Estimating the distribution of ratio of paired event times in phase II oncology trials

Abstract

With the rapid development of new anti-cancer agents which are cytostatic, new endpoints are needed to better measure treatment efficacy in phase II trials. For this purpose, Von Hoff (1998) proposed the growth modulation index (GMI), that is, the ratio between times to progression or progression-free survival times in two successive treatment lines. An essential task in studies using GMI as an endpoint is to estimate the distribution of GMI. Traditional methods for survival data have been used for estimating the GMI distribution because censoring is common for GMI data. However, we point out that the independent censoring assumption required by traditional survival methods is always violated for GMI, which may lead to severely biased results. In this paper, we construct both nonparametric and parametric estimators for the distribution of GMI, accounting for the dependent censoring of GMI. Extensive simulation studies show that our nonparametric estimators perform well in practical situations and outperform existing estimators, and our parametric estimators perform better than our nonparametric estimators and existing estimators when the parametric model is correctly specified. A phase II clinical trial using GMI as the primary endpoint is provided for illustration.

Keywords: Phase II trial; dependent censoring; nonparametric and parametric estimators; paired event times; progression-free survival; time to progression.