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10.1007/s11095-015-1699-x http://scihub22266oqcxt.onion/10.1007/s11095-015-1699-x C4577546!4577546!25994981     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       Pharm+Res 2015 ; 32 (10): 3228-37 Nephropedia Template TP {{tp|p=25994981|t=2015. Generating Virtual Patients by Multivariate and Discrete Re-Sampling Techniques |pdf=|usr=}}{{25994981}} gab.com Text Generating Virtual Patients by Multivariate and Discrete Re-Sampling Techniques JO: Pharm Res http://www.kidney.de/pget.php?&tw=1&pmid=25994981 #FOAvip Purpose: Clinical Trial Simulations (CTS) are a valuable tool for decision-making during drug development. However, to obtain realistic simulation scenarios, the patients included in the CTS must be representative of the target population. This is particularly important when covariate effects exist that may affect the outcome of a trial. The objective of our investigation was to evaluate and compare CTS results using re-sampling from a population pool and multivariate distributions to simulate patient covariates. Methods: COPD was selected as paradigm disease for the purposes of our analysis, FEV1 was used as response measure and the effects of a hypothetical intervention were evaluated in different populations in order to assess the predictive performance of the two methods. Results: Our results show that the multivariate distribution method produces realistic covariate correlations, comparable to the real population. Moreover, it allows simulation of patient characteristics beyond the limits of inclusion and exclusion criteria in historical protocols. Conclusion: Both methods, discrete resampling and multivariate distribution generate realistic pools of virtual patients. However the use of a multivariate distribution enable more flexible simulation scenarios since it is not necessarily bound to the existing covariate combinations in the available clinical data sets. Electronic supplementary material: The online version of this article (doi:10.1007/s11095-015-1699-x) contains supplementary material, which is available to authorized users. Twit Text FOAVip Generating Virtual Patients by Multivariate and Discrete Re-Sampling Techniques ????Pharm Res http://www.kidney.de/pget.php?&tw=1&pmid=25994981 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=25994981 #FOAvip English Wikipedia <ref>{{Cite pmid|25994981}}</ref> Generating Virtual Patients by Multivariate and Discrete Re-Sampling Techniques #MMPMID25994981 Teutonico D; Musuamba F; Maas HJ; Facius A; Yang S; Danhof M; Della Pasqua O Pharm Res 2015[]; 32 (10): 3228-37 PMID25994981show ga Purpose: Clinical Trial Simulations (CTS) are a valuable tool for decision-making during drug development. However, to obtain realistic simulation scenarios, the patients included in the CTS must be representative of the target population. This is particularly important when covariate effects exist that may affect the outcome of a trial. The objective of our investigation was to evaluate and compare CTS results using re-sampling from a population pool and multivariate distributions to simulate patient covariates. Methods: COPD was selected as paradigm disease for the purposes of our analysis, FEV1 was used as response measure and the effects of a hypothetical intervention were evaluated in different populations in order to assess the predictive performance of the two methods. Results: Our results show that the multivariate distribution method produces realistic covariate correlations, comparable to the real population. Moreover, it allows simulation of patient characteristics beyond the limits of inclusion and exclusion criteria in historical protocols. Conclusion: Both methods, discrete resampling and multivariate distribution generate realistic pools of virtual patients. However the use of a multivariate distribution enable more flexible simulation scenarios since it is not necessarily bound to the existing covariate combinations in the available clinical data sets. Electronic supplementary material: The online version of this article (doi:10.1007/s11095-015-1699-x) contains supplementary material, which is available to authorized users. äGenerating Virtual Patients by Multivariate and Discrete Re-Sampling T(epmc).pdf DeepDyve Pubget Overpricing