A robust method for the assessment of average bioequivalence in the presence of outliers and skewness

Burger, Divan Aristo; Schall, Robert; Merwe, Sean van der

doi:10.1007/s11095-021-03110-z

Cited by 3 publications

(3 citation statements)

References 24 publications

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“…Two recent articles have used a Bayesian approach to bioequivalence on the log scale using the skew t-distribution, a four-parameter distribution which allows for nonsymmetry (34,35). Burger et al performed simulations for a crossover design with n=30 subjects and incorporated outliers by introducing contamination of 2.5 standard deviations of the lognormal with 1% probability (and 5% in the Supplemental Material).…”

Section: Tost or Best-which To Employ For Be?mentioning

confidence: 99%

Comparing a Bayesian Approach (BEST) with the Two One-Sided t-Tests (TOSTs) for Bioequivalence Studies

Peck

Campbell

Yoo

et al. 2022

AAPS J

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The two one-sided t-tests (TOST) procedure has been used to evaluate average bioequivalence (BE). As a regulatory standard, it is crucial that TOST distinguish BE from not-BE (NBE) when BE data are not lognormal. TOST was compared with a Bayesian procedure (BEST by Kruschke) in simulated datasets of test/reference ratios (T/R) which were BE and NBE, wherein (1) log(T/R) or T-R were normally distributed, (2) sample sizes ranged 10–50, and (3) extreme log(T/R) or T-R values were randomly included in datasets. The 90% “credible interval” (CrI) from BEST is a Bayesian alternative of the 90% confidence interval (CI) of TOST and it can be derived from a posterior distribution that is more reflective of the observed mean log(T/R) distribution that often deviates from normality. In the absence of extreme T/R values, both methods agreed BE when observed T/R were lognormal. BEST more accurately concluded BE or NBE, while requiring fewer subjects, when observed log(T/R) or T-R were normal in the presence of extreme values. Overall, TOST and BEST perform comparably on lognormal T/R, while BEST is more accurate, requiring fewer subjects when datasets are normal for T-R or contain extreme values. Of note, the normally distributed datasets only rarely contain extreme values. Our results imply that when BEST and TOST yield different BE assessment results from bioequivalent products, TOST may disadvantage applicants when T/R are not lognormal and/or include extreme T/R values. Application of BEST can address the situation when T/R are not lognormal or include extreme data values. Application of BEST to BE data can be considered a useful alternative to TOST for evaluation of BE and for efficient development of BE formulations.

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Section: Tost or Best-which To Employ For Be?mentioning

confidence: 99%

Comparing a Bayesian Approach (BEST) with the Two One-Sided t-Tests (TOSTs) for Bioequivalence Studies

Peck

Campbell

Yoo

et al. 2022

AAPS J

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“…However, this may not be a reasonable solution for most cases, thus causing a considerable modeling challenge for the statistician (Feng et al [16] ). The use of Bayesian methods based on extended generalized gamma distribution and skew-t distribution has been discussed in the literature for such bioequivalence studies with the skewed response (de Souza et al [12] , Burger et al [9] ). Using conventional analysis techniques for such skewed multivariate data may lead to an incorrect and biased parameter and variance estimates.…”

Section: Introductionmentioning

confidence: 99%

Likelihood-based Inference for Skewed Responses in a Crossover Trial Setup

Pareek¹,

Das²,

Mukhopadhyay³

2023

Preprint

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This work proposes a statistical model for crossover trials with multiple skewed responses measured in each period. A 3 × 3 crossover trial data where different doses of a drug were administered to subjects with a history of seasonal asthma rhinitis to grass pollen is used for motivation. In each period, gene expression values for ten genes were measured from each subject. It considers a linear mixed effect model with skew normally distributed random effect or random error term to model the asymmetric responses in the crossover trials. The paper examines cases (i) when a random effect follows a skew-normal distribution, as well as (ii) when a random error follows a skew-normal distribution. The EM algorithm is used in both cases to compute maximum likelihood estimates of parameters. Simulations and crossover data from the gene expression study illustrate the proposed approach.

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“…For example, de Souza et al [ 19 ] developed a Bayesian methodology for bioequivalence trials in which a normality assumption on the data is not a prerequisite. Advantages of using a heavily-tailed distribution were further illustrated from a Bayesian perspective in the interest of handling outliers [ 20 ]. As far as we are aware, though, statistical literature in this field has been written vastly for analysing pivotal bioequivalence trials, whereas scant attention has been paid to decision-making using the pilot data.…”

Section: Introductionmentioning

confidence: 99%

A Bayesian approach to pilot-pivotal trials for bioequivalence assessment

Lv,

Grayling,

Zhang

et al. 2023

BMC Med Res Methodol

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Background To demonstrate bioequivalence between two drug formulations, a pilot trial is often conducted prior to a pivotal trial to assess feasibility and gain preliminary information about the treatment effect. Due to the limited sample size, it is not recommended to perform significance tests at the conventional 5% level using pilot data to determine if a pivotal trial should take place. Whilst some authors suggest to relax the significance level, a Bayesian framework provides an alternative for informing the decision-making. Moreover, a Bayesian approach also readily permits possible incorporation of pilot data in priors for the parameters that underpin the pivotal trial. Methods We consider two-sequence, two-period crossover designs that compare test (T) and reference (R) treatments. We propose a robust Bayesian hierarchical model, embedded with a scaling factor, to elicit a Go/No-Go decision using predictive probabilities. Following a Go decision, the final analysis to formally establish bioequivalence can leverage both the pilot and pivotal trial data jointly. A simulation study is performed to evaluate trial operating characteristics. Results Compared with conventional procedures, our proposed method improves the decision-making to correctly allocate a Go decision in scenarios of bioequivalence. By choosing an appropriate threshold, the probability of correctly (incorrectly) making a No-Go (Go) decision can be ensured at a desired target level. Using both pilot and pivotal trial data in the final analysis can result in a higher chance of declaring bioequivalence. The false positive rate can be maintained in situations when T and R are not bioequivalent. Conclusions The proposed methodology is novel and effective in different stages of bioequivalence assessment. It can greatly enhance the decision-making process in bioequivalence trials, particularly in situations with a small sample size.

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A robust method for the assessment of average bioequivalence in the presence of outliers and skewness

Cited by 3 publications

References 24 publications

Comparing a Bayesian Approach (BEST) with the Two One-Sided t-Tests (TOSTs) for Bioequivalence Studies

Comparing a Bayesian Approach (BEST) with the Two One-Sided t-Tests (TOSTs) for Bioequivalence Studies

Likelihood-based Inference for Skewed Responses in a Crossover Trial Setup

A Bayesian approach to pilot-pivotal trials for bioequivalence assessment

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