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10.1002/jrsm.1191 http://scihub22266oqcxt.onion/10.1002/jrsm.1191 C4964911!4964911!26610739     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Res+Synth+Methods 2016 ; 7 (3): 314-28 Nephropedia Template TP {{tp|p=26610739|t=2016. New models for describing outliers in meta?analysis Link |pdf=|usr=}}{{26610739}} gab.com Text New models for describing outliers in meta analysis Link JO: Res Synth Methods http://www.kidney.de/pget.php?&tw=1&pmid=26610739 #FOAvip An unobserved random effect is often used to describe the between?study variation that is apparent in meta?analysis datasets. A normally distributed random effect is conventionally used for this purpose. When outliers or other unusual estimates are included in the analysis, the use of alternative random effect distributions has previously been proposed. Instead of adopting the usual hierarchical approach to modelling between?study variation, and so directly modelling the study specific true underling effects, we propose two new marginal distributions for modelling heterogeneous datasets. These two distributions are suggested because numerical integration is not needed to evaluate the likelihood. This makes the computation required when fitting our models much more robust. The properties of the new distributions are described, and the methodology is exemplified by fitting models to four datasets. © 2015 The Authors. Research Synthesis Methods published by John Wiley & Sons, Ltd. Twit Text FOAVip New models for describing outliers in meta analysis Link ????Res Synth Methods http://www.kidney.de/pget.php?&tw=1&pmid=26610739 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26610739 #FOAvip English Wikipedia <ref>{{Cite pmid|26610739}}</ref> New models for describing outliers in meta?analysis Link #MMPMID26610739 Baker R; Jackson D Res Synth Methods 2016[Sep]; 7 (3): 314-28 PMID26610739show ga An unobserved random effect is often used to describe the between?study variation that is apparent in meta?analysis datasets. A normally distributed random effect is conventionally used for this purpose. When outliers or other unusual estimates are included in the analysis, the use of alternative random effect distributions has previously been proposed. Instead of adopting the usual hierarchical approach to modelling between?study variation, and so directly modelling the study specific true underling effects, we propose two new marginal distributions for modelling heterogeneous datasets. These two distributions are suggested because numerical integration is not needed to evaluate the likelihood. This makes the computation required when fitting our models much more robust. The properties of the new distributions are described, and the methodology is exemplified by fitting models to four datasets. © 2015 The Authors. Research Synthesis Methods published by John Wiley & Sons, Ltd. äNew models for describing outliers in meta?analysis Link (epmc).pdf DeepDyve Pubget Overpricing