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10.1002/sim.5494 http://scihub22266oqcxt.onion/10.1002/sim.5494 C5410995!5410995!22806991     free     free     free Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 253.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\22806991.jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Stat+Med 2013 ; 32 (1): 51-66 Nephropedia Template TP {{tp|p=22806991|t=2013. A fully Bayesian application of the Copas selection model for publication bias extended to network meta-analysis |pdf=|usr=}}{{22806991}} gab.com Text A fully Bayesian application of the Copas selection model for publication bias extended to network meta-analysis JO: Stat Med http://www.kidney.de/pget.php?&tw=1&pmid=22806991 #FOAvip The Copas parametric model is aimed at exploring the potential impact of publication bias via sensitivity analysis, by making assumptions regarding the probability of publication of individual studies related to the standard error of their effect sizes. Reviewers often have prior assumptions about the extent of selection in the set of studies included in a meta-analysis. However, a Bayesian implementation of the Copas model has not been studied yet. We aim to present a Bayesian selection model for publication bias and to extend it to the case of network meta-analysis where each treatment is compared either to placebo or to a reference treatment creating a star-shaped network. We take advantage of the greater flexibility offered in the Bayesian context to incorporate in the model prior information on the extent and strength of selection. To derive prior distributions, we use both external data and an elicitation process of expert opinion. Twit Text FOAVip A fully Bayesian application of the Copas selection model for publication bias extended to network meta-analysis ????Stat Med http://www.kidney.de/pget.php?&tw=1&pmid=22806991 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=22806991 #FOAvip English Wikipedia <ref>{{Cite pmid|22806991}}</ref> A fully Bayesian application of the Copas selection model for publication bias extended to network meta-analysis #MMPMID22806991 Mavridis D; Sutton A; Cipriani A; Salanti G Stat Med 2013[Jan]; 32 (1): 51-66 PMID22806991show ga The Copas parametric model is aimed at exploring the potential impact of publication bias via sensitivity analysis, by making assumptions regarding the probability of publication of individual studies related to the standard error of their effect sizes. Reviewers often have prior assumptions about the extent of selection in the set of studies included in a meta-analysis. However, a Bayesian implementation of the Copas model has not been studied yet. We aim to present a Bayesian selection model for publication bias and to extend it to the case of network meta-analysis where each treatment is compared either to placebo or to a reference treatment creating a star-shaped network. We take advantage of the greater flexibility offered in the Bayesian context to incorporate in the model prior information on the extent and strength of selection. To derive prior distributions, we use both external data and an elicitation process of expert opinion. äA fully Bayesian application of the Copas selection model for publicat(epmc).pdf DeepDyve Pubget Overpricing