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10.1515/1557-4679.1423 http://scihub22266oqcxt.onion/10.1515/1557-4679.1423 C6038934!6038934!22850073     free     free     free Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Int+J+Biostat 2012 ; 8 (1): 18 Nephropedia Template TP {{tp|p=22850073|t=2012. Statistical Issues and Limitations in Personalized Medicine Research with Clinical Trials |pdf=|usr=}}{{22850073}} gab.com Text Statistical Issues and Limitations in Personalized Medicine Research with Clinical Trials JO: Int J Biostat http://www.kidney.de/pget.php?&tw=1&pmid=22850073 #FOAvip We discuss using clinical trial data to construct and evaluate rules that use baseline covariates to assign different treatments to different patients. Given such a candidate personalization rule, we first note that its performance can often be evaluated without actually applying the rule to subjects, and a class of estimators is characterized from a statistical efficiency standpoint. We also point out a recently noted reduction of the rule construction problem to a classification task and extend results in this direction. Together these facts suggest a natural form of cross-validation in which a personalized medicine rule can be constructed from clinical trial data using standard classification tools and then evaluated in a replicated trial. Because replication is often required by the FDA to provide evidence of safety and efficacy before pharmaceutical drugs can be marketed, there are abundant data with which to explore the potential benefits of more tailored therapy. We constructed and evaluated personalized medicine rules using simulations based on two active-controlled randomized clinical trials of antibacterial drugs for the treatment of skin and skin structure infections. Unfortunately we present negative results that did not suggest benefit from personalization. We discuss the implications of this finding and why statistical approaches to personalized medicine problems will often face difficult challenges. Twit Text FOAVip Statistical Issues and Limitations in Personalized Medicine Research with Clinical Trials ????Int J Biostat http://www.kidney.de/pget.php?&tw=1&pmid=22850073 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=22850073 #FOAvip English Wikipedia <ref>{{Cite pmid|22850073}}</ref> Statistical Issues and Limitations in Personalized Medicine Research with Clinical Trials #MMPMID22850073 Rubin DB; van der Laan MJ Int J Biostat 2012[Jul]; 8 (1): 18 PMID22850073show ga We discuss using clinical trial data to construct and evaluate rules that use baseline covariates to assign different treatments to different patients. Given such a candidate personalization rule, we first note that its performance can often be evaluated without actually applying the rule to subjects, and a class of estimators is characterized from a statistical efficiency standpoint. We also point out a recently noted reduction of the rule construction problem to a classification task and extend results in this direction. Together these facts suggest a natural form of cross-validation in which a personalized medicine rule can be constructed from clinical trial data using standard classification tools and then evaluated in a replicated trial. Because replication is often required by the FDA to provide evidence of safety and efficacy before pharmaceutical drugs can be marketed, there are abundant data with which to explore the potential benefits of more tailored therapy. We constructed and evaluated personalized medicine rules using simulations based on two active-controlled randomized clinical trials of antibacterial drugs for the treatment of skin and skin structure infections. Unfortunately we present negative results that did not suggest benefit from personalization. We discuss the implications of this finding and why statistical approaches to personalized medicine problems will often face difficult challenges. äStatistical Issues and Limitations in Personalized Medicine Research w(epmc).pdf DeepDyve Pubget Overpricing