Population Pharmacokinetics of Atorvastatin and Its Active Metabolites in Children and Adolescents With Heterozygous Familial Hypercholesterolemia: Selective Use of Informative Prior Distributions from Adults

2013 ◽  
Vol 53 (5) ◽  
pp. 505-516 ◽  
Author(s):  
William Knebel ◽  
Marc R. Gastonguay ◽  
Bimal Malhotra ◽  
Ahmed El-Tahtawy ◽  
Frank Jen ◽  
...  
Keyword(s):  
Children And Adolescents ◽  
Population Pharmacokinetics ◽  
Familial Hypercholesterolemia ◽  
Active Metabolites ◽  
Prior Distributions ◽  
Heterozygous Familial Hypercholesterolemia ◽  
Informative Prior ◽  
Informative Prior Distributions

Th-P16:415 Efficacy and safety of fluvastatin in children and adolescents with heterozygous familial hypercholesterolemia

2006 ◽  
Vol 7 (3) ◽  
pp. 585
Author(s):  
A. Van Der Graaf ◽  
M.C. Nierman ◽  
J.C. Firth ◽  
K.H. Wolmarans ◽  
A.D. Marais ◽  
...  
Keyword(s):  
Children And Adolescents ◽  
Familial Hypercholesterolemia ◽  
Efficacy And Safety ◽  
Heterozygous Familial Hypercholesterolemia

scholarly journals Updating Knowledge in The Estimation of The Genetics Parameters Multi-trait and Multi-Environment Bayesian Analysis in Rice (Oryza Sativa L.)

2021 ◽  
Author(s):  
Camila Ferreira Azevedo ◽  
Cynthia Barreto ◽  
Matheus Suela ◽  
Moysés Nascimento ◽  
Antônio Carlos Júnior ◽  
...  
Keyword(s):  
Bayesian Approach ◽  
Prior Distribution ◽  
Variance Components ◽  
Genetic Correlations ◽  
Bayesian Framework ◽  
Prior Distributions ◽  
Informative Prior ◽  
Informative Prior Distribution ◽  
Informative Prior Distributions ◽  
The Bayesian Approach

Abstract Among the multi-trait models used to jointly study several traits and environments, the Bayesian framework has been a preferable tool for using a more complex and biologically realistic model. In most cases, the non-informative prior distributions are adopted in studies using the Bayesian approach. Still, the Bayesian approach tends to present more accurate estimates when it uses informative prior distributions. The present study was developed to evaluate the efficiency and applicability of multi-trait multi-environment (MTME) models under a Bayesian framework utilizing a strategy for eliciting informative prior distribution using previous data from rice. The study involved data pertained to rice genotypes in three environments and five agricultural years (2010/2011 until 2014/2015) for the following traits: grain yield (GY), flowering in days (FLOR) and plant height (PH). Variance components and genetic and non-genetic parameters were estimated by the Bayesian method. In general, the informative prior distribution in Bayesian MTME models provided higher estimates of heritability and variance components, as well as minor lengths for the highest probability density interval (HPD), compared to their respective non-informative prior distribution analyses. The use of more informative prior distributions makes it possible to detect genetic correlations between traits, which cannot be achieved with the use of non-informative prior distributions. Therefore, this mechanism presented for updating knowledge to the elicitation of an informative prior distribution can be efficiently applied in rice genetic selection.

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