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10.1016/j.cct.2018.02.011 http://scihub22266oqcxt.onion/10.1016/j.cct.2018.02.011 C5886309!5886309!29474933     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       Contemp+Clin+Trials 2018 ; 67 (ä): 116-20 Nephropedia Template TP {{tp|p=29474933|t=2018. An optimised multi-arm multi-stage clinical trial design for unknown variance |pdf=|usr=}}{{29474933}} gab.com Text An optimised multi-arm multi-stage clinical trial design for unknown variance JO: Contemp Clin Trials http://www.kidney.de/pget.php?&tw=1&pmid=29474933 #FOAvip Multi-arm multi-stage trial designs can bring notable gains in efficiency to the drug development process. However, for normally distributed endpoints, the determination of a design typically depends on the assumption that the patient variance in response is known. In practice, this will not usually be the case. To allow for unknown variance, previous research explored the performance of t-test statistics, coupled with a quantile substitution procedure for modifying the stopping boundaries, at controlling the familywise error-rate to the nominal level. Here, we discuss an alternative method based on Monte Carlo simulation that allows the group size and stopping boundaries of a multi-arm multi-stage t-test to be optimised, according to some nominated optimality criteria. We consider several examples, provide R code for general implementation, and show that our designs confer a familywise error-rate and power close to the desired level. Consequently, this methodology will provide utility in future multi-arm multi-stage trials. Twit Text FOAVip An optimised multi-arm multi-stage clinical trial design for unknown variance ????Contemp Clin Trials http://www.kidney.de/pget.php?&tw=1&pmid=29474933 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=29474933 #FOAvip English Wikipedia <ref>{{Cite pmid|29474933}}</ref> An optimised multi-arm multi-stage clinical trial design for unknown variance #MMPMID29474933 Grayling MJ; Wason JM; Mander AP Contemp Clin Trials 2018[Apr]; 67 (ä): 116-20 PMID29474933show ga Multi-arm multi-stage trial designs can bring notable gains in efficiency to the drug development process. However, for normally distributed endpoints, the determination of a design typically depends on the assumption that the patient variance in response is known. In practice, this will not usually be the case. To allow for unknown variance, previous research explored the performance of t-test statistics, coupled with a quantile substitution procedure for modifying the stopping boundaries, at controlling the familywise error-rate to the nominal level. Here, we discuss an alternative method based on Monte Carlo simulation that allows the group size and stopping boundaries of a multi-arm multi-stage t-test to be optimised, according to some nominated optimality criteria. We consider several examples, provide R code for general implementation, and show that our designs confer a familywise error-rate and power close to the desired level. Consequently, this methodology will provide utility in future multi-arm multi-stage trials. äAn optimised multi-arm multi-stage clinical trial design for unknown v(epmc).pdf DeepDyve Pubget Overpricing