Abstract
Relying on a single primary endpoint in randomized controlled trials (RCTs) is often infeasible, for example due to rare or heterogeneous events. Regulatory guidance therefore allows multiple endpoints, but different analytical strategies address different scientific questions and null hypotheses, even when applied to the same set of variables. We explored three approaches to consider multiple endpoints in the primary analysis of RCTs, as stated in the FDA and EMA guidelines on multiplicity: (i) a composite endpoint (CE), (ii) multiple testing and multiplicity correction (MTMC), and (iii) a hierarchical non-parametric procedure, called generalized pairwise comparisons (GPC). Using clinical trial simulations, we compared these strategies’ power in two-arm RCTs perform when testing strategy-specific hypotheses across a range of scenarios reflecting endpoint prioritization, correlation between endpoints, and opposing treatment effects. When testing time-to-event endpoints, global testing strategies (CE and GPC) generally achieved higher power than MTMC. However, we also demonstrate that global procedures may yield statistically significant results even when treatment effects are heterogeneous across endpoints, underscoring the importance of careful interpretation and component-wise assessment. As trials increasingly use multiple endpoints, understanding the trade-off between statistical efficiency and interpretability, and provide practical guidance for choosing endpoint definitions and primary analysis strategies in future trials.
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Data availability
The simulated datasets used and analysed during the current study are available from the corresponding author on reasonable request.
Code availability
The R-code for all simulations conducted is available from the authors upon request.
References
Sankoh, A. J., Li, H. & D’Agostino, R. B. Sr. Use of composite endpoints in clinical trials. Stat. Med. 33, 4709–4714. https://doi.org/10.1002/sim.6205 (2014).
Ristl, R., Urach, S., Rosenkranz, G. & Posch, M. Methods for the analysis of multiple endpoints in small populations: A review. J. Biopharmaceut. Stat. 29, 1–29 (2019).
The European Agency for the Evaluation of Medicinal Products (EMEA). Points to consider on multiplicity issues in clinical trials. In Technical Report CPMP/EWP/908/99, European Medicines Agency (2002). Accessed 20 Jan 2025 (2025).
The European Agency for the Evaluation of Medicinal Products (EMEA). Ich e9: Statistical principles for clinical trials. In Technical Report CPMP/ICH/363/96, European Medicines Agency (1998). Accessed 22 Sep 2024 (2024).
U.S. Food and Drug Administration. Multiple endpoints in clinical trials: Guidance for industry. In Technical Report FDA-2016-D-4460, U.S. Department of Health and Human Services, Food and Drug Administration,Center for Biologics Evaluation and Research (CBER), Center for Drug Evaluation and Research (CDER). Accessed 29 Oct 2024 (2022).
Cortés-Martínez, J., Bofill-Roig, M. & Gómez-Melis, G. Design of trials with composite endpoints with the r package comparedesign. Stat. Biosci. 1–23. https://doi.org/10.1007/s12561-025-09488-3 (2025).
Del Paggio, J. C. et al. Evolution of the randomized clinical trial in the era of precision oncology. JAMA Oncol. 7, 728–734. https://doi.org/10.1001/jamaoncol.2021.0379 (2021).
European Medicines Agency. Guideline on clinical investigation of immunosuppressants for solid organ transplantation. In Technical Report CHMP/EWP/263148/06, European Medicines Agency, Committee for Medicinal Products for Human Use (CHMP). Accessed 10 June 2025 (2008).
Srinivas, T. R. & Oppenheimer, F. Identifying endpoints to predict the influence of immunosuppression on long-term kidney graft survival. Clin. Transplant. 29, 644–653. https://doi.org/10.1111/ctr.12554 (2015).
Knoll, G. A. et al. Ramipril versus placebo in kidney transplant patients with proteinuria: A multicentre, double-blind, randomised controlled trial. Lancet Diabetes Endocrinol. 4, 318–326. https://doi.org/10.1016/S2213-8587(15)00368-X (2016).
Sautenet, B. et al. Range and consistency of outcomes reported in randomized trials conducted in kidney transplant recipients: A systematic review. Transplantation 102, 2065–2071. https://doi.org/10.1097/TP.0000000000002278 (2018).
Buyse, M. Generalized pairwise comparisons of prioritized outcomes in the two-sample problem. Stat. Med. 29, 3245–3257. https://doi.org/10.1002/sim.3923 (2010).
Pocock, S. J., Ariti, C. A., Collier, T. J. & Wang, D. The win ratio: A new approach to the analysis of composite endpoints in clinical trials based on clinical priorities. Eur. Heart J. 33, 176–182. https://doi.org/10.1093/eurheartj/ehr352 (2011).
Fergusson, N. A., Ramsay, T., Chassé, M., English, S. W. & Knoll, G. A. The win ratio approach did not alter study conclusions and may mitigate concerns regarding unequal composite end points in kidney transplant trials. J. Clin. Epidemiol. 98, 9–15. https://doi.org/10.1016/j.jclinepi.2018.02.001 (2018).
Abramowicz, D. et al. Recent advances in kidney transplantation: A viewpoint from the Descartes Advisory Board. Nephrol. Dial. Transplant. 33, 1699–1707. https://doi.org/10.1093/ndt/gfx365 (2018).
Anwar, I. J., Srinivas, T. R., Gao, Q. & Knechtle, S. J. Shifting clinical trial endpoints in kidney transplantation: The rise of composite endpoints and machine learning to refine prognostication. Transplantation 106, 1558–1564. https://doi.org/10.1097/TP.0000000000004107 (2022).
Hariharan, S., McBride, M. A. & Cohen, E. P. Evolution of endpoints for renal transplant outcome. Am. J. Transplant. 3, 933–941. https://doi.org/10.1034/j.1600-6143.2003.00176.x (2003).
Bofill Roig, M. & Gómez Melis, G. A new approach for sizing trials with composite binary endpoints using anticipated marginal values and accounting for the correlation between components. Stat. Med. 38, 1935–1956. https://doi.org/10.1002/sim.8092 (2019).
Morris, T. P., White, I. R. & Crowther, M. J. Using simulation studies to evaluate statistical methods. Stat. Med. 38, 2074–2102. https://doi.org/10.1002/sim.8086 (2019).
Smith, M. K. & Marshall, A. Importance of protocols for simulation studies in clinical drug development. Stat. Methods Med. Res. 20, 613–622. https://doi.org/10.1177/0962280210378949 (2011).
Friede, T. et al. Refinement of the clinical scenario evaluation framework for assessment of competing development strategies with an application to multiple sclerosis. Drug Inf. J. DIJ/Drug Inf. Assoc. 44, 713–718. https://doi.org/10.1177/009286151004400607 (2010).
Benda, N., Branson, M., Maurer, W. & Friede, T. Aspects of modernizing drug development using clinical scenario planning and evaluation. Drug Inf. J. DIJ/Drug Inf. Assoc. 44, 299–315. https://doi.org/10.1177/009286151004400312 (2010).
Haupenthal, F. et al. A multicentre, patient-and assessor-blinded, non-inferiority, randomised and controlled phase II trial to compare standard and torque Teno virus-guided immunosuppression in kidney transplant recipients in the first year after transplantation: Ttvguideit. Trials 24, 213. https://doi.org/10.1186/s13063-023-07216-0 (2023).
Herkner, F. et al. Statistical analysis plan for ttvguideit-A multicentre, patient-and assessor-blinded, non-inferiority, randomised and controlled phase ii trial to compare standard and torque Teno virus-guided immunosuppression in kidney transplant recipients in the first year after transplantation. Trials 26, 1–17. https://doi.org/10.1186/s13063-025-09119-8 (2025).
Chan, S. et al. Range and consistency of infection outcomes reported in trials conducted in kidney transplant recipients: A systematic review. Transplantation 105, 2632–2638. https://doi.org/10.1097/TP.0000000000003723 (2021).
Solez, K. et al. International standardization of criteria for the histologic diagnosis of renal allograft rejection: The Banff working classification of kidney transplant pathology. Kidney Int. 44, 411–422. https://doi.org/10.1038/ki.1993.259 (1993).
Green, M. et al. Foreword: 4th edition of the American Society of Transplantation infectious diseases guidelines. Clin. Transplant. 33, e13642. https://doi.org/10.1111/ctr.13642 (2019).
Gray, R. J. A class of k-sample tests for comparing the cumulative incidence of a competing risk. Ann. Stat. 16, 1141–1154 (1988).
Simes, R. An improved Bonferroni procedure for multiple tests of significance. Biometrika 73, 751–754. https://doi.org/10.1093/biomet/73.3.751 (1986).
Hommel, G. A stagewise rejective multiple test procedure based on a modified Bonferroni test. Biometrika 75, 383–386. https://doi.org/10.1093/biomet/75.2.383 (1988).
Yousefi, E. et al. Efficiency of multivariate tests in trials in progressive supranuclear palsy. Sci. Rep. 14, 25581. https://doi.org/10.1038/s41598-024-76668-4 (2024).
Buyse, M. et al. Handbook of Generalized Pairwise Comparisons: Methods for Patient-Centric Analysis (CRC Press, 2025).
Péron, J., Buyse, M., Ozenne, B., Roche, L. & Roy, P. An extension of generalized pairwise comparisons for prioritized outcomes in the presence of censoring. Stat. Methods Med. Res. 27, 1230–1239. https://doi.org/10.1177/0962280216658320 (2018).
Deltuvaite-Thomas, V. et al. Generalized pairwise comparisons for censored data: An overview. Biometric. J. 65, 2100354. https://doi.org/10.1002/bimj.202100354 (2023).
Ozenne, B., Budtz-Jørgensen, E. & Péron, J. The asymptotic distribution of the net benefit estimator in presence of right-censoring. Stat. Methods Med. Res. 30, 2399–2412. https://doi.org/10.1177/09622802211037067 (2021).
Redfors, B. et al. The win ratio approach for composite endpoints: Practical guidance based on previous experience. Eur. Heart J. 41, 4391–4399. https://doi.org/10.1093/eurheartj/ehaa665 (2020).
Meyer, E. L. et al. Why and how should we simulate platform trials? Learnings from eu-pearl. BMC Med. Res. Methodol. 25, 12. https://doi.org/10.1186/s12874-024-02453-6 (2025).
Schoenen, S., Heussen, N., Verbeeck, J. & Hilgers, R.-D. The impact of allocation bias on test decisions in clinical trials with multiple endpoints using multiple testing strategies. BMC Med. Res. Methodol. 24, 223. https://doi.org/10.1186/s12874-024-02335-x (2024).
Trivedi, P. K. & Zimmer, D. M. Copula modeling: An introduction for practitioners. Found. Trends Econ. 1, 1–111. https://doi.org/10.1561/0800000005 (2007).
Verbeeck, J. et al. Generalized pairwise comparison methods to analyze (non) prioritized composite endpoints. Stat. Med. 38, 5641–5656. https://doi.org/10.1002/sim.8388 (2019).
Großhennig, A., Thomas, N. H., Brannath, W. & Koch, A. How to avoid concerns with the interpretation of two primary endpoints if significant superiority in one is sufficient for formal proof of efficacy. Pharmaceut. Stat. 22, 836–845. https://doi.org/10.1002/pst.2314 (2023).
Röhmel, J., Gerlinger, C., Benda, N. & Läuter, J. On testing simultaneously non-inferiority in two multiple primary endpoints and superiority in at least one of them. Biometric. J. J. Math. Methods Biosci. 48, 916–933. https://doi.org/10.1002/bimj.200510289 (2006).
Bloch, D. A., Lai, T. L., Su, Z. & Tubert-Bitter, P. A combined superiority and non-inferiority approach to multiple endpoints in clinical trials. Stat. Med. 26, 1193–1207. https://doi.org/10.1002/sim.2611 (2007).
Ferreira, J. P. et al. Use of the win ratio in cardiovascular trials. JACC Heart Fail. 8, 441–450. https://doi.org/10.1016/j.jchf.2020.02.010 (2020).
Butler, J., Stockbridge, N. & Packer, M. Win ratio: A seductive but potentially misleading method for evaluating evidence from clinical trials. Circulation 149, 1546–1548. https://doi.org/10.1161/CIRCULATIONAHA.123.067786 (2024).
Ajufo, E., Nayak, A. & Mehra, M. R. Fallacies of using the win ratio in cardiovascular trials. JACC Basic Transl. Sci. 8, 720–727. https://doi.org/10.1016/j.jacbts.2023.05.004 (2023).
Gregson, J. et al. Recurrent events in cardiovascular trials. JACC 82, 1445–1463. https://doi.org/10.1016/j.jacc.2023.07.024 (2023).
Heinz Schmidli, J. H. R. & Akacha, M. Estimands for recurrent event endpoints in the presence of a terminal event. Stat. Biopharmaceut. Res. 15, 238–248. https://doi.org/10.1080/19466315.2021.1895883 (2023).
Fritsch, A. et al. Efficiency comparison of analysis methods for recurrent event and time-to-first event endpoints in the presence of terminal events-application to clinical trials in chronic heart failure. Stat. Biopharmaceut. Res. 15, 268–279. https://doi.org/10.1080/19466315.2021.1945488 (2023).
European Medicines Agency. Ich e9 (r1) addendum on estimands and sensitivity analysis in clinical trials to the guideline on statistical principles for clinical trials. In Technical Report EMA/CHMP/ICH/436221/2017, European Medicines Agency. Accessed 22 Sep 2024 (2020).
Bardo, M. et al. Methods for non-proportional hazards in clinical trials: A systematic review. Stat. Methods Med. Res. 33, 1069–1092. https://doi.org/10.1177/09622802241242325 (2024).
Klinglmüller, F. et al. A comparison of statistical methods for time-to-event analyses in randomized controlled trials under non-proportional hazards. Stat. Med. 44, e70019. https://doi.org/10.1002/sim.70019 (2025).
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This project has received funding from the European Commission Horizon 2020 Research and Innovation Action (project number: 896932; project name: TTVguideTX; project coordinator: Gregor Bond)
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FH and FK designed the study including the statistical methodology and simulation study. FH prepared the initial draft of the manuscript under the supervision of FK and GB. FH performed the statistical analysis, performed simulations and prepared all figures/tables under the supervision of FK. GB contributed to the clinical nephrological background. MP gave critical input to the statistical methodology and the design and conduct of the clinical trial simulations. All authors discussed the results, provided comments and reviewed the manuscript.
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Herkner, F., Posch, M., Bond, G. et al. Comparison of primary analysis strategies of randomized controlled trials with multiple endpoints with application to kidney transplantation. Sci Rep (2026). https://doi.org/10.1038/s41598-026-38979-6
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DOI: https://doi.org/10.1038/s41598-026-38979-6


