Outlier Detection and Influence Diagnostics in Network Meta-Analysis

Autor/inn/en	Noma, Hisashi; Gosho, Masahiko; Ishii, Ryota; Oba, Koji; Furukawa, Toshi A.
Titel	Outlier Detection and Influence Diagnostics in Network Meta-Analysis
Quelle	In: Research Synthesis Methods, 11 (2020) 6, S.891-902 (12 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Noma, Hisashi)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1759-2879
DOI	10.1002/jrsm.1455
Schlagwörter	Networks; Meta Analysis; Evidence; Comparative Analysis; Outcomes of Treatment; Network Analysis; Medical Research; Models; Change; Hypertension; Drug Therapy + Suchen Sie Ihr Suchwort? Meta-analysis; Metaanalyse; Evidenz; Netzplantechnik; Analogiemodell; Wandel; Bluthochdruck; Hypertonie
Abstract	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 Using the four proposed methods, we could detect three potential influential trials involving an obvious outlier that was retracted because of data falsifications.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. (As Provided).
Anmerkungen	Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01