A new permutation-based method for assessing agreement between two observers making replicated binary readings

A new coefficient for assessing agreement between two observers using a permutation-based method is introduced in this article. When observations are binary, this coefficient compares the observed disagreement (probability of discordance) between the observers with its expected value under the hypothesis of individual equivalence. This hypothesis states that for each subject, the conditional distributions of the readings of the two observers are identical, and therefore from a statistical viewpoint it does not matter which observer makes the reading on this subject. Let K and L denote the numbers of replicated observations that are available from observers X and Y, respectively, on a given subject. Then the expected disagreement under individual equivalence for a subject is based on the (K + L) choosing K possible assignments of X's and Y's to the K + L observations made on this subject. Simple methods for the estimation of the new coefficient and its standard error are derived. The new coefficient is compared with kappa and the coefficient of individual agreement, which is based on comparing the inter and intra observer disagreements. Simulation studies confirm the validity of the estimated coefficient and its standard error. Data from a study involving the evaluation of mammograms by 10 radiologists are used to illustrate this new approach to the evaluation of observer agreement.     

FDA“治疗等效性评价供企业用指导原则”（草案）介绍

美国食品药品管理局（FDA）于2022年7月发布了"治疗等效性评价供企业用指导原则"（草案）。该指导原则阐明了FDA治疗等效性的标准以及治疗等效性编码系统，目的是准确评价仿制药与参比制剂的治疗等效性并通过治疗等效性代码，在"橙皮书"中迅速检索到治疗等效的仿制药。而中国目前尚无类似的指导原则，详细介绍FDA该指导原则主要内容，期望对中国加强仿制药的治疗等效性评价和加速完善和实施符合国情的治疗等效性编码系统有所帮助。     

Algorithms for evaluating reference scaled average bioequivalence: power,bias, and consumer risk

The determination of the bioequivalence between highly variable drug products involves the evaluation of reference scaled average bioequivalence. The European and US regulatory authorities suggest different algorithms for the implementation of this approach. Both algorithms are based on approximations reflected in lower than the achievable power or higher than the nominal consumer risk of 5%. To overcome these deficiencies, a new class of algorithms, the so‐called Exact methods, was earlier introduced. However, their applicability was limited. We propose 2 modifications which make their computation simpler and also applicable with any study design. Four algorithms were evaluated in simulated 3‐period and 4‐period bioequivalence studies: Hyslop's approach recommended by the US FDA, the method of average bioequivalence with expanding limits requested by the European EMA, and 2 versions of the new Exact methods. At small sample sizes, the Exact methods had substantially higher statistical power than Hyslop's algorithm and had lower consumer risk than the method of average bioequivalence with expanding limits. Similarly to the Hyslop's algorithm, higher than 5% consumer risk was observed only with either unbalanced study design or with additional regulatory requirements. The improved Exact algorithms compare favorably with the alternative procedures. They are based on the bias correction method of Hedges. The recognition that the scaled difference statistics is measured with bias has important practical implications when results of pilot bioequivalence studies are evaluated and, at the same time, calls for the revision of the statistical theory of RSABE and its related methods.     

Sample size requirement in analytical studies for similarity assessment

ABSTRACT

For the assessment of biosimilar products, the FDA recommends a stepwise approach for obtaining the totality-of-the-evidence for assessing biosimilarity between a proposed biosimilar product and its corresponding innovative biologic product. The stepwise approach starts with analytical studies for assessing similarity in critical quality attributes (CQAs), which are relevant to clinical outcomes at various stages of the manufacturing process. For CQAs that are the most relevant to clinical outcomes, the FDA requires an equivalence test be performed for similarity assessment based on an equivalence acceptance criterion (EAC) that is obtained using a single test value of some selected reference lots. In practice, we often have extremely imbalanced numbers of reference and test lots available for the establishment of EAC. In this case, to assist the sponsors, the FDA proposed an idea for determining the number of reference lots and the number of test lots required in order not to have imbalanced sample sizes when establishing EAC for the equivalence test based on extensive simulation studies. Along this line, this article not only provides statistical justification of Dong, Tsong, and Weng’s proposal, but also proposes an alternative method for sample size requirement for the Tier 1 equivalence test.     

Exact test-based approach for equivalence test with parameter margin

ABSTRACT

The equivalence test has a wide range of applications in pharmaceutical statistics which we need to test for the similarity between two groups. In recent years, the equivalence test has been used in assessing the analytical similarity between a proposed biosimilar product and a reference product. More specifically, the mean values of the two products for a given quality attribute are compared against an equivalence margin in the form of ±f × σ_R, where ± f × σ R is a function of the reference variability. In practice, this margin is unknown and is estimated from the sample as ±f × S_R. If we use this estimated margin with the classic t-test statistic on the equivalence test for the means, both Type I and Type II error rates may inflate. To resolve this issue, we develop an exact-based test method and compare this method with other proposed methods, such as the Wald test, the constrained Wald test, and the Generalized Pivotal Quantity (GPQ) in terms of Type I error rate and power. Application of those methods on data analysis is also provided in this paper. This work focuses on the development and discussion of the general statistical methodology and is not limited to the application of analytical similarity.     

Development of statistical methods for analytical similarity assessment

ABSTRACT

To evaluate the analytical similarity between the proposed biosimilar product and the US-licensed reference product, a working group at Food and Drug Administration (FDA) developed a tiered approach. This proposed tiered approach starts with a criticality determination of quality attributes (QAs) based on risk ranking of their potential impact on product quality and the clinical outcomes. Those QAs characterize biological products in terms of structural, physicochemical, and functional properties. Correspondingly, we propose three tiers of statistical approaches based on the levels of stringency in requirements. The three tiers of statistical approaches will be applied to QAs based on the criticality ranking and other factors. In this article, we discuss the statistical methods applicable to the three tiers of QA. We further provide more details for the proposed equivalence test as the Tier 1 approach. We also provide some discussion on the statistical challenges of the proposed equivalence test in the context of analytical similarity assessment.     

正确运用统计学检验方法判定两治疗方案疗效等价

对两组平均水平差异的差异性检验得出不拒绝零假设的结论时,此结果常常被误解为两治疗方案疗效等价。本文从等效性检验的方法实现、样本量要求、等效界限的选择等几个方面阐述了两组疗效比较的临床试验应如何判定疗效等价,以期引起广大医务工作者、临床治疗用药物研制者的注意,正确地给出对试验结果的诠释。     

Likelihood ratio and score tests to test the non‐inferiority (or equivalence) of the odds ratio in a crossover study with binary outcomes

We consider the non‐inferiority (or equivalence) test of the odds ratio (OR) in a crossover study with binary outcomes to evaluate the treatment effects of two drugs. To solve this problem, Lui and Chang (2011) proposed both an asymptotic method and a conditional method based on a random effects logit model. Kenward and Jones (1987) proposed a likelihood ratio test (LRT_M) based on a log linear model. These existing methods are all subject to model misspecification. In this paper, we propose a likelihood ratio test (LRT) and a score test that are independent of model specification. Monte Carlo simulation studies show that, in scenarios considered in this paper, both the LRT and the score test have higher power than the asymptotic and conditional methods for the non‐inferiority test; the LRT, score, and asymptotic methods have similar power, and they all have higher power than the conditional method for the equivalence test. When data can be well described by a log linear model, the LRT_M has the highest power among all the five methods (LRT_M, LRT, score, asymptotic, and conditional) for both non‐inferiority and equivalence tests. However, in scenarios for which a log linear model does not describe the data well, the LRT_M has the lowest power for the non‐inferiority test and has inflated type I error rates for the equivalence test. We provide an example from a clinical trial that illustrates our methods. Copyright © 2016 John Wiley & Sons, Ltd.     

Comparison of the grain composition in resveratrol-enriched and glufosinate-tolerant rice (Oryza sativa) to conventional rice using univariate and multivariate analysis

Resveratrol-enriched rice (RR) contains genes that express the recombinant Arachis hypogaea stilbene synthase (AhSTS1) and phosphinothricin-N-acetyltrasferase (PAT) for resveratrol production and glufosinate tolerance, respectively. To satisfy regulatory safety evaluations, herein, the content of 55 analytes in the RR (non-sprayed and sprayed with glufosinate) and conventional non-transgenic rice grown at three different sites in the Republic of Korea was determined. Data evaluated using univariate and multivariate analyses indicated compositional equivalence between the RR and conventional rice. Principal components analysis (PCA) revealed significantly higher differences among plants from different locations than between the transgenic versus non-transgenic plants. Pearson correlation and hierarchical clustering (HCA) analyses indicated significant correlations among the contents of most minerals. Furthermore, PCA could not distinguish among glufosinate-sprayed RR, non-sprayed RR, and its control, supporting the conclusion that glufosinate-ammonium treatment caused insignificant changes in the RR grain composition.