Speaker
Description
Graph-based multiple testing procedures provide an intuitive way to define closed testing strategies that control the family-wise error rate (FWER) in fixed sample settings [1]. They have been extended to adaptive trial designs based on the (partial) conditional error rate (CER) method [2]. These procedures control the FWER in two-stage designs where the trial is adapted after an interim analysis based on unblinded data. When (part of) the correlation structure between test statistics is known, it can be directly incorporated into the testing procedure to improve efficiency [2, 3].
Building on these methods, we introduce adagraph, an R package implementing graph-based (partial) CER tests for adaptive two-stage trial designs, extending current R packages for graph-based multiple testing, such as [4]. The package constructs adaptive closed testing procedures from any user-specified graph-based fixed sample multiple test. It covers tests for trials with multiple arms, confirmatory subgroup analyses, multiple endpoints and combinations thereof, as well as different endpoint types. The implemented approach accounts for stochastically dependent test statistics under the assumption that they are (approximatealy) multivariate normally distributed with a known correlation structure, and uses tests based on the Bonferroni inequality otherwise. The package allows for a range of adaptations based on unblinded interim data, such as sample size reassessment, the selection of arms, endpoints or subgroups, and changes to the testing strategy.
adagraph supports arbitrary correlation structures between test statistics and provides functions to compute those for several common trial designs. This includes trials comparing multiple arms to a shared control group and trials testing pre-specified subgroups alongside the full population. To explore the operating characteristics of the defined adaptive trial designs, adagraph can simulate these trials, providing methods for data generation and trial adaptations. We demonstrate adagraph with several case studies illustrating its practical use for planning adaptive trials with multiple hypotheses.
References
[1] F. Bretz et al. “A graphical approach to sequentially rejective multiple test procedures”. In: Statistics in Medicine 28.4 (2009), pp. 586–604.
[2] F. Klinglmüller, M. Posch, and F. König. “Adaptive graph-based multiple testing procedures”. In: Pharmaceutical Statistics 13.6 (2014), pp. 345–356.
[3] C. Mehta, A. Mukhopadhyay, and M. Posch. Graph Based, Adaptive, Multi Arm, Multiple Endpoint, Two Stage Design. arXiv:2501.03197 (2025).
[4] K. Rohmeyer and F. Klinglmüller. gMCP: Graph Based Multiple Test Procedures. R package version 0.8-17. 2024.
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