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To justify evidence synthesis, consistency is an important assumption; however, existing methods founded on statistical testing can be substantially limited in statistical power or have several drawbacks when handling multi\u2010arm studies. Moreover, inconsistency can be theoretically explained as design\u2010by\u2010treatment interactions, and the primary purpose of such analyses is to prioritize the further investigation of specific \u201cdesigns\u201d to explore sources of bias and other issues that might influence the overall results. In this article, we propose an alternative framework for evaluating inconsistency using influence diagnostics methods, which enable the influence of individual designs on the overall results to be quantitatively evaluated. We provide four new methods, the averaged studentized residual, MDFFITS, , and , to quantify the influence of individual designs through a \u201cleave\u2010one\u2010design\u2010out\u201d analysis framework. We also propose a simple summary measure, the\n                    <jats:italic>O<\/jats:italic>\n                    \u2010value, for prioritizing designs and interpreting these influential analyses in a straightforward manner. Furthermore, we propose another testing approach based on the leave\u2010one\u2010design\u2010out analysis framework. By applying the new methods to a network meta\u2010analysis of antihypertensive drugs and performing simulation studies, we demonstrate that the new methods accurately located potential sources of inconsistency. The proposed methods provide new insights into alternatives to existing test\u2010based methods, especially the quantification of the influence of individual designs on the overall network meta\u2010analysis results.\n                  <\/jats:p>","DOI":"10.1002\/sim.70614","type":"journal-article","created":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T02:04:27Z","timestamp":1779242667000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Influence Analyses of \u201cDesigns\u201d for Evaluating Inconsistency in Network Meta\u2010Analysis"],"prefix":"10.1002","volume":"45","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-3081-9438","authenticated-orcid":false,"given":"Kotaro","family":"Sasaki","sequence":"first","affiliation":[{"name":"The Graduate Institute for Advanced Studies The Graduate University for Advanced Studies (SOKENDAI)  Tokyo Japan"},{"name":"Human Biology Integration Foundation Deep Human Biology Learning, Eisai Co. 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