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10.1002/pst.1862 http://scihub22266oqcxt.onion/10.1002/pst.1862 C6055870!6055870!29667367     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       Pharm+Stat 2018 ; 17 (4): 329-41 Nephropedia Template TP {{tp|p=29667367|t=2018. How to use prior knowledge and still give new data a chance?|pdf=|usr=}}{{29667367}} gab.com Text How to use prior knowledge and still give new data a chance JO: Pharm Stat http://www.kidney.de/pget.php?&tw=1&pmid=29667367 #FOAvip A common challenge for the development of drugs in rare diseases and special populations, eg, paediatrics, is the small numbers of patients that can be recruited into clinical trials. Extrapolation can be used to support development and licensing in paediatrics through the structured integration of available data in adults and prospectively generated data in paediatrics to derive conclusions that support licensing decisions in the target paediatric population. In this context, Bayesian analyses have been proposed to obtain formal proof of efficacy of a new drug or therapeutic principle by using additional information (data, opinion, or expectation), expressed through a prior distribution. However, little is said about the impact of the prior assumptions on the evaluation of outcome and prespecified strategies for decision?making as required in the regulatory context.On the basis of examples, we explore the use of data?based Bayesian meta?analytic?predictive methods and compare these approaches with common frequentist and Bayesian meta?analysis models. Noninformative efficacy prior distributions usually do not change the conclusions irrespective of the chosen analysis method. However, if heterogeneity is considered, conclusions are highly dependent on the heterogeneity prior. When using informative efficacy priors based on previous study data in combination with heterogeneity priors, these may completely determine conclusions irrespective of the data generated in the target population. Thus, it is important to understand the impact of the prior assumptions and ensure that prospective trial data in the target population have an appropriate chance, to change prior belief to avoid trivial and potentially erroneous conclusions. Twit Text FOAVip How to use prior knowledge and still give new data a chance ????Pharm Stat http://www.kidney.de/pget.php?&tw=1&pmid=29667367 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=29667367 #FOAvip English Wikipedia <ref>{{Cite pmid|29667367}}</ref> How to use prior knowledge and still give new data a chance? #MMPMID29667367 Weber K; Hemmings R; Koch A Pharm Stat 2018[Jul]; 17 (4): 329-41 PMID29667367show ga A common challenge for the development of drugs in rare diseases and special populations, eg, paediatrics, is the small numbers of patients that can be recruited into clinical trials. Extrapolation can be used to support development and licensing in paediatrics through the structured integration of available data in adults and prospectively generated data in paediatrics to derive conclusions that support licensing decisions in the target paediatric population. In this context, Bayesian analyses have been proposed to obtain formal proof of efficacy of a new drug or therapeutic principle by using additional information (data, opinion, or expectation), expressed through a prior distribution. However, little is said about the impact of the prior assumptions on the evaluation of outcome and prespecified strategies for decision?making as required in the regulatory context.On the basis of examples, we explore the use of data?based Bayesian meta?analytic?predictive methods and compare these approaches with common frequentist and Bayesian meta?analysis models. Noninformative efficacy prior distributions usually do not change the conclusions irrespective of the chosen analysis method. However, if heterogeneity is considered, conclusions are highly dependent on the heterogeneity prior. When using informative efficacy priors based on previous study data in combination with heterogeneity priors, these may completely determine conclusions irrespective of the data generated in the target population. Thus, it is important to understand the impact of the prior assumptions and ensure that prospective trial data in the target population have an appropriate chance, to change prior belief to avoid trivial and potentially erroneous conclusions. äHow to use prior knowledge and still give new data a chance?(epmc).pdf DeepDyve Pubget Overpricing