Futility analysis is a critical component of clinical trials and should always be part of the discussion when setting up a clinical trial. It requires clear communication from statisticians to all stakeholders to make informed decisions. In this presentation, we share our recent experience with setting up a futility analysis and the practical learnings we gained from it. Specifically, we discuss the key risks involved in futility analysis and how we effectively communicated these risks to decision makers. Additionally, we propose a set of metrics and visualizations that can help facilitate discussions with non-statistical stakeholders.

Late phase clinical trials are occasionally planned with one or more interim analyses to allow for early termination or adaptation of the study. While extensive theory has been developed for the analysis of ordered categorical data in terms of the Wilcoxon-Mann-Whitney test, there has been comparatively little discussion in the group sequential literature on how to provide repeated confidence intervals and simple power formulas to ease sample size determination. Dealing more broadly with the nonparametric Behrens-Fisher problem, we focus on the comparison of two parallel treatment arms and show that the Wilcoxon-Mann-Whitney test, the Brunner-Munzel test, as well as a test procedure based on the log win odds, a modification of the win ratio, asymptotically follow the canonical joint distribution. In addition to developing power formulas based on these results, simulations confirm the adequacy of the proposed methods for a range of scenarios. Lastly, we apply our methodology to the FREEDOMS clinical trial (ClinicalTrials.gov Identifier: NCT00289978) in patients with relapse-remitting multiple sclerosis.

doi:https://doi.org/10.1177/09622802221107103

For randomized clinical trials where a single, primary, binary endpoint would require unfeasibly large sample sizes, composite endpoints (CEs) are widely chosen as the primary endpoint. Despite being commonly used, CEs entail challenges in designing and interpreting results. Given that the components may be of different relevance and have different effect sizes, the choice of components must be made carefully. Especially, sample size calculations for composite binary endpoints depend not only on the anticipated effect sizes and event probabilities of the composite components but also on the correlation between them. However, information on the correlation between endpoints is usually not reported in the literature, which can be an obstacle to designing future sound trials.

We consider two-arm randomized controlled trials with a primary composite binary endpoint and an endpoint that consists only of the clinically more important component of the CE. We propose a trial design that allows an adaptive modification of the primary endpoint based on blinded information obtained at an interim analysis. Especially, we consider a decision rule to select between a CE and its most relevant component as the primary endpoint. The decision rule chooses the endpoint with the lower estimated required sample size. Additionally, the sample size is reassessed using the estimated event probabilities and correlation, and the expected effect sizes of the composite components. We investigate the statistical power and significance level under the proposed design through simulations. We show that the adaptive design is equally or more powerful than designs without adaptive modification on the primary endpoint. Besides, the targeted power is achieved even if the correlation is misspecified at the planning stage while maintaining the type 1 error. Extensions to trials with multiple composite components of different relevance and with more than two arms are also presented. Finally, we illustrate the proposal by means of a cardiology trial using the eselect R package, which provides the implementation of the proposed design.

We discuss clinical trials that allow adaptive enrichment with prespecified subgroups at an interim analysis and assessment of treatment effect in the enriched subgroups with hypotheses related to two primary endpoints. This setting leads to various methodological, such as: (1) different subgroups may be selected at an interim analysis for each endpoint; (2) depending on the flexibility of the designs, more than one source of multiplicity need be considered due to multiple endpoints, subgroups, and analyses.

In this presentation, we consider a two-stage design where a test intervention is compared with a control intervention with possible adaptations based on conditional power after Stage 1, to enrich to common subgroups for both endpoints. Multiple hypotheses are assessed by following the closure principle and combination tests are used for combining the stagewise p-values. The implications of these designs on power and sample size under the Type I error control are discussed. We illustrate the approaches with an example.

The growth of Bayesian adaptive designs has been hampered by the lack of software readily available to statisticians. Part of the problem is due to the burden generated by Monte Carlo Markov Chains (MCMC) typically used to compute posterior distributions.

In this work, we follow a different approach based on the Laplace approximation to circumvent MCMC. The aim of this project is to provide a flexible structure for the fast simulation of Bayesian adaptive designs. We focus our attention on multi-arm multi-stage (MAMS) designs investigated as a first step. We will illustrate how the BATS package (Bayesian Adaptive Trials Simulator) can be used to define the operating characteristics of a Bayesian adaptive design for different types of endpoints given the most common adaptations - stopping trial/arms for efficacy or futility, fixed or (covariate-adjusted) response-adaptive randomisation - based on self-defined rules. Other important features include: parallel processing, customisability, use on a cluster computer or PC/Mac, adjustment for covariates.

BATS has been successfully used for the recent rounds of MRFF or NHMRC grant applications.