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10.1038/bjc.2015.278 http://scihub22266oqcxt.onion/10.1038/bjc.2015.278 C4559835!4559835!26263479     free     free     free Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Br+J+Cancer 2015 ; 113 (5): 699-705 Nephropedia Template TP {{tp|p=26263479|t=2015. A Bayesian adaptive design for biomarker trials with linked treatments |pdf=|usr=}}{{26263479}} gab.com Text A Bayesian adaptive design for biomarker trials with linked treatments JO: Br J Cancer http://www.kidney.de/pget.php?&tw=1&pmid=26263479 #FOAvip Background:: Response to treatments is highly heterogeneous in cancer. Increased availability of biomarkers and targeted treatments has led to the need for trial designs that efficiently test new treatments in biomarker-stratified patient subgroups. Methods:: We propose a novel Bayesian adaptive randomisation (BAR) design for use in multi-arm phase II trials where biomarkers exist that are potentially predictive of a linked treatment's effect. The design is motivated in part by two phase II trials that are currently in development. The design starts by randomising patients to the control treatment or to experimental treatments that the biomarker profile suggests should be active. At interim analyses, data from treated patients are used to update the allocation probabilities. If the linked treatments are effective, the allocation remains high; if ineffective, the allocation changes over the course of the trial to unlinked treatments that are more effective. Results:: Our proposed design has high power to detect treatment effects if the pairings of treatment with biomarker are correct, but also performs well when alternative pairings are true. The design is consistently more powerful than parallel-groups stratified trials. Conclusions:: This BAR design is a powerful approach to use when there are pairings of biomarkers with treatments available for testing simultaneously. Twit Text FOAVip A Bayesian adaptive design for biomarker trials with linked treatments ????Br J Cancer http://www.kidney.de/pget.php?&tw=1&pmid=26263479 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26263479 #FOAvip English Wikipedia <ref>{{Cite pmid|26263479}}</ref> A Bayesian adaptive design for biomarker trials with linked treatments #MMPMID26263479 Wason JMS; Abraham JE; Baird RD; Gournaris I; Vallier AL; Brenton JD; Earl HM; Mander AP Br J Cancer 2015[Sep]; 113 (5): 699-705 PMID26263479show ga Background:: Response to treatments is highly heterogeneous in cancer. Increased availability of biomarkers and targeted treatments has led to the need for trial designs that efficiently test new treatments in biomarker-stratified patient subgroups. Methods:: We propose a novel Bayesian adaptive randomisation (BAR) design for use in multi-arm phase II trials where biomarkers exist that are potentially predictive of a linked treatment's effect. The design is motivated in part by two phase II trials that are currently in development. The design starts by randomising patients to the control treatment or to experimental treatments that the biomarker profile suggests should be active. At interim analyses, data from treated patients are used to update the allocation probabilities. If the linked treatments are effective, the allocation remains high; if ineffective, the allocation changes over the course of the trial to unlinked treatments that are more effective. Results:: Our proposed design has high power to detect treatment effects if the pairings of treatment with biomarker are correct, but also performs well when alternative pairings are true. The design is consistently more powerful than parallel-groups stratified trials. Conclusions:: This BAR design is a powerful approach to use when there are pairings of biomarkers with treatments available for testing simultaneously. äA Bayesian adaptive design for biomarker trials with linked treatments(epmc).pdf DeepDyve Pubget Overpricing