Designing therapeutic cancer vaccine trials with delayed treatment effect
Abstract
Arming the immune system against cancer has emerged as a powerful tool in oncology during recent years. Instead of poisoning a tumor or destroying it with radiation, therapeutic cancer vaccine, a type of cancer immunotherapy, unleashes the immune system to combat cancer. This indirect mechanism‐of‐action of vaccines poses the possibility of a delayed onset of clinical effect, which results in a delayed separation of survival curves between the experimental and control groups in therapeutic cancer vaccine trials with time‐to‐event endpoints. This violates the proportional hazard assumption. As a result, the conventional study design based on the regular log‐rank test ignoring the delayed effect would lead to a loss of power. In this paper, we propose two innovative approaches for sample size and power calculation using the piecewise weighted log‐rank test to properly and efficiently incorporate the delayed effect into the study design. Both theoretical derivations and empirical studies demonstrate that the proposed methods, accounting for the delayed effect, can reduce sample size dramatically while achieving the target power relative to a standard practice. Copyright © 2016 John Wiley & Sons, Ltd.
Citing Literature
Number of times cited according to CrossRef: 22
- Chenghao Chu, Shufang Liu, Alan Rong, Study design of single‐arm phase II immunotherapy trials with long‐term survivors and random delayed treatment effect, Pharmaceutical Statistics, 10.1002/pst.1976, 19, 4, (358-369), (2020).
- Zhenzhen Xu, Yongsoek Park, Ke Liu, Bin Zhu, Treating non-responders: pitfalls and implications for cancer immunotherapy trial design, Journal of Hematology & Oncology, 10.1186/s13045-020-0847-x, 13, 1, (2020).
- José L. Jiménez, Quantifying treatment differences in confirmatory trials under non-proportional hazards, Journal of Applied Statistics, 10.1080/02664763.2020.1815673, (1), (2020).
- Xue Ding, Jianrong Wu, Designing cancer immunotherapy trials with delayed treatment effect using maximin efficiency robust statistics, Pharmaceutical Statistics, 10.1002/pst.2003, 19, 4, (424-435), (2020).
- Pralay Mukhopadhyay, Wenmei Huang, Paul Metcalfe, Fredrik Öhrn, Mary Jenner, Andrew Stone, Statistical and practical considerations in designing of immuno-oncology trials, Journal of Biopharmaceutical Statistics, 10.1080/10543406.2020.1815035, (1-17), (2020).
- Marc Buyse, Everardo D. Saad, Tomasz Burzykowski, Julien Péron, Assessing Treatment Benefit in Immuno-oncology, Statistics in Biosciences, 10.1007/s12561-020-09268-1, (2020).
- Hajime Uno, Lu Tian, Is the Log-Rank and Hazard Ratio Test/Estimation the Best Approach for Primary Analysis for All Trials?, Journal of Clinical Oncology, 10.1200/JCO.19.03097, (JCO.19.03097), (2020).
- Jianrong Wu, Jing Wei, Cancer immunotherapy trial design with delayed treatment effect, Pharmaceutical Statistics, 10.1002/pst.1982, 19, 3, (202-213), (2019).
- Jing Wei, Jianrong Wu, Cancer immunotherapy trial design with cure rate and delayed treatment effect, Statistics in Medicine, 10.1002/sim.8440, 39, 6, (698-708), (2019).
- Dominic Magirr, Carl‐Fredrik Burman, Modestly weighted logrank tests, Statistics in Medicine, 10.1002/sim.8186, 38, 20, (3782-3790), (2019).
- Mahsa Keshavarz-Fathi, Nima Rezaei, Obstacles in the Development of Therapeutic Cancer Vaccines, Vaccines for Cancer Immunotherapy, 10.1016/B978-0-12-814039-0.00012-6, (153-160), (2019).
- Ellen G. Feigal, Natalie D. DeWitt, Cathy Cantilena, Carl Peck, David Stroncek, At the end of the beginning: immunotherapies as living drugs, Nature Immunology, 10.1038/s41590-019-0452-8, (2019).
- Miao Yang, Zhaowei Hua, Lan Xue, Mingxiu Hu, Z max test for delayed effect in immuno-oncology clinical trials , Journal of Biopharmaceutical Statistics, 10.1080/10543406.2019.1632873, (1-23), (2019).
- Ting Ye, Menggang Yu, A robust approach to sample size calculation in cancer immunotherapy trials with delayed treatment effect, Biometrics, 10.1111/biom.12916, 74, 4, (1292-1300), (2018).
- Zhenzhen Xu, Yongsoek Park, Boguang Zhen, Bin Zhu, Designing cancer immunotherapy trials with random treatment time‐lag effect, Statistics in Medicine, 10.1002/sim.7937, 37, 30, (4589-4609), (2018).
- Shufang Liu, Chenghao Chu, Alan Rong, Weighted log‐rank test for time‐to‐event data in immunotherapy trials with random delayed treatment effect and cure rate, Pharmaceutical Statistics, 10.1002/pst.1878, 17, 5, (541-554), (2018).
- Miki Horiguchi, Lu Tian, Hajime Uno, SuChun Cheng, Dae Hyun Kim, Deb Schrag, Lee-Jen Wei, Quantification of Long-term Survival Benefit in a Comparative Oncology Clinical Study, JAMA Oncology, 10.1001/jamaoncol.2018.0518, 4, 6, (881), (2018).
- Edward L. Korn, Boris Freidlin, Interim Futility Monitoring Assessing Immune Therapies With a Potentially Delayed Treatment Effect, Journal of Clinical Oncology, 10.1200/JCO.2018.77.7144, 36, 23, (2444-2449), (2018).
- Wei Li, Sophie Yu-Pu Chen, Alan Rong, Estimation of delay time in survival data with delayed treatment effect, Journal of Biopharmaceutical Statistics, 10.1080/10543406.2018.1534857, (1-15), (2018).
- Ray S. Lin, Larry F. León, Estimation of treatment effects in weighted log-rank tests, Contemporary Clinical Trials Communications, 10.1016/j.conctc.2017.09.004, 8, (147-155), (2017).
- Zhenzhen Xu, Bin Zhu, Yongsoek Park, Design for immuno‐oncology clinical trials enrolling both responders and nonresponders, Statistics in Medicine, 10.1002/sim.8694, 0, 0, (undefined).
- Kaifeng Lu, Sample size calculation for logrank test and prediction of number of events over time, Pharmaceutical Statistics, 10.1002/pst.2069, 0, 0, (undefined).




