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.

Department of Biostatistics, The University of Kansas Medical Center, Mail Stop 1026, 3901 Rainbow Blvd., Kansas City, KS, 66160, USA, .
Clinical Pharmacokinetics (Impact Factor: 5.05). 10/2013; 52(12). DOI: 10.1007/s40262-013-0103-4
Source: PubMed


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.

Download full-text


Available from: Ron Krebill, Feb 03, 2014
1 Follower
195 Reads
  • [Show abstract] [Hide abstract]
    ABSTRACT: In a traditional assessment of the bioequivalence of two formulations of a drug one compares the average bioavailability from the two formulations. Anderson and Hauck argued that in some situations it is not sufficient to demonstrate average bioequivalence, and they proposed a method for the assessment of what they called individual bioequivalence, which essentially is the comparison of the individual responses to the two drug formulations within subjects. In this paper we propose a unified strategy for the assessment of bioequivalence that encompasses new approaches to the assessment of both population bioequivalence, which is the comparison of the marginal or population distributions of bioavailabilities, and individual bioequivalence, which is the comparison of the conditional or within-subject distributions of bioavailabilities. The general idea is to use a comparison of the reference formulation to itself as the basis for the comparison of the test with the reference formulation. The new approaches overcome the main weakness of the current methods for the assessment of bioequivalence by considering the variability of bioavailabilities in addition to their means. The current methods for the assessment of bioequivalence, namely the conventional assessment of average bioequivalence and the proposal by Anderson and Hauck for the assessment of individual bioequivalence, emerge as special cases. One can evaluate the new bioequivalence criteria statistically by use of bootstrap confidence intervals.
    Statistics in Medicine 07/1993; 12(12):1109-24. DOI:10.1002/sim.4780121202 · 1.83 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: The objective of the methods proposed is to provide a parametric model for the incubation of AIDS and to use the chosen parameterization to test for the effect of age at seroconversion, and, after adjusting for markers of immunosuppression, to assess variations in periods corresponding to different levels of use of AIDS therapies at the population level. We compared the fit of Weibull, log-normal and three-parameter logistic models incorporating truncation in prevalent cohort and interval censored data. We showed the advantages by restricting the analysis to follow-up durations of greater than five years to improve estimation of the tail of the distribution for the prediction of long-term survivors. We applied the proposed methods to the combination of 1649 seroprevalent and 476 seroconverters with 1022 and 177 AIDS cases, respectively, who have been followed in the Multicenter AIDS Cohort Study (MACS) up to April 1995. Differences according to age at seroconversion are quantified in terms of relative percentiles and their associated 95 per cent confidence intervals were calculated using methods of multiple imputation. Using the proposed methods, we found that the log-normal model provides a fit as good as the three-parameter logistic; both are close to the non-parametric estimate and are significantly better than the fit of the Weibull model. We found that the older the age at seroconversion, the shorter the time to AIDS (relative percentile = 0.72 for age > or = 40 versus age < 25), and that the incubation of AIDS in calendar periods where treatment has been widely administered has been significantly longer among individuals with low CD4 cell counts.
    Statistics in Medicine 11/1996; 15(21-22):2459-73. DOI:10.1002/(SICI)1097-0258(19961130)15:223.0.CO;2-Q · 1.83 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Bioequivalence or interaction trials are commonly studied in crossover design and can be analysed by nonlinear mixed effects models as an alternative to noncompartmental approach. We propose an extension of the population Fisher information matrix in nonlinear mixed effects models to design crossover pharmacokinetic trials, using a linearisation of the model around the random effect expectation, including within-subject variability and discrete covariates fixed or changing between periods. We use the expected standard errors of treatment effect to compute the power for the Wald test of comparison or equivalence and the number of subjects needed for a given power. We perform various simulations mimicking crossover two-period trials to show the relevance of these developments. We then apply these developments to design a crossover pharmacokinetic study of amoxicillin in piglets and implement them in the new version 3.2 of the r function PFIM.
    Statistics in Medicine 05/2012; 31(11-12):1043-58. DOI:10.1002/sim.4390 · 1.83 Impact Factor
Show more