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10.1002/sim.6457 http://scihub22266oqcxt.onion/10.1002/sim.6457 C4420688!4420688 !25736915     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 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\25736915 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Stat+Med 2015 ; 34 (12 ): 2035-47 Nephropedia Template TP {{tp|p=25736915 |t=2015. Detecting treatment-covariate interactions using permutation methods |pdf=|usr=}}{{25736915 }} gab.com Text Detecting treatment-covariate interactions using permutation methods JO: Stat Med http://www.kidney.de/pget.php?&tw=1&pmid=25736915 #FOAvip The primary objective of a Randomized Clinical Trial usually is to investigate whether one treatment is better than its alternatives on average. However, treatment effects may vary across different patient subpopulations. In contrast to demonstrating one treatment is superior to another on the average sense, one is often more concerned with the question that, for a particular patient, or a group of patients with similar characteristics, which treatment strategy is most appropriate to achieve a desired outcome. Various interaction tests have been proposed to detect treatment effect heterogeneity; however, they typically examine covariates one at a time, do not offer an integrated approach that incorporates all available information, and can greatly increase the chance of a false positive finding when the number of covariates is large. We propose a new permutation test for the null hypothesis of no interaction effects for any covariate. The proposed test allows us to consider the interaction effects of many covariates simultaneously without having to group subjects into subsets based on pre-specified criteria and applies generally to randomized clinical trials of multiple treatments. The test provides an attractive alternative to the standard likelihood ratio test, especially when the number of covariates is large. We illustrate the proposed methods using a dataset from the Treatment of Adolescents with Depression Study. Twit Text FOAVip Detecting treatment-covariate interactions using permutation methods ????Stat Med http://www.kidney.de/pget.php?&tw=1&pmid=25736915 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=25736915 #FOAvip English Wikipedia <ref>{{Cite pmid|25736915 }}</ref> Detecting treatment-covariate interactions using permutation methods #MMPMID25736915 Wang R ; Schoenfeld DA ; Hoeppner B ; Evins AE Stat Med 2015[May]; 34 (12 ): 2035-47 PMID25736915 show ga The primary objective of a Randomized Clinical Trial usually is to investigate whether one treatment is better than its alternatives on average. However, treatment effects may vary across different patient subpopulations. In contrast to demonstrating one treatment is superior to another on the average sense, one is often more concerned with the question that, for a particular patient, or a group of patients with similar characteristics, which treatment strategy is most appropriate to achieve a desired outcome. Various interaction tests have been proposed to detect treatment effect heterogeneity; however, they typically examine covariates one at a time, do not offer an integrated approach that incorporates all available information, and can greatly increase the chance of a false positive finding when the number of covariates is large. We propose a new permutation test for the null hypothesis of no interaction effects for any covariate. The proposed test allows us to consider the interaction effects of many covariates simultaneously without having to group subjects into subsets based on pre-specified criteria and applies generally to randomized clinical trials of multiple treatments. The test provides an attractive alternative to the standard likelihood ratio test, especially when the number of covariates is large. We illustrate the proposed methods using a dataset from the Treatment of Adolescents with Depression Study. |*Cognitive Behavioral Therapy [MESH] |Adolescent [MESH] |Analysis of Variance [MESH] |Antidepressive Agents, Second-Generation/adverse effects/therapeutic use [MESH] |Bias [MESH] |Clinical Decision-Making/*methods [MESH] |Combined Modality Therapy [MESH] |Computer Simulation [MESH] |Confounding Factors, Epidemiologic [MESH] |Data Interpretation, Statistical [MESH] |Depressive Disorder, Major/*therapy [MESH] |Fluoxetine/*adverse effects/therapeutic use [MESH] |Humans [MESH] |Linear Models [MESH] |Precision Medicine/*methods [MESH] |Randomized Controlled Trials as Topic/methods/*statistics & numerical data [MESH] |Research Design [MESH]Detecting treatment-covariate interactions using permutation methods (epmc).pdf DeepDyve Pubget Overpricing