Outlier detection and influence diagnostics in network meta-analysis.

Publisher: Wiley Blackwell Country of Publication: England NLM ID: 101543738 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1759-2887 (Electronic) Linking ISSN: 17592879 NLM ISO Abbreviation: Res Synth Methods Subsets: MEDLINE

Publication: : Chichester : Wiley Blackwell
Original Publication: Malden, MA : John Wiley & Sons, 2010-

Network Meta-Analysis* ; Normal Distribution* ; Research Design*; Hypertension/*drug therapy; Algorithms ; Antihypertensive Agents/pharmacology ; Bayes Theorem ; Bias ; Comparative Effectiveness Research ; Humans ; Models, Statistical ; Multivariate Analysis ; Odds Ratio ; Randomized Controlled Trials as Topic ; Reproducibility of Results ; Scientific Misconduct ; Treatment Outcome

Network meta-analysis has been gaining prominence as an evidence synthesis method that enables the comprehensive synthesis and simultaneous comparison of multiple treatments. In many network meta-analyses, some of the constituent studies may have markedly different characteristics from the others, and may be influential enough to change the overall results. The inclusion of these "outlying" studies might lead to biases, yielding misleading results. In this article, we propose effective methods for detecting outlying and influential studies in a frequentist framework. In particular, we propose suitable influence measures for network meta-analysis models that involve missing outcomes and adjust the degree of freedoms appropriately. We propose three influential measures by a leave-one-trial-out cross-validation scheme: (1) comparison-specific studentized residual, (2) relative change measure for covariance matrix of the comparative effectiveness parameters, (3) relative change measure for heterogeneity covariance matrix. We also propose (4) a model-based approach using a likelihood ratio statistic by a mean-shifted outlier detection model. We illustrate the effectiveness of the proposed methods via applications to a network meta-analysis of antihypertensive drugs. Using the four proposed methods, we could detect three potential influential trials involving an obvious outlier that was retracted because of data falsifications. We also demonstrate that the overall results of comparative efficacy estimates and the ranking of drugs were altered by omitting these three influential studies.
(© 2020 John Wiley & Sons Ltd.)

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JP17K19808 Japan Society for the Promotion of Science; JP19H04074 Japan Society for the Promotion of Science

Keywords: contrast-based model; influence diagnostics; multivariate meta-analysis; network meta-analysis; outlier detection

0 (Antihypertensive Agents)

Date Created: 20200913 Date Completed: 20210906 Latest Revision: 20210906

20240628

10.1002/jrsm.1455

32921002