuren, Stef
    Moons, Karel G. M.
    STATISTICS IN MEDICINE, 2015, 34 (11) : 1841 - 1863