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Designing therapeutic cancer vaccine trials with delayed treatment effect
Author(s) -
Xu Zhenzhen,
Zhen Boguang,
Park Yongsoek,
Zhu Bin
Publication year - 2016
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.7157
Subject(s) - sample size determination , cancer , cancer immunotherapy , medicine , piecewise , clinical trial , immunotherapy , cancer vaccine , hazard , log rank test , rank (graph theory) , survival analysis , statistics , computer science , oncology , mathematics , biology , mathematical analysis , ecology , combinatorics
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.