Literature DB >> 24085600

Random-effects linear modeling and sample size tables for two special crossover designs of average bioequivalence studies: the four-period, two-sequence, two-formulation and six-period, three-sequence, three-formulation designs.

Francisco J Diaz1, Michel J Berg, Ron Krebill, Timothy Welty, Barry E Gidal, Rita Alloway, Michael Privitera.   

Abstract

Due to concern and debate in the epilepsy medical community and to the current interest of the US Food and Drug Administration (FDA) in revising approaches to the approval of generic drugs, the FDA is currently supporting ongoing bioequivalence studies of antiepileptic drugs, the EQUIGEN studies. During the design of these crossover studies, the researchers could not find commercial or non-commercial statistical software that quickly allowed computation of sample sizes for their designs, particularly software implementing the FDA requirement of using random-effects linear models for the analyses of bioequivalence studies. This article presents tables for sample-size evaluations of average bioequivalence studies based on the two crossover designs used in the EQUIGEN studies: the four-period, two-sequence, two-formulation design, and the six-period, three-sequence, three-formulation design. Sample-size computations assume that random-effects linear models are used in bioequivalence analyses with crossover designs. Random-effects linear models have been traditionally viewed by many pharmacologists and clinical researchers as just mathematical devices to analyze repeated-measures data. In contrast, a modern view of these models attributes an important mathematical role in theoretical formulations in personalized medicine to them, because these models not only have parameters that represent average patients, but also have parameters that represent individual patients. Moreover, the notation and language of random-effects linear models have evolved over the years. Thus, another goal of this article is to provide a presentation of the statistical modeling of data from bioequivalence studies that highlights the modern view of these models, with special emphasis on power analyses and sample-size computations.

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Year:  2013        PMID: 24085600     DOI: 10.1007/s40262-013-0103-4

Source DB:  PubMed          Journal:  Clin Pharmacokinet        ISSN: 0312-5963            Impact factor:   6.447


  20 in total

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2.  Position statement on the coverage of anticonvulsant drugs for the treatment of epilepsy.

Authors:  K Liow; G L Barkley; J R Pollard; C L Harden; C W Bazil
Journal:  Neurology       Date:  2007-04-17       Impact factor: 9.910

3.  Statistical tests with accurate size and power for balanced linear mixed models.

Authors:  Keith E Muller; Lloyd J Edwards; Sean L Simpson; Douglas J Taylor
Journal:  Stat Med       Date:  2007-08-30       Impact factor: 2.373

4.  Models for the incubation of AIDS and variations according to age and period.

Authors:  A Muñoz; J Xu
Journal:  Stat Med       Date:  1996 Nov 15-30       Impact factor: 2.373

5.  Safe and effective variability-a criterion for dose individualization.

Authors:  Nicholas H G Holford; Thierry Buclin
Journal:  Ther Drug Monit       Date:  2012-10       Impact factor: 3.681

Review 6.  Clobazam therapeutic drug monitoring: a comprehensive review of the literature with proposals to improve future studies.

Authors:  Jose de Leon; Edoardo Spina; Francisco J Diaz
Journal:  Ther Drug Monit       Date:  2013-02       Impact factor: 3.681

7.  A comparison of the two one-sided tests procedure and the power approach for assessing the equivalence of average bioavailability.

Authors:  D J Schuirmann
Journal:  J Pharmacokinet Biopharm       Date:  1987-12

8.  On population and individual bioequivalence.

Authors:  R Schall; H G Luus
Journal:  Stat Med       Date:  1993-06-30       Impact factor: 2.373

9.  Generic antiepileptic drugs: current controversies and future directions.

Authors:  Michael D Privitera
Journal:  Epilepsy Curr       Date:  2008 Sep-Oct       Impact factor: 7.500

10.  A study of genetic (CYP2D6 and ABCB1) and environmental (drug inhibitors and inducers) variables that may influence plasma risperidone levels.

Authors:  J de Leon; M T Susce; R-M Pan; P J Wedlund; M L Orrego; F J Diaz
Journal:  Pharmacopsychiatry       Date:  2007-05       Impact factor: 5.788

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  4 in total

1.  Measuring the individual benefit of a medical or behavioral treatment using generalized linear mixed-effects models.

Authors:  Francisco J Diaz
Journal:  Stat Med       Date:  2016-06-20       Impact factor: 2.373

2.  Bioequivalence Between Generic and Branded Lamotrigine in People With Epilepsy: The EQUIGEN Randomized Clinical Trial.

Authors:  Michel Berg; Timothy E Welty; Barry E Gidal; Francisco J Diaz; Ron Krebill; Jerzy P Szaflarski; Barbara A Dworetzky; John R Pollard; Edmund J Elder; Wenlei Jiang; Xiaohui Jiang; Regina D Switzer; Michael D Privitera
Journal:  JAMA Neurol       Date:  2017-08-01       Impact factor: 18.302

3.  Construction of the Design Matrix for Generalized Linear Mixed-Effects Models in the Context of Clinical Trials of Treatment Sequences.

Authors:  Francisco J Diaz
Journal:  Rev Colomb Estad       Date:  2018-07

4.  Measuring individual benefits of psychiatric treatment using longitudinal binary outcomes: Application to antipsychotic benefits in non-cannabis and cannabis users.

Authors:  Xuan Zhang; Jose de Leon; Benedicto Crespo-Facorro; Francisco J Diaz
Journal:  J Biopharm Stat       Date:  2020-06-08       Impact factor: 1.503

  4 in total

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