One-sample survival tests in the presence of non-proportional hazards in oncology clinical trials

TL;DR

This paper develops and evaluates new one-sample survival tests for oncology trials, addressing non-proportional hazards and external control uncertainties, with improved power demonstrated through simulations and real data applications.

Contribution

It extends existing one-sample log-rank tests using flexible models for non-proportional hazards and introduces a max-Combo test with superior power in various scenarios.

Findings

Score tests are as conservative as OSLRT and most powerful when models match data.

Max-Combo test outperforms OSLRT across scenarios, offering higher power.

External control survival curve uncertainty impacts test performance.

Abstract

In oncology, conduct well-powered time-to-event randomized clinical trials may be challenging due to limited patietns number. Many designs for single-arm trials (SATs) have recently emerged as an alternative to overcome this issue. They rely on the (modified) one-sample log-rank test (OSLRT) under the proportional hazards to compare the survival curves of an experimental and an external control group. We extend Finkelstein's formulation of OSLRT as a score test by using a piecewise exponential model for early, middle and delayed treatment effects and an accelerated hazards model for crossing hazards. We adapt the restricted mean survival time based test and construct a combination test procedure (max-Combo) to SATs. The performance of the developed are evaluated through a simulation study. The score tests are as conservative as the OSLRT and have the highest power when the data generation matches the model underlying score tests. The max-Combo test is more powerful than the OSLRT whatever the scenarios and is thus an interesting approach as compared to a score test. Uncertainty on the survival curve estimated of the external control group and its model misspecification may have a significant impact on performance. For illustration, we apply the developed tests on real data examples.