Performing Arm-Based Network Meta-Analysis in R with the pcnetmeta Package

被引:94
|
作者
Lin, Lifeng [1 ]
Zhang, Jing [2 ]
Hodges, James S. [1 ]
Chu, Haitao [1 ]
机构
[1] Univ Minnesota, Sch Publ Hlth, Div Biostat, Minneapolis, MN 55455 USA
[2] Univ Maryland, Sch Publ Hlth, Dept Epidemiol & Biostat, College Pk, MD 20740 USA
来源
JOURNAL OF STATISTICAL SOFTWARE | 2017年 / 80卷 / 05期
关键词
absolute effect; arm-based method; Bayesian inference; network meta-analysis; CHAIN MONTE-CARLO; CLINICAL-TRIALS; OUTCOMES; INCONSISTENCY; FRAMEWORK; ABSOLUTE; MODELS; EVENT;
D O I
10.18637/jss.v080.i05
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Network meta-analysis is a powerful approach for synthesizing direct and indirect evidence about multiple treatment comparisons from a collection of independent studies. At present, the most widely used method in network meta-analysis is contrast-based, in which a baseline treatment needs to be specified in each study, and the analysis focuses on modeling relative treatment effects (typically log odds ratios). However, population-averaged treatment-specific parameters, such as absolute risks, cannot be estimated by this method without an external data source or a separate model for a reference treatment. Recently, an arm-based network meta-analysis method has been proposed, and the R package pcnetmeta provides user-friendly functions for its implementation. This package estimates both absolute and relative effects, and can handle binary, continuous, and count outcomes.
引用
收藏
页码:1 / 25
页数:25
相关论文
共 50 条
  • [21] The R Package metaLik for Likelihood Inference in Meta-Analysis
    Guolo, Annamaria
    Varin, Cristiano
    [J]. JOURNAL OF STATISTICAL SOFTWARE, 2012, 50 (07): : 1 - 14
  • [22] metaplus: An R Package for the Analysis of Robust Meta-Analysis and Meta-Regression
    Beath, Ken J.
    [J]. R JOURNAL, 2016, 8 (01): : 5 - 16
  • [23] The impact of covariance priors on arm-based Bayesian network meta-analyses with binary outcomes
    Wang, Zhenxun
    Lin, Lifeng
    Hodges, James S.
    Chu, Haitao
    [J]. STATISTICS IN MEDICINE, 2020, 39 (22) : 2883 - 2900
  • [24] IMPLEMENTATION OF CONTRAST-BASED AND SHARED PARAMETER MODELS IN BUGSNET, AN R PACKAGE FOR CONDUCTING BAYESIAN NETWORK META-ANALYSIS
    Wigle, A.
    Pollock, R. F.
    Beliveau, A.
    [J]. VALUE IN HEALTH, 2022, 25 (07) : S531 - S531
  • [25] Visualizing assumptions and results in network meta-analysis: The network graphs package
    Chaimani, Anna
    Salanti, Georgia
    [J]. STATA JOURNAL, 2015, 15 (04): : 905 - 950
  • [26] bspmma: An R Package for Bayesian Semiparametric Models for Meta-Analysis
    Burr, Deborah
    [J]. JOURNAL OF STATISTICAL SOFTWARE, 2012, 50 (04): : 1 - 23
  • [27] rqt: an R package for gene-level meta-analysis
    Zhbannikov, Ilya Y.
    Arbeev, Konstantin G.
    Yashin, Anatoliy I.
    [J]. BIOINFORMATICS, 2017, 33 (19) : 3129 - 3130
  • [28] HyperCo: Optimizing Network Performance in ARM-Based Mobile Virtualization
    Yao, Jianguo
    Deng, Ting
    Liu, Xue
    Jacobsen, Hans-Arno
    Guan, Haibing
    [J]. IEEE TRANSACTIONS ON SERVICES COMPUTING, 2019, 12 (01) : 131 - 143
  • [29] Nonparametric estimation of the random effects distribution for the risk or rate ratio in rare events meta-analysis with the arm-based and contrast-based approaches
    Sangnawakij, Patarawan
    Bohning, Dankmar
    Holling, Heinz
    Jansen, Katrin
    [J]. STATISTICS IN MEDICINE, 2024, 43 (04) : 706 - 730
  • [30] Multiple moderator meta-analysis using the R-package Meta-CART
    Li, Xinru
    Dusseldorp, Elise
    Su, Xiaogang
    Meulman, Jacqueline J.
    [J]. BEHAVIOR RESEARCH METHODS, 2020, 52 (06) : 2657 - 2673