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.

Keywords: cancer clinical trial; cancer immunotherapy; delayed treatment effect; non-proportional hazard assumption; sample size and power calculation; therapeutic cancer vaccine.

Figure 1

Kaplan–Meier estimates of overall survival in the pivotal study underlying the approval of Sipuleucel-T.

Figure 2

Power of APPLE, SEPPLE methods, and regular log-rank test ignoring the delayed treatment effect given sample size and hazard ratio, where the power of APPLE method is set at 80%. The treatment effect delayed duration t^* = 6 months; proportion of subjects who can survive beyond the delayed phase p = 0.7 (i.e., h_C = 0.0019); Type I error rate α = 0.05.

Figure 3

Power of APPLE, SEPPLE methods, and regular log-rank test ignoring the delayed treatment effect given sample size and hazard ratio under different values of proportions of subjects who can survive beyond the delayed phase p = 70%, 80%, and 90%. Total accrual duration A = 1 year; total study duration τ = 3 years; the treatment effect delayed duration t^* = 6 months; Type I error rate α = 0.05.

Figure 4

Power of APPLE, SEPPLE methods, and regular log-rank test ignoring the delayed treatment effect given sample size and hazard ratio under different values of treatment effect delayed duration t^* = 3 months, 6months, and 9 months. Total accrual duration A = 1 year; total study duration τ = 3 years; proportion of subjects who can survive beyond the delayed phase p = 70%; Type I error rate α = 0.05.