A method for assessing robustness of the results of a star-shaped network meta-analysis under the unidentifiable consistency assumption.

Szczegóły
Abstrakt

Tytuł:: A method for assessing robustness of the results of a star-shaped network meta-analysis under the unidentifiable consistency assumption.
Autorzy:: Yoon JH; Interdisciplinary Program in Medical Informatics, Seoul National University College of Medicine, Seoul, South Korea.; Institute of Health Policy and Management, Medical Research Center, Seoul National University, Seoul, South Korea.
Dias S; Centre for Reviews and Dissemination, University of York, York, UK.
Hahn S; Institute of Health Policy and Management, Medical Research Center, Seoul National University, Seoul, South Korea. .; Department of Human Systems Medicine, Medical Statistics Laboratory, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea. .
Źródło:: BMC medical research methodology [BMC Med Res Methodol] 2021 Jun 01; Vol. 21 (1), pp. 113. Date of Electronic Publication: 2021 Jun 01.
Typ publikacji:: Journal Article; Meta-Analysis; Research Support, Non-U.S. Gov't
Język:: English
Imprint Name(s):: Original Publication: London : BioMed Central, [2001-
MeSH Terms:: Network Meta-Analysis*
Computer Simulation ; Humans ; Reproducibility of Results
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Grant Information:: HI19C1178 Ministry of Health and Welfare
Contributed Indexing:: Keywords: Data imputation; Inconsistency; Indirect comparisons; Network meta-analysis; Sensitivity analysis; Star-shaped network
Entry Date(s):: Date Created: 20210602 Date Completed: 20210628 Latest Revision: 20210628
Update Code:: 20240104
PubMed Central ID:: PMC8171049
DOI:: 10.1186/s12874-021-01290-1
PMID:: 34074239
: Czasopismo naukowe

Pełny tekst

Background: In a star-shaped network, pairwise comparisons link treatments with a reference treatment (often placebo or standard care), but not with each other. Thus, comparisons between non-reference treatments rely on indirect evidence, and are based on the unidentifiable consistency assumption, limiting the reliability of the results. We suggest a method of performing a sensitivity analysis through data imputation to assess the robustness of results with an unknown degree of inconsistency.
Methods: The method involves imputation of data for randomized controlled trials comparing non-reference treatments, to produce a complete network. The imputed data simulate a situation that would allow mixed treatment comparison, with a statistically acceptable extent of inconsistency. By comparing the agreement between the results obtained from the original star-shaped network meta-analysis and the results after incorporating the imputed data, the robustness of the results of the original star-shaped network meta-analysis can be quantified and assessed. To illustrate this method, we applied it to two real datasets and some simulated datasets.
Results: Applying the method to the star-shaped network formed by discarding all comparisons between non-reference treatments from a real complete network, 33% of the results from the analysis incorporating imputed data under acceptable inconsistency indicated that the treatment ranking would be different from the ranking obtained from the star-shaped network. Through a simulation study, we demonstrated the sensitivity of the results after data imputation for a star-shaped network with different levels of within- and between-study variability. An extended usability of the method was also demonstrated by another example where some head-to-head comparisons were incorporated.
Conclusions: Our method will serve as a practical technique to assess the reliability of results from a star-shaped network meta-analysis under the unverifiable consistency assumption.

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