Abstract:
BACKGROUND:Network meta-analysis can be used to combine results from several randomized trials involving more than two treatments. Potential inconsistency among different types of trial (designs) differing in the set of treatments tested is a major challenge, and application of procedures for detecting and locating inconsistency in trial networks is a key step in the conduct of such analyses. METHODS:Network meta-analysis can be very conveniently performed using factorial analysis-of-variance methods. Inconsistency can be scrutinized by inspecting the design × treatment interaction. This approach is in many ways simpler to implement than the more common approach of using treatment-versus-control contrasts. RESULTS:We show that standard regression diagnostics available in common linear mixed model packages can be used to detect and locate inconsistency in trial networks. Moreover, a suitable definition of factors and effects allows devising significance tests for inconsistency. CONCLUSION:Factorial analysis of variance provides a convenient framework for conducting network meta-analysis, including diagnostic checks for inconsistency.
journal_name
BMC Med Res Methodoljournal_title
BMC medical research methodologyauthors
Piepho HPdoi
10.1186/1471-2288-14-61subject
Has Abstractpub_date
2014-05-10 00:00:00pages
61issn
1471-2288pii
1471-2288-14-61journal_volume
14pub_type
杂志文章abstract:BACKGROUND:Systematic reviews (SRs) must be of high quality. The purpose of our research was to compare the methodological and reporting quality of original versus updated Cochrane SRs to determine whether updating had improved these two quality dimensions. METHODS:We identified updated Cochrane SRs published in issue...
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