For clinical trials with multiple treatment arms or endpoints a variety of sequentially rejective, weighted Bonferroni-type tests have been proposed, such as gatekeeping procedures, fixed sequence tests, and fallback procedures. They allow to map the difference in importance as well as the relationship between the various research questions onto an adequate multiple test procedure. Since these procedures rely on the closed test principle, they usually require the explicit specification of a large number of intersection hypotheses tests. The underlying test strategy may therefore be difficult to communicate. We propose a simple iterative graphical approach to construct and perform such Bonferroni-type tests. The resulting multiple test procedures are represented by directed, weighted graphs, where each node corresponds to an elementary hypothesis, together with a simple algorithm to generate such graphs while sequentially testing the individual hypotheses. The approach is illustrated with the visualization of several common gatekeeping strategies. A case study is used to illustrate how the methods from this article can be used to tailor a multiple test procedure to given study objectives.

Traditional drug development consists of a sequence of independent trials organized in different phases. Full development typically involves (i) a learning phase II trial and (ii) one or two confirmatory phase III trial(s). For example, in the phase II trials several doses of the new compound might be compared to a control and/or placebo with the goal of deciding whether to stop or continue development and, in the latter case, selecting one or two "best" doses to carry forward into the confirmatory phase. The phase III trials are then conducted as stand-alone confirmatory studies, not incorporating in their statistical analyses data collected in the previous phases. Seamless phase II/III designs are aimed at interweaving the two phases of full development by combining them into one single, uninterrupted study conducted in two stages. In the dose-finding example above, one (or more) dose(s) are selected after the first stage based on the available data at interim, and are then observed further in the second stage. The final analysis of the selected dose(s) includes patients from both stages and is performed such that the overall type I error rate is controlled at a prespecified level regardless of the dose selection rule used at interim. The adequacy of the dose selection at interim is obviously a critical step for the success of a seamless phase II/III trial. In this paper we focus on the description of flexible test procedures allowing for adaptively selecting hypotheses at interim and thus allowing the combination of learning and confirming in a single seamless trial. We review the statistical background, introduce different test procedures and compare them in a power study. In a subsequent paper (Schmidli et al., 2006) we give several applications from our daily practice and discuss related implementation issues in conducting adaptive seamless designs.

The confirmatory analysis of pre-specified multiple hypotheses has become common in pivotal clinical trials. In the recent past multiple test procedures have been developed that reflect the relative importance of different study objectives, such as fixed sequence, fallback, and gatekeeping procedures. In addition, graphical approaches have been proposed that facilitate the visualization and communication of Bonferroni-based closed test procedures for common multiple test problems, such as comparing several treatments with a control, assessing the benefit of a new drug for more than one endpoint, combined non-inferiority and superiority testing, or testing a treatment at different dose levels in an overall and a subpopulation. In this paper, we focus on extended graphical approaches by dissociating the underlying weighting strategy from the employed test procedure. This allows one to first derive suitable weighting strategies that reflect the given study objectives and subsequently apply appropriate test procedures, such as weighted Bonferroni tests, weighted parametric tests accounting for the correlation between the test statistics, or weighted Simes tests. We illustrate the extended graphical approaches with several examples. In addition, we describe briefly the gMCP package in R, which implements some of the methods described in this paper.

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In this paper we present a general testing principle for a class of multiple testing problems based on weighted hypotheses. Under moderate conditions, this principle leads to powerful consonant multiple testing procedures. Furthermore, short-cut versions can be derived, which simplify substantially the implementation and interpretation of the related test procedures. It is shown that many well-known multiple test procedures turn out to be special cases of this general principle. Important examples include gatekeeping procedures, which are often applied in clinical trials when primary and secondary objectives are investigated, and multiple test procedures based on hypotheses which are completely ordered by importance. We illustrate the methodology with two real clinical studies.

Inferential test strategies for multi-arm trials are adapted or proposed for the special situation when more than one dose of a test treatment, placebo and active control(s) are compared. This includes between doses, dose-placebo and dose-active-control comparisons. The procedures refer to situations when detailed comparisons make sense only if the sensitivity of the trial has been shown, for example, if a dose-response relationship or a difference between active control and placebo has been established. Split strategies, hierarchical (assuming an order restriction among doses) or linked procedures are introduced. In linked procedures, equivalence to the active control will be established only if the dose is also shown to be effective as compared to placebo. All the inferential procedures control the experimentwise error rate in the strong sense for the respective sets of null hypotheses considered.

Adaptive seamless phase II/III designs combine a phase II and a phase III study into one single confirmatory clinical trial. Several examples of such designs are presented, where the primary endpoint is binary, time-to-event or continuous. The interim adaptations considered include the selection of treatments and the selection of hypotheses related to a pre-specified subgroup of patients. Practical aspects concerning the planning and implementation of adaptive seamless confirmatory studies are also discussed.

We consider the situation of testing hierarchically a (key) secondary endpoint in a group-sequential clinical trial that is mainly driven by a primary endpoint. By 'mainly driven', we mean that the interim analyses are planned at points in time where a certain number of patients or events have accrued on the primary endpoint, and the trial will run either until statistical significance of the primary endpoint is achieved at one of the interim analyses or to the final analysis. We consider both the situation where the trial is stopped as soon as the primary endpoint is significant as well as the situation where it is continued after primary endpoint significance to further investigate the secondary endpoint. In addition, we investigate how to achieve strong control of the familywise error rate (FWER) at a pre-specified significance level alpha for both the primary and the secondary hypotheses. We systematically explore various multiplicity adjustment methods. Starting point is a naive strategy of testing the secondary endpoint at level alpha whenever the primary endpoint is significant. Hung et al. (J. Biopharm. Stat. 2007; 17:1201-1210) have already shown that this naive strategy does not maintain the FWER at level alpha. We derive a sharp upper bound for the rejection probability of the secondary endpoint in the naive strategy. This suggests a number of multiple test strategies and also provides a benchmark for deciding whether a method is conservative or might be improved while maintaining the FWER at alpha. We use a numerical example based on a real case study to illustrate the results of different hierarchical test strategies.