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10.1002/sim.6443 http://scihub22266oqcxt.onion/10.1002/sim.6443 C4390488!4390488!25626676     free     free     free Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Stat+Med 2015 ; 34 (10): 1721-32 Nephropedia Template TP {{tp|p=25626676|t=2015. A Bayesian Dose-finding Design for Phase I/II Clinical Trials with Non-ignorable Dropouts |pdf=|usr=}}{{25626676}} gab.com Text A Bayesian Dose-finding Design for Phase I/II Clinical Trials with Non-ignorable Dropouts JO: Stat Med http://www.kidney.de/pget.php?&tw=1&pmid=25626676 #FOAvip Phase I/II trials utilize both toxicity and efficacy data to achieve efficient dose finding. However, due to the requirement of assessing efficacy outcome, which often takes a long period of time to be evaluated, the duration of phase I/II trials is often longer than that of the conventional dose-finding trials. As a result, phase I/II trials are susceptible to the missing data problem caused by patient dropout, and the missing efficacy outcomes are often non-ignorable in the sense that patients who do not experience treatment efficacy are more likely to drop out of the trial. We propose a Bayesian phase I/II trial design to accommodate non-ignorable dropouts. We treat toxicity as a binary outcome and efficacy as a time-to-event outcome. We model the marginal distribution of toxicity using a logistic regression and jointly model the times to efficacy and dropout using proportional hazard models to adjust for non-ignorable dropouts. The correlation between times to efficacy and dropout is modeled using a shared frailty. We propose a two-stage dose-finding algorithm to adaptively assign patients to desirable doses. Simulation studies show that the proposed design has desirable operating characteristics. Our design selects the target dose with a high probability and assigns most patients to the target dose. Twit Text FOAVip A Bayesian Dose-finding Design for Phase I/II Clinical Trials with Non-ignorable Dropouts ????Stat Med http://www.kidney.de/pget.php?&tw=1&pmid=25626676 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=25626676 #FOAvip English Wikipedia <ref>{{Cite pmid|25626676}}</ref> A Bayesian Dose-finding Design for Phase I/II Clinical Trials with Non-ignorable Dropouts #MMPMID25626676 Guo B; Yuan Y Stat Med 2015[May]; 34 (10): 1721-32 PMID25626676show ga Phase I/II trials utilize both toxicity and efficacy data to achieve efficient dose finding. However, due to the requirement of assessing efficacy outcome, which often takes a long period of time to be evaluated, the duration of phase I/II trials is often longer than that of the conventional dose-finding trials. As a result, phase I/II trials are susceptible to the missing data problem caused by patient dropout, and the missing efficacy outcomes are often non-ignorable in the sense that patients who do not experience treatment efficacy are more likely to drop out of the trial. We propose a Bayesian phase I/II trial design to accommodate non-ignorable dropouts. We treat toxicity as a binary outcome and efficacy as a time-to-event outcome. We model the marginal distribution of toxicity using a logistic regression and jointly model the times to efficacy and dropout using proportional hazard models to adjust for non-ignorable dropouts. The correlation between times to efficacy and dropout is modeled using a shared frailty. We propose a two-stage dose-finding algorithm to adaptively assign patients to desirable doses. Simulation studies show that the proposed design has desirable operating characteristics. Our design selects the target dose with a high probability and assigns most patients to the target dose. äA Bayesian Dose-finding Design for Phase I/II Clinical Trials with Non(epmc).pdf DeepDyve Pubget Overpricing