Re-randomization tests in clinical trials.

As randomization methods use more information in more complex ways to assign patients to treatments, analysis of the resulting data becomes challenging. The treatment assignment vector and outcome vector become correlated whenever randomization probabilities depend on data correlated with outcomes. One straightforward analysis method is a re-randomization test that fixes outcome data and creates a reference distribution for the test statistic by repeatedly re-randomizing according to the same randomization method used in the trial. This article reviews re-randomization tests, especially in nonstandard settings like covariate-adaptive and response-adaptive randomization. We show that re-randomization tests provide valid inference in a wide range of settings. Nonetheless, there are simple examples demonstrating limitations.

Flexible alpha allocation strategies for confirmatory adaptive enrichment clinical trials with a prespecified subgroup.

Adaptive enrichment designs have recently received considerable attention as they have the potential to make drug development process for personalized medicine more efficient. Several statistical approaches have been proposed so far in the literature and the operating characteristics of these approaches are extensively investigated using simulation studies. In this paper, we improve on existing adaptive enrichment designs by assigning unequal weights to the significance levels associated with the hypotheses of the overall population and a prespecified subgroup. More specifically, we focus on the standard combination test, a modified combination test, the marginal combination test, and the partial conditional error rate approach and explore the operating characteristics of these approaches by a simulation study. We show that these approaches can lead to power gains, compared to existing approaches, if the weights are chosen carefully.

Adaptive graph-based multiple testing procedures.

Multiple testing procedures defined by directed, weighted graphs have recently been proposed as an intuitive visual tool for constructing multiple testing strategies that reflect the often complex contextual relations between hypotheses in clinical trials. Many well-known sequentially rejective tests, such as (parallel) gatekeeping tests or hierarchical testing procedures are special cases of the graph based tests. We generalize these graph-based multiple testing procedures to adaptive trial designs with an interim analysis. These designs permit mid-trial design modifications based on unblinded interim data as well as external information, while providing strong family wise error rate control. To maintain the familywise error rate, it is not required to prespecify the adaption rule in detail. Because the adaptive test does not require knowledge of the multivariate distribution of test statistics, it is applicable in a wide range of scenarios including trials with multiple treatment comparisons, endpoints or subgroups, or combinations thereof. Examples of adaptations are dropping of treatment arms, selection of subpopulations, and sample size reassessment. If, in the interim analysis, it is decided to continue the trial as planned, the adaptive test reduces to the originally planned multiple testing procedure. Only if adaptations are actually implemented, an adjusted test needs to be applied. The procedure is illustrated with a case study and its operating characteristics are investigated by simulations.