Literature DB >> 16832840

A semi-parametric Bayesian approach to average bioequivalence.

Pulak Ghosh1, Gary L Rosner.   

Abstract

Bioequivalence assessment is an issue of great interest. Development of statistical methods for assessing bioequivalence is an important area of research for statisticians. Bioequivalence is usually determined based on the normal distribution. We relax this assumption and develop a semi-parametric mixed model for bioequivalence data. The proposed method is quite flexible and practically meaningful. Our proposed method is based on a mixture normal distribution and a non-parametric Bayesian approach using a Dirichlet process mixture prior. A numerical example illustrates the use of our procedure. Copyright (c) 2006 John Wiley & Sons, Ltd.

Mesh:

Year:  2007        PMID: 16832840     DOI: 10.1002/sim.2620

Source DB:  PubMed          Journal:  Stat Med        ISSN: 0277-6715            Impact factor:   2.373


  3 in total

1.  Semiparametric Bayesian approaches to joinpoint regression for population-based cancer survival data.

Authors:  Pulak Ghosh; Lan Huang; Binbing Yu; Ram C Tiwari
Journal:  Comput Stat Data Anal       Date:  2009-10-01       Impact factor: 1.681

2.  Two general methods for population pharmacokinetic modeling: non-parametric adaptive grid and non-parametric Bayesian.

Authors:  Tatiana Tatarinova; Michael Neely; Jay Bartroff; Michael van Guilder; Walter Yamada; David Bayard; Roger Jelliffe; Robert Leary; Alyona Chubatiuk; Alan Schumitzky
Journal:  J Pharmacokinet Pharmacodyn       Date:  2013-02-13       Impact factor: 2.745

3.  Analyzing longitudinal data to characterize the accuracy of markers used to select treatment.

Authors:  Colleen M Sitlani; Patrick J Heagerty
Journal:  Stat Med       Date:  2014-03-13       Impact factor: 2.373

  3 in total

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