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10.1002/sim.6165 http://scihub22266oqcxt.onion/10.1002/sim.6165 C4047140!4047140!24719270     free     free     free Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Stat+Med 2014 ; 33 (15): 2577-84 Nephropedia Template TP {{tp|p=24719270|t=2014. Assessing the incremental predictive performance of novel biomarkers over standard predictors |pdf=|usr=}}{{24719270}} gab.com Text Assessing the incremental predictive performance of novel biomarkers over standard predictors JO: Stat Med http://www.kidney.de/pget.php?&tw=1&pmid=24719270 #FOAvip It is unclear to what extent the incremental predictive performance of a novel biomarker is impacted by the method used to control for standard predictors. We investigated whether adding a biomarker to a model with a published risk score overestimates its incremental performance as compared to adding it to a multivariable model with individual predictors (or a composite risk score estimated from the sample of interest), and to a null model. We used 1000 simulated datasets (with a range of risk factor distributions and event rates) to compare these methods, using the continuous Net Reclassification Index (NRI), the Integrated Discrimination Index (IDI), and change in the C-statistic as discrimination metrics. The new biomarker was added to a: null model; model including a published risk score; model including a composite risk score estimated from the sample of interest; and multivariable model with individual predictors. We observed a gradient in the incremental performance of the biomarker, with the null model resulting in the highest predictive performance of the biomarker and the model using individual predictors resulting in the lowest (mean increases in C-statistic between models without and with the biomarker: 0.261, 0.085, 0.030, and 0.031; NRI: 0.767, 0.621, 0.513, and 0.530; IDI: 0.153, 0.093, 0.053 and 0.057, respectively). These findings were supported by Framingham Study data predicting atrial fibrillation using novel biomarkers. We recommend that authors report the effect of a new biomarker after controlling for standard predictors modeled as individual variables. Twit Text FOAVip Assessing the incremental predictive performance of novel biomarkers over standard predictors ????Stat Med http://www.kidney.de/pget.php?&tw=1&pmid=24719270 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=24719270 #FOAvip English Wikipedia <ref>{{Cite pmid|24719270}}</ref> Assessing the incremental predictive performance of novel biomarkers over standard predictors #MMPMID24719270 Xanthakis V; Sullivan LM; Vasan RS; Benjamin EJ; Massaro JM; D?Agostino RB; Pencina MJ Stat Med 2014[Jul]; 33 (15): 2577-84 PMID24719270show ga It is unclear to what extent the incremental predictive performance of a novel biomarker is impacted by the method used to control for standard predictors. We investigated whether adding a biomarker to a model with a published risk score overestimates its incremental performance as compared to adding it to a multivariable model with individual predictors (or a composite risk score estimated from the sample of interest), and to a null model. We used 1000 simulated datasets (with a range of risk factor distributions and event rates) to compare these methods, using the continuous Net Reclassification Index (NRI), the Integrated Discrimination Index (IDI), and change in the C-statistic as discrimination metrics. The new biomarker was added to a: null model; model including a published risk score; model including a composite risk score estimated from the sample of interest; and multivariable model with individual predictors. We observed a gradient in the incremental performance of the biomarker, with the null model resulting in the highest predictive performance of the biomarker and the model using individual predictors resulting in the lowest (mean increases in C-statistic between models without and with the biomarker: 0.261, 0.085, 0.030, and 0.031; NRI: 0.767, 0.621, 0.513, and 0.530; IDI: 0.153, 0.093, 0.053 and 0.057, respectively). These findings were supported by Framingham Study data predicting atrial fibrillation using novel biomarkers. We recommend that authors report the effect of a new biomarker after controlling for standard predictors modeled as individual variables. äAssessing the incremental predictive performance of novel biomarkers o(epmc).pdf DeepDyve Pubget Overpricing