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10.1186/1471-2288-14-61 http://scihub22266oqcxt.onion/10.1186/1471-2288-14-61 C4049370!4049370!24885590     free     free     free Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 243.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       BMC+Med+Res+Methodol 2014 ; 14 (ä): 61 Nephropedia Template TP {{tp|p=24885590|t=2014. Network-meta analysis made easy: detection of inconsistency using factorial analysis-of-variance models |pdf=|usr=}}{{24885590}} gab.com Text Network-meta analysis made easy: detection of inconsistency using factorial analysis-of-variance models JO: BMC Med Res Methodol http://www.kidney.de/pget.php?&tw=1&pmid=24885590 #FOAvip 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. Twit Text FOAVip Network-meta analysis made easy: detection of inconsistency using factorial analysis-of-variance models ????BMC Med Res Methodol http://www.kidney.de/pget.php?&tw=1&pmid=24885590 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=24885590 #FOAvip English Wikipedia <ref>{{Cite pmid|24885590}}</ref> Network-meta analysis made easy: detection of inconsistency using factorial analysis-of-variance models #MMPMID24885590 Piepho HP BMC Med Res Methodol 2014[]; 14 (ä): 61 PMID24885590show ga 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. äNetwork-meta analysis made easy: detection of inconsistency using fact(epmc).pdf DeepDyve Pubget Overpricing