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10.1038/s41431-018-0159-6 http://scihub22266oqcxt.onion/10.1038/s41431-018-0159-6 C6057994!6057994!29706643     free     free     free Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Eur+J+Hum+Genet 2018 ; 26 (8): 1194-201 Nephropedia Template TP {{tp|p=29706643|t=2018. Are your covariates under control? How normalization can re-introduce covariate effects |pdf=|usr=}}{{29706643}} gab.com Text Are your covariates under control How normalization can re-introduce covariate effects JO: Eur J Hum Genet http://www.kidney.de/pget.php?&tw=1&pmid=29706643 #FOAvip Many statistical tests rely on the assumption that the residuals of a model are normally distributed. Rank-based inverse normal transformation (INT) of the dependent variable is one of the most popular approaches to satisfy the normality assumption. When covariates are included in the analysis, a common approach is to first adjust for the covariates and then normalize the residuals. This study investigated the effect of regressing covariates against the dependent variable and then applying rank-based INT to the residuals. The correlation between the dependent variable and covariates at each stage of processing was assessed. An alternative approach was tested in which rank-based INT was applied to the dependent variable before regressing covariates. Analyses based on both simulated and real data examples demonstrated that applying rank-based INT to the dependent variable residuals after regressing out covariates re-introduces a linear correlation between the dependent variable and covariates, increasing type-I errors and reducing power. On the other hand, when rank-based INT was applied prior to controlling for covariate effects, residuals were normally distributed and linearly uncorrelated with covariates. This latter approach is therefore recommended in situations were normality of the dependent variable is required. Twit Text FOAVip Are your covariates under control How normalization can re-introduce covariate effects ????Eur J Hum Genet http://www.kidney.de/pget.php?&tw=1&pmid=29706643 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=29706643 #FOAvip English Wikipedia <ref>{{Cite pmid|29706643}}</ref> Are your covariates under control? How normalization can re-introduce covariate effects #MMPMID29706643 Pain O; Dudbridge F; Ronald A Eur J Hum Genet 2018[Aug]; 26 (8): 1194-201 PMID29706643show ga Many statistical tests rely on the assumption that the residuals of a model are normally distributed. Rank-based inverse normal transformation (INT) of the dependent variable is one of the most popular approaches to satisfy the normality assumption. When covariates are included in the analysis, a common approach is to first adjust for the covariates and then normalize the residuals. This study investigated the effect of regressing covariates against the dependent variable and then applying rank-based INT to the residuals. The correlation between the dependent variable and covariates at each stage of processing was assessed. An alternative approach was tested in which rank-based INT was applied to the dependent variable before regressing covariates. Analyses based on both simulated and real data examples demonstrated that applying rank-based INT to the dependent variable residuals after regressing out covariates re-introduces a linear correlation between the dependent variable and covariates, increasing type-I errors and reducing power. On the other hand, when rank-based INT was applied prior to controlling for covariate effects, residuals were normally distributed and linearly uncorrelated with covariates. This latter approach is therefore recommended in situations were normality of the dependent variable is required. äAre your covariates under control? How normalization can re-introduce (epmc).pdf DeepDyve Pubget Overpricing