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10.1214/14-STS504 http://scihub22266oqcxt.onion/10.1214/14-STS504 C4856206!4856206!27158186     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Stat+Sci 2015 ; 30 (2): 199-215 Nephropedia Template TP {{tp|p=27158186|t=2015. Multi-armed Bandit Models for the Optimal Design of Clinical Trials: Benefits and Challenges |pdf=|usr=}}{{27158186}} gab.com Text Multi-armed Bandit Models for the Optimal Design of Clinical Trials: Benefits and Challenges JO: Stat Sci http://www.kidney.de/pget.php?&tw=1&pmid=27158186 #FOAvip Multi-armed bandit problems (MABPs) are a special type of optimal control problem well suited to model resource allocation under uncertainty in a wide variety of contexts. Since the first publication of the optimal solution of the classic MABP by a dynamic index rule, the bandit literature quickly diversified and emerged as an active research topic. Across this literature, the use of bandit models to optimally design clinical trials became a typical motivating application, yet little of the resulting theory has ever been used in the actual design and analysis of clinical trials. To this end, we review two MABP decision-theoretic approaches to the optimal allocation of treatments in a clinical trial: the infinite-horizon Bayesian Bernoulli MABP and the finite-horizon variant. These models possess distinct theoretical properties and lead to separate allocation rules in a clinical trial design context. We evaluate their performance compared to other allocation rules, including fixed randomization. Our results indicate that bandit approaches offer significant advantages, in terms of assigning more patients to better treatments, and severe limitations, in terms of their resulting statistical power. We propose a novel bandit-based patient allocation rule that overcomes the issue of low power, thus removing a potential barrier for their use in practice. Twit Text FOAVip Multi-armed Bandit Models for the Optimal Design of Clinical Trials: Benefits and Challenges ????Stat Sci http://www.kidney.de/pget.php?&tw=1&pmid=27158186 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=27158186 #FOAvip English Wikipedia <ref>{{Cite pmid|27158186}}</ref> Multi-armed Bandit Models for the Optimal Design of Clinical Trials: Benefits and Challenges #MMPMID27158186 Villar SS; Bowden J; Wason J Stat Sci 2015[]; 30 (2): 199-215 PMID27158186show ga Multi-armed bandit problems (MABPs) are a special type of optimal control problem well suited to model resource allocation under uncertainty in a wide variety of contexts. Since the first publication of the optimal solution of the classic MABP by a dynamic index rule, the bandit literature quickly diversified and emerged as an active research topic. Across this literature, the use of bandit models to optimally design clinical trials became a typical motivating application, yet little of the resulting theory has ever been used in the actual design and analysis of clinical trials. To this end, we review two MABP decision-theoretic approaches to the optimal allocation of treatments in a clinical trial: the infinite-horizon Bayesian Bernoulli MABP and the finite-horizon variant. These models possess distinct theoretical properties and lead to separate allocation rules in a clinical trial design context. We evaluate their performance compared to other allocation rules, including fixed randomization. Our results indicate that bandit approaches offer significant advantages, in terms of assigning more patients to better treatments, and severe limitations, in terms of their resulting statistical power. We propose a novel bandit-based patient allocation rule that overcomes the issue of low power, thus removing a potential barrier for their use in practice. äMulti-armed Bandit Models for the Optimal Design of Clinical Trials: B(epmc).pdf DeepDyve Pubget Overpricing