Ethnic sensitive or molecular sensitive beyond all regions being equal in multiregional clinical trials.

For decades, clinical trials have been the primary mechanism for medical products to enter the marketplace. Over more than a decade, globalization of medical product development via a multiregional clinical trial (MRCT) approach has generated greater enthusiasm because of tangible benefits in terms of cost and time for drug development. There are, however, many challenges including and not limited to design issues, statistical analysis methods, interpretation of extreme region performance, and in-process quality assurance issues. This article presents a number of examples to exemplify regional variability expected versus precision of treatment effect estimates that are generally impacted by the type of primary efficacy endpoint evaluated. We explore region-driven intrinsic and extrinsic ethnic factors for potential explanation of regional heterogeneity caused by differences in medical practice and / or disease etiology. Bayesian credible interval may be considered as a viable approach to assess the robustness of region-specific treatment effect. Ethnic-sensitive or molecular-sensitive region-driven designs may be explored to prospectively address the potential regional heterogeneity versus the potential predictiveness of causal genetic variants or molecular target biomarkers on treatment effect.

Sample size adaptation in fixed-dose combination drug trial.

Statistical testing in clinical trials can be complex when the statistical distribution of the test statistic involves a nuisance parameter. Some type of nuisance parameters such as standard deviation of a continuous response variable can be handled without too much difficulty. Other type of nuisance parameters, specifically associated with the main parameter under testing, can be difficult to handle. Without knowledge of the possible value of such a nuisance parameter, the maximum type I error associated with testing the main parameter may occur at an extreme value of the nuisance parameter. A well known example is the intersection-union test for comparing a combination drug with its two component drugs where the nuisance parameter is the mean difference between the two components. Knowledge of the possible range of value of this mean difference may help enhance the clinical trial design. For instance, if the interim internal data suggest that this mean difference falls into a possible range of value, then the sample size may be reallocated after the interim look to possibly improve the efficiency of statistical testing. This research sheds some light into possible power advantage from such a sample size reallocation at the interim look.

Establishing consistency across all regions in a multi-regional clinical trial.

In recent years, global collaboration has become a conventional strategy for new drug development. To accelerate the development process and shorten approval time, the design of multi-regional clinical trials (MRCTs) incorporates subjects from many countries/regions around the world under the same protocol. After showing the overall efficacy of a drug in a global trial, one can also simultaneously evaluate the possibility of applying the overall trial results to all regions and subsequently support drug registration in each region. However, most of the recent approaches developed for the design and evaluation of MRCTs focus on establishing criteria to examine whether the overall results from the MRCT can be applied to a specific region. In this paper, we use the consistency criterion of Method 1 from the Japanese Ministry of Health, Labour and Welfare (MHLW) guidance to assess whether the overall results from the MRCT can be applied to all regions. Sample size determination for the MRCT is also provided to take all the consistency criteria from each individual region into account. Numerical examples are given to illustrate applications of the proposed approach.

Challenges to multiple testing in clinical trials.

Multiple testing problems are complex in evaluating statistical evidence in pivotal clinical trials for regulatory applications. However, a common practice is to employ a general and rather simple multiple comparison procedure to handle the problems. Applying multiple comparison adjustments is to ensure proper control of type I error rates. However, in many practices, the emphasis of the type I error rate control often leads to a choice of a statistically valid multiple test procedure but the common sense is overlooked. The challenges begin with confusions in defining a relevant family of hypotheses for which the type I error rates need to be properly controlled. Multiple testing problems are in a wide variety, ranging from testing multiple doses and endpoints jointly, composite endpoint, non-inferiority and superiority, to studying time of onset of a treatment effect, and searching for minimum effective dose or a patient subgroup in which the treatment effect lies. To select a valid and sensible multiple test procedure, the first step should be to tailor the selection to the study questions and to the ultimate clinical decision tree. Then evaluation of statistical power performance should come in to play in the next step to fine tune the selected procedure.

Impacts on type I error rate with inappropriate use of learn and confirm in confirmatory adaptive design trials.

A two-stage adaptive design trial is a single trial that combines the learning data from stage 1 (or phase II) and the confirming data in stage 2 (or phase III) for formal statistical testing. We call it a "Learn and Confirm" trial. The studywise type I error rate remains to be at issue in a "Learn and Confirm" trial. For studying multiple doses or multiple enpdoints, a "Learn and Confirm" adaptive design can be more attractive than a fixed design approach. This is because intuitively the learning data in stage 1 should not be subjected to type I error scrutiny if there is no formal interim analysis performed and only an adaptive selection of design parameters is made at stage 1. In this work, we conclude from extensive simulation studies that the intuition is most often misleading. That is, regardless of whether or not there is a formal interim analysis for making an adaptive selection, the type I error rates are always at risk of inflation. Inappropriate use of any "Learn and Confirm" strategy should not be overlooked.

Some fundamental issues with non-inferiority testing in active controlled trials.

In an active controlled non-inferiority trial without a placebo arm, it is often not entirely clear what the primary objective is. In many cases the considered goal is to demonstrate that the experimental treatment preserves at least some fraction of the effect of the active control. The active control effect is a parameter, the value of which is unknown. To test the hypothesis of effect preservation, the classical confidence interval approach requires specification of a non-inferiority margin which is a function of the unknown active control effect. When the margin is estimated, it is also not clear what is the relevant type I error of making a false assertion about preservation of the active control effect. The statistical uncertainty of the estimated margin arguably needs to be incorporated in evaluation of the type I error. In this paper we discuss these fundamental issues. We show that the classical confidence interval approach cannot attain the target type I error exactly since this error varies as the sample size or as the values of the nuisance parameters in the active controlled trial change. In contrast, the preservation tests, as proposed in literature, can attain the target type I error rate exactly, regardless of the sample size and the values of the nuisance parameters, but can do so only at the price of several strong assumptions holding that may not be directly verifiable. One assumption is the constancy condition holding whereby the effect of the active control in the historical trial populations is assumed to carry to the population of the active control trial. When this condition is violated, both the confidence interval approach and the preservation test method may be problematic.