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We propose a frequentist testing procedure that maintains a defined coverage and is optimal in the sense that it gives maximal power to detect deviations from a null hypothesis when the alternative to the null hypothesis is sampled from a pre-specified distribution (the prior distribution). Selecting a prior distribution allows to tune the decision rule. This leads to an increased power, if the true data generating distribution happens to be compatible with the prior. It comes at the cost of losing power, if the data generating distribution or the observed data are incompatible with the prior. We illustrate the proposed approach for a binomial experiment, which is sufficiently simple such that the decision sets can be illustrated in figures, which should facilitate an intuitive understanding. The potential beyond the simple example will be discussed: the approach is generic in that the test is defined based on the likelihood function and the prior only. It is comparatively simple to implement and efficient to execute, since it does not rely on Minimax optimization. Conceptually it is interesting to note that for constructing the testing procedure the Bayesian posterior probability distribution is used. [ABSTRACT FROM AUTHOR]

Biosimilar products are a relative newcomer in the pharmaceutical industry. It was only in 2006 that the first biosimilar product, Omnitrope[textregistered] (somatropin), a human growth hormone produced by Sandoz was approved by the European Medicine Agency, followed later the same year by the US Food & Drug Administration, and in 2009 by Pharmaceuticals and Medical Devices Agency in Japan. The clinical component of a biosimilar development borrows extensively from traditional drug development. However, when it comes to clinical study designs, this may not always be applicable. This article investigates Type I error violations that occur when blinded sample size reviews are applied in equivalence testing as used in biosimilar drug development. We give a derivation which explains why such violations are more pronounced in equivalence testing than in the case of superiority testing. In addition, the amount of Type I error inflation is quantified by simulation as well as by some theoretical considerations. Nonnegligible Type I error violations arise when blinded interim reassessments of sample sizes are performed particularly if sample sizes are small, but within the range of what is practically relevant. [ABSTRACT FROM AUTHOR]

To efficiently and completely correct for selection bias in adaptive two-stage trials, uniformly minimum variance conditionally unbiased estimators (UMVCUEs) have been derived for trial designs with normally distributed data. However, a common assumption is that the variances are known exactly, which is unlikely to be the case in practice. We extend the work of Cohen and Sackrowitz (Statistics & Probability Letters, 8(3):273-278, 1989), who proposed an UMVCUE for the best performing candidate in the normal setting with a common unknown variance. Our extension allows for multiple selected candidates, as well as unequal stage one and two sample sizes. [ABSTRACT FROM AUTHOR]

The article deals with the design of a complex clinical trial with multiple doses of an experimental treatment tested against a control treatment, interim analyses and several endpoints characterizing treatment success. The closed test principle, methods from group sequential testing, and combination test methodology are combined to control the familywise error rate (FWER) when testing for treatment benefit. We also discuss strategies for utilizing the correlations between some of the relevant test statistics. [ABSTRACT FROM AUTHOR]

The article presents comments of the author on the study "A Bayesian Meta-analysis Method for Estimating Risk Difference of Rare Events" by researcher Y. Tang and colleagues. It is noted that the study found that binomial model with beta distributed random effects for risk differences (B-BIRD) is closely related to the beta-binomial model (BBIN).

The U.S. FDA has published a draft guidance on 'Adaptive Design Clinical Trials for Drugs and Biologics', which gives regulatory guidance on methodological issues in exploratory and confirmatory clinical trials planned with an adaptive design. This comment summarizes the discussion within the joint working group 'Adaptive Designs and Multiple Testing Procedures' of the Austro-Swiss and German regions of the International Biometric Society held at the 90-day public comment period in spring 2010. [ABSTRACT FROM AUTHOR]

A method of multivariate data compression and dimension reduction is established, which is based on principal components and avoids all overfitting effects. This method allows the use of ‘compressed’ data for exact level-alpha tests of hypotheses on the mean vectors. It is a particularity of the method that the coefficients of the constructed linear scores depend solely on the residual sums of products matrix; the empirical means are not necessary to determine the compression. Thus, novel and very simple confidence regions of the unknown multivariate mean vectors are also obtained. The method can be combined with strategies of selecting variables. Furthermore, multiple testing procedures are derived, which serve for finding all sets of variables with deviations from the null hypothesis. The methods are evaluated by computer simulations. [ABSTRACT FROM AUTHOR]

In this paper, we consider tests for the hypothesis that the mean vector is zero against one-sided alternatives when the observation vectors are independently and identically distributed as normal with unknown covariance matrix. The exact null-distribution of the tests is derived. The tests generalize the centre-direction test proposed by Tang et al. for known covariance. In addition, the modification is order- and scale-invariant. Power comparisons with some other tests are presented. It can be shown that the null distribution of the test statistic holds for data arising from any elliptical distribution, not just the normal distribution. [ABSTRACT FROM AUTHOR]

Fu (2018) developed a parametric consonant closed testing procedure for correlated endpoints in a group-sequential design. In their approach, correlations between endpoints are exploited leading to a proportional up-scaling of local significance levels from Holm's weighting scheme. We challenge the claim that the proposed calculation of the up-scaling factor leads to a test that protects the type I error in the group-sequential setting and outline two alternative approaches which do lead to exact error control. [ABSTRACT FROM AUTHOR]