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10.1111/biom.12444 http://scihub22266oqcxt.onion/10.1111/biom.12444 C4870158!4870158!26575519     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 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Biometrics 2016 ; 72 (2): 584-95 Nephropedia Template TP {{tp|p=26575519|t=2016. Bayes and empirical Bayes methods for reduced rank regression models in matched case-control studies |pdf=|usr=}}{{26575519}} gab.com Text Bayes and empirical Bayes methods for reduced rank regression models in matched case-control studies JO: Biometrics http://www.kidney.de/pget.php?&tw=1&pmid=26575519 #FOAvip Matched case-control studies are popular designs used in epidemiology for assessing the effects of exposures on binary traits. Modern studies increasingly enjoy the ability to examine a large number of exposures in a comprehensive manner. However, several risk factors often tend to be related in a non-trivial way, undermining efforts to identify the risk factors using standard analytic methods due to inflated type I errors and possible masking of effects. Epidemiologists often use data reduction techniques by grouping the prognostic factors using a thematic approach, with themes deriving from biological considerations. We propose shrinkage type estimators based on Bayesian penalization methods to estimate the effects of the risk factors using these themes. The properties of the estimators are examined using extensive simulations. The methodology is illustrated using data from a matched case-control study of polychlorinflated biphenyls in relation to the etiology of non-Hodgkin?s lymphoma. Twit Text FOAVip Bayes and empirical Bayes methods for reduced rank regression models in matched case-control studies ????Biometrics http://www.kidney.de/pget.php?&tw=1&pmid=26575519 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26575519 #FOAvip English Wikipedia <ref>{{Cite pmid|26575519}}</ref> Bayes and empirical Bayes methods for reduced rank regression models in matched case-control studies #MMPMID26575519 Satagopan JM; Sen A; Zhou Q; Lan Q; Rothman N; Langseth H; Engel LS Biometrics 2016[Jun]; 72 (2): 584-95 PMID26575519show ga Matched case-control studies are popular designs used in epidemiology for assessing the effects of exposures on binary traits. Modern studies increasingly enjoy the ability to examine a large number of exposures in a comprehensive manner. However, several risk factors often tend to be related in a non-trivial way, undermining efforts to identify the risk factors using standard analytic methods due to inflated type I errors and possible masking of effects. Epidemiologists often use data reduction techniques by grouping the prognostic factors using a thematic approach, with themes deriving from biological considerations. We propose shrinkage type estimators based on Bayesian penalization methods to estimate the effects of the risk factors using these themes. The properties of the estimators are examined using extensive simulations. The methodology is illustrated using data from a matched case-control study of polychlorinflated biphenyls in relation to the etiology of non-Hodgkin?s lymphoma. äBayes and empirical Bayes methods for reduced rank regression models i(epmc).pdf DeepDyve Pubget Overpricing