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

ABSTRACT:

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. B

SUBMITTER: Yoon JH 

PROVIDER: S-EPMC8171049 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature