A Bayesian Approach to Parallelism Testing in Bioassay

Parallelism is a prerequisite for the determination of relative potency in bioassays. It involves testing of similarity between a pair of dose–response curves of a reference standard and a test sample. Methods for parallelism assessment that are currently in use include p-value-based significance tests and interval-based equivalence tests. These methods make statistical inference about the similarity between the model parameters of the dose–response curves based on the sampling distribution of the estimates of these parameters. Although the methods have some merits for parallelism testing, there is a major drawback to these approaches, namely that the similarity between the model parameters does not necessarily translate into the similarity between the two dose–response curves. As a result, a test may conclude that the model parameters are similar, yet there is little assurance on the similarity between the two dose–response curves. In this article, we reformulate the parallelism testing problem as testing the hypothesis that the test sample is a dilution or concentration of the reference standard. We propose a Bayesian approach to directly test the hypothesis. When the dose–response curves are linear, a closed-form solution is obtained. For nonlinear cases, we render a solution based on a simple two-dimensional optimization routine. The empirical properties of the method are evaluated and compared with existing methods through a simulation study based on real-life examples. It is shown that the method overcomes the shortcomings of the current approaches and is a viable alternative to parallelism testing.     

Essais de non-infériorité et d’équivalence : les points clés de leur méthodologie

Non-inferiority and equivalence trials aim to promote new treatments that are not expected to be superior to existing ones in a given indication. In order to compensate for a possible loss of efficacy, the new treatment should offer other advantages compared to the reference treatment, a better safety of use for example. Their methods somewhat differ from those of superiority trials, often better known to the medical community. This article presents the key points of the methodology of non-inferiority and equivalence trials in order to inform the readers of such trials about the issues and critical points. The general methodology (hypotheses, decision rules, number of subjects required, and strategy of analysis) is presented using examples and graphic illustrations. The issues and critical points are identified and discussed, in particular the choice of the comparator and of the margin of non-inferiority.     

Statistical Considerations in Demonstrating CMC Analytical Similarity for a Biosimilar Product

Demonstration of analytical similarity between a reference product and a biosimilar product is required as part of the biosimilarity approval process. A statistical two one-sided test (TOST) based on the difference of means proposed in the literature and recommended by the FDA for Tier 1 quality attributes is demonstrated to have a Type I error rate greater than the specified level, which cannot be corrected by increasing sample size. In this article, an alternative TOST based on the effect size is demonstrated to maintain the desired Type I error rate, and in some situations, provide greater power than the TOST based on the mean difference. Results are demonstrated both analytically and with computer simulations. An example with calculations is provided in the article. Supplementary materials for this article are avalilable online.     

A Comparison of Testing Parameters and the Implementation of a Group Sequential Design for Equivalence Studies Using Paired-Sample Analysis

We address the problem of establishing two-sided equivalence using paired-sample analysis of two treatments or two laboratory tests with a binary endpoint. Through real data examples and Monte Carlo simulations, we compare three commonly used testing parameters, namely, the difference of response probabilities, the ratio of response probabilities, and the ratio of discordant probabilities based on score test statistics for constructing equivalence hypothesis tests of paired binary data. We provide suggestions on the choice of these three testing parameters and proper equivalence margins in hypothesis formulation of equivalence testing. In addition, we describe the implementation of a group sequential design in the context of equivalence testing with early stopping to reject, as well as to declare equivalence.     

A Review of: “Applied Survival Analysis: Regression Modeling of Time-to-Event Data, 2nd ed., by D. W. Hosmer,S. Lemeshow,and S. May”

For the statistical validation of surrogate endpoints, an alternative formulation is proposed for testing Prentice’s fourth criterion, under a bivariate normal model. In such a setup, the criterion involves inference concerning an appropriate regression parameter, and the criterion holds if the regression parameter is zero. Testing such a null hypothesis has been criticized in the literature since it can only be used to reject a poor surrogate, and not to validate a good surrogate. In order to circumvent this, an equivalence hypothesis is formulated for the regression parameter, namely the hypothesis that the parameter is equivalent to zero. Such an equivalence hypothesis is formulated as an alternative hypothesis, so that the surrogate endpoint is statistically validated when the null hypothesis is rejected. Confidence intervals for the regression parameter and tests for the equivalence hypothesis are proposed using bootstrap methods and small sample asymptotics, and their performances are numerically evaluated and recommendations are made. The choice of the equivalence margin is a regulatory issue that needs to be addressed. The proposed equivalence testing formulation is also adopted for other parameters that have been proposed in the literature on surrogate endpoint validation, namely, the relative effect and proportion explained.     

Methodological issues in the obsessive–compulsive spectrum

The obsessive–compulsive spectrum is a heterogeneous class of conditions. Recently, expert consensus has emerged regarding possible candidate disorders [Mataix-Cols, D., Petrusa, A., Leckman, J.F., 2007. Issues for DSM-V: How should obsessive–compulsive and related disorders should be classified. American Journal of Psychiatry, 164, 1313–1314]. Further, expert survey data suggest that obsessive-compulsive disorder is composed of overlapping subtypes. However, methodological approaches for testing whether candidate disorders properly belong in the spectrum have varied widely, and do not necessarily differentiate conditions from subtypes. We describe preliminary methodological and statistical recommendations for a systematic approach to determining what constitutes a subtype, how to determine inclusion in the spectrum, and means for ruling out candidate disorders.     

Tensions among siblings in parent care

From a place of “genealogical equivalence” as children of their parents, siblings spend a lifetime developing separate identities. As parents near the end of their lives, issues of sibling equivalence are renegotiated in the face of equal obligations to provide care and equal entitlement to parent assets. In this paper, we hypothesize how unresolved issues of rivalry for parent affection/attention among siblings may be reasserted when parents need care. Data are drawn from a project about how parent care and assets are shared. In-depth interviews with three sibling groups experiencing conflict over sharing parent care and assets along with six Canadian legal case portrayals of disputes among siblings over how parent care and assets were shared are examined. Findings are that disputes occur when siblings perceive others as dominating parent care and assets through tactics such as separating the parent from other siblings and preventing other siblings from being engaged in decisions about care and assets. Discussion is focused on paradoxes faced by siblings given expectations for equity in parent relationships alongside perceived pre-eminence in care and asset decisions. Because the legal cases are part of public record, actual names are used in reporting and referencing these data. Pseudonyms were assigned to siblings from caregiving families who were interviewed.     

普通话言语测听材料

言语测听是一种很重要的言语能力评估手段。我国地域广阔，方言多样，测试材料不易统一，测试设备不易购置，导致言语测昕开展得不够普及。因此需要一套完整的。集测听材料和测听软件于一体的言语测听系统来改变国内当前状况。本文阐述了北京市耳鼻咽喉科研究所研发的普通话言语测听材料MSTMs（Mandarin Speech Test Materials）的编制原则、录音合成、等价性分析等，对MSTMs的组成以及相应的识别一强度函数测定也做了介绍。     

Sample size determination for a three-arm equivalence trial of Poisson and negative binomial responses

ABSTRACT

Assessing equivalence or similarity has drawn much attention recently as many drug products have lost or will lose their patents in the next few years, especially certain best-selling biologics. To claim equivalence between the test treatment and the reference treatment when assay sensitivity is well established from historical data, one has to demonstrate both superiority of the test treatment over placebo and equivalence between the test treatment and the reference treatment. Thus, there is urgency for practitioners to derive a practical way to calculate sample size for a three-arm equivalence trial. The primary endpoints of a clinical trial may not always be continuous, but may be discrete. In this paper, the authors derive power function and discuss sample size requirement for a three-arm equivalence trial with Poisson and negative binomial clinical endpoints. In addition, the authors examine the effect of the dispersion parameter on the power and the sample size by varying its coefficient from small to large. In extensive numerical studies, the authors demonstrate that required sample size heavily depends on the dispersion parameter. Therefore, misusing a Poisson model for negative binomial data may easily lose power up to 20%, depending on the value of the dispersion parameter.     

Statistical assessment of biosimilarity based on the relative distance between follow-on biologics for binary endpoints

A new three-arm parallel design was recently proposed to investigate the biosimilarity between a biological product and a reference product by using the relative distance. The purpose of this article is to extend their results to binary endpoints for three popular metrics: the risk difference, the log relative risk, and the log odds ratio. The relative distances based on the three metrics are defined, and corresponding test procedures are developed. The type I error rates and powers are investigated theoretically and empirically.