The observed improvements in ADAS-Cog and CIBIC+ following treatment with xanomeline provide the first evidence, from a large-scale, placebo-controlled clinical trial, that a direct-acting muscarinic receptor agonist can improve cognitive function in patients with AD. Furthermore, the dramatic and favorable effects on disturbing behaviors in AD suggest a novel approach for treatment of noncognitive symptoms.

SummaryIn this paper we describe methods for addressing multiplicity issues arising in the analysis of clinical trials with multiple endpoints and/or multiple dose levels. Efficient "gatekeeping strategies" for multiplicity problems of this kind are developed. One family of hypotheses (comprised of the primary objectives) is treated as a "gatekeeper," and the other family or families (comprised of secondary and tertiary objectives) are tested only if one or more gatekeeper hypotheses have been rejected. We discuss methods for constructing gatekeeping testing procedures using weighted Bonferroni tests, weighted Simes tests, and weighted resampling-based tests, all within a closed testing framework. The new strategies are illustrated using an example from a clinical trial with co-primary endpoints, and using an example from a dose-finding study with multiple endpoints. Power comparisons with competing methods show the gatekeeping methods are more powerful when the primary objective of the trial must be met.

There are quite a few disorders for which regulatory agencies have required a treatment to demonstrate a statistically significant effect on multiple endpoints, each at the one-sided 2.5% level, before accepting the treatment's efficacy for the disorders. Depending on the correlation among the endpoints, this requirement could lead to a substantial reduction in the study's power to conclude the efficacy of a treatment. To investigate the prevalence of this requirement and propose possible solutions, a multiple-disciplinary Multiple Endpoints Expert Team sponsored by Pharmaceutical Research and Manufacturers of America was formed in November 2003. The team recognized early that many researchers were not fully aware of the implications of requiring multiple co-primary endpoints. The team proposes possible solutions from both the medical and the statistical perspectives. The optimal solution is to reduce the number of multiple co-primary endpoints. If after careful considerations, multiple co-primary endpoints remain a scientific requirement, the team proposes statistical solutions and encourages that regulatory agencies be receptive to approaches that adopt modest upward adjustments of the nominal significance levels for testing individual endpoints. Finally, the team hopes that this report will draw more attention to the problem of multiple co-primary endpoints and stimulate further research.

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Summary Many new experimental treatments benefit only a subset of the population. Identifying the baseline covariate profiles of patients who benefit from such a treatment, rather than determining whether or not the treatment has a population-level effect, can substantially lessen the risk in undertaking a clinical trial and expose fewer patients to treatments that do not benefit them. The standard analyses for identifying patient subgroups that benefit from an experimental treatment either do not account for multiplicity, or focus on testing for the presence of treatment-covariate interactions rather than the resulting individualized treatment effects. We propose a Bayesian credible subgroups method to identify two bounding subgroups for the benefiting subgroup: one for which it is likely that all members simultaneously have a treatment effect exceeding a specified threshold, and another for which it is likely that no members do. We examine frequentist properties of the credible subgroups method via simulations and illustrate the approach using data from an Alzheimer's disease treatment trial. We conclude with a discussion of the advantages and limitations of this approach to identifying patients for whom the treatment is beneficial.

There are many disorders where regulatory agencies have required a new treatment to demonstrate efficacy on multiple co-primary endpoints, all significant at the one-sided 2.5 per cent level, before accepting the treatment's effect for the disorder. This requirement, rooted in the intersection-union (IU) test, has led many researchers to increase the study sample size to make up for the reduction in the statistical power at the study level. Unfortunately, the increase in sample size could be substantial when the endpoints are minimally correlated and the treatment effects on the multiple endpoints are comparable. In this paper, we demonstrate that the frequentist concept of controlling the maximum false positive rate, even when applied to a restricted null space, has only limited success in keeping the sample size increase at a reasonable level. We therefore propose an approach that is based on the notion of controlling an average type I error rate. By employing an upper bound for the average type I error rate, the new approach provides an adjustment to the significance level that depends only on the correlation among the endpoints. For the most common case of two or three co-primary endpoints, the adjusted significance level is at most 5 per cent (one-sided) when the endpoints are moderately correlated. We show how sample size could be calculated under the proposed approach and contrast the needed sample size with that required under the IU test. We provide additional comments and discuss why the new approach is consistent with the principle requiring evidence of significance in the drug development and approval process.

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