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10.1177/0962280215597716 http://scihub22266oqcxt.onion/10.1177/0962280215597716 C5395089!5395089 !26265771     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=26265771 &cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215 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 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\26265771 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Stat+Methods+Med+Res 2017 ; 26 (5 ): 2376-2388 Nephropedia Template TP {{tp|p=26265771 |t=2017. Unbiased estimation for response adaptive clinical trials |pdf=|usr=}}{{26265771 }} gab.com Text Unbiased estimation for response adaptive clinical trials JO: Stat Methods Med Res http://www.kidney.de/pget.php?&tw=1&pmid=26265771 #FOAvip Bayesian adaptive trials have the defining feature that the probability of randomization to a particular treatment arm can change as information becomes available as to its true worth. However, there is still a general reluctance to implement such designs in many clinical settings. One area of concern is that their frequentist operating characteristics are poor or, at least, poorly understood. We investigate the bias induced in the maximum likelihood estimate of a response probability parameter, p, for binary outcome by the process of adaptive randomization. We discover that it is small in magnitude and, under mild assumptions, can only be negative - causing one's estimate to be closer to zero on average than the truth. A simple unbiased estimator for p is obtained, but it is shown to have a large mean squared error. Two approaches are therefore explored to improve its precision based on inverse probability weighting and Rao-Blackwellization. We illustrate these estimation strategies using two well-known designs from the literature. Twit Text FOAVip Unbiased estimation for response adaptive clinical trials ????Stat Methods Med Res http://www.kidney.de/pget.php?&tw=1&pmid=26265771 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26265771 #FOAvip English Wikipedia <ref>{{Cite pmid|26265771 }}</ref> Unbiased estimation for response adaptive clinical trials #MMPMID26265771 Bowden J ; Trippa L Stat Methods Med Res 2017[Oct]; 26 (5 ): 2376-2388 PMID26265771 show ga Bayesian adaptive trials have the defining feature that the probability of randomization to a particular treatment arm can change as information becomes available as to its true worth. However, there is still a general reluctance to implement such designs in many clinical settings. One area of concern is that their frequentist operating characteristics are poor or, at least, poorly understood. We investigate the bias induced in the maximum likelihood estimate of a response probability parameter, p, for binary outcome by the process of adaptive randomization. We discover that it is small in magnitude and, under mild assumptions, can only be negative - causing one's estimate to be closer to zero on average than the truth. A simple unbiased estimator for p is obtained, but it is shown to have a large mean squared error. Two approaches are therefore explored to improve its precision based on inverse probability weighting and Rao-Blackwellization. We illustrate these estimation strategies using two well-known designs from the literature. |*Bias [MESH] |Adaptive Clinical Trials as Topic/*methods [MESH] |Bayes Theorem [MESH] |Data Interpretation, Statistical [MESH] |Humans [MESH] |Likelihood Functions [MESH]Unbiased estimation for response adaptive clinical trials (epmc).pdf DeepDyve Pubget Overpricing