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10.1073/pnas.1518285112 http://scihub22266oqcxt.onion/10.1073/pnas.1518285112 C4653162!4653162 !26504198     free     free     free Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 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\26504198 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Proc+Natl+Acad+Sci+U+S+A 2015 ; 112 (45 ): 13892-7 Nephropedia Template TP {{tp|p=26504198 |t=2015. Why significant variables aren t automatically good predictors |pdf=|usr=}}{{26504198 }} gab.com Text Why significant variables aren t automatically good predictors JO: Proc Natl Acad Sci U S A http://www.kidney.de/pget.php?&tw=1&pmid=26504198 #FOAvip Thus far, genome-wide association studies (GWAS) have been disappointing in the inability of investigators to use the results of identified, statistically significant variants in complex diseases to make predictions useful for personalized medicine. Why are significant variables not leading to good prediction of outcomes? We point out that this problem is prevalent in simple as well as complex data, in the sciences as well as the social sciences. We offer a brief explanation and some statistical insights on why higher significance cannot automatically imply stronger predictivity and illustrate through simulations and a real breast cancer example. We also demonstrate that highly predictive variables do not necessarily appear as highly significant, thus evading the researcher using significance-based methods. We point out that what makes variables good for prediction versus significance depends on different properties of the underlying distributions. If prediction is the goal, we must lay aside significance as the only selection standard. We suggest that progress in prediction requires efforts toward a new research agenda of searching for a novel criterion to retrieve highly predictive variables rather than highly significant variables. We offer an alternative approach that was not designed for significance, the partition retention method, which was very effective predicting on a long-studied breast cancer data set, by reducing the classification error rate from 30% to 8%. Twit Text FOAVip Why significant variables aren t automatically good predictors ????Proc Natl Acad Sci U S A http://www.kidney.de/pget.php?&tw=1&pmid=26504198 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26504198 #FOAvip English Wikipedia <ref>{{Cite pmid|26504198 }}</ref> Why significant variables aren t automatically good predictors #MMPMID26504198 Lo A ; Chernoff H ; Zheng T ; Lo SH Proc Natl Acad Sci U S A 2015[Nov]; 112 (45 ): 13892-7 PMID26504198 show ga Thus far, genome-wide association studies (GWAS) have been disappointing in the inability of investigators to use the results of identified, statistically significant variants in complex diseases to make predictions useful for personalized medicine. Why are significant variables not leading to good prediction of outcomes? We point out that this problem is prevalent in simple as well as complex data, in the sciences as well as the social sciences. We offer a brief explanation and some statistical insights on why higher significance cannot automatically imply stronger predictivity and illustrate through simulations and a real breast cancer example. We also demonstrate that highly predictive variables do not necessarily appear as highly significant, thus evading the researcher using significance-based methods. We point out that what makes variables good for prediction versus significance depends on different properties of the underlying distributions. If prediction is the goal, we must lay aside significance as the only selection standard. We suggest that progress in prediction requires efforts toward a new research agenda of searching for a novel criterion to retrieve highly predictive variables rather than highly significant variables. We offer an alternative approach that was not designed for significance, the partition retention method, which was very effective predicting on a long-studied breast cancer data set, by reducing the classification error rate from 30% to 8%. |*Genome-Wide Association Study [MESH] |Humans [MESH] |Precision Medicine [MESH]Why significant variables aren t automatically good predictors (epmc).pdf DeepDyve Pubget Overpricing