Statistical methods for a phase II oncology trial with a growth modulation index (GMI) endpoint

For cytostatic cancer therapies, alternatives to traditional phase II endpoints are needed. Von Hoff (1998) proposed an intrapatient progression-free survival (PFS) ratio, the growth modulation index (GMI). Current practice in estimation of the GMI success rate is conservative and omits a measure of uncertainty. We investigated nonparametric and parametric methods to estimate the GMI success rate, including an approach using midranks for paired survival outcomes (Hudgens and Satten (2002)). Estimators were applied to a phase II GMI dataset (Bonetti et al. (2001)). From simulation studies, it was determined that a rank-based estimator had the most favorable statistical properties. Its point estimate bias was consistently within 1.5%; its bias and precision were robust over a range of effect and censoring scenarios. Using a proof of concept criterion of {P(GMI ≥ 1) ≥ θ}, a simulation investigation found that a θ of 50%, for sample sizes between 20 and 30 patients, had type I error of ≤ 20% and a power to detect Von Hoff's 1.33 effect of ≥ 80%. When the amount of censoring was ≥ 20%, the midrank estimator had a minimum of 14% greater power over the simple percentage estimator for the GMI success rate. Future investigations reporting the GMI should consider adopting the midrank methodology.