Evaluation of Treatment Effect with Paired Failure Times in a Single-Arm Phase II Trial in Oncology.

Matthieu Texier,Michel Ducreux,Olivier Bouché,Stefan Michiels,Jean-Pierre Pignon,Federico Rotolo

doi:10.1155/2018/1672176

Abstract

In early phase clinical trials of cytotoxic drugs in oncology, the efficacy is typically evaluated based on the tumor shrinkage. However, this criterion is not always appropriate for more recent cytostatic agents, and alternative endpoints have been proposed. The growth modulation index (GMI), defined as the ratio between the times to progression in two successive treatment lines, has been proposed for a single-arm phase II trials. The treatment effect is evaluated by estimating the rate of patients having a GMI superior to a given threshold. To estimate this rate, we investigated a parametric method based on the distribution of the times to progression and a nonparametric one based on a midrank estimator. Through simulations, we studied their operating characteristics and the impact of different design parameters (censoring, dependence, and distribution) on them. In these simulations, the nonparametric estimator slightly underestimated the rate and had slightly overconservative confidence intervals in some cases. Conversely, the parametric estimator overestimated the rate and had anticonservative confidence intervals in some cases. The nonparametric method appeared to be more robust to censoring than the parametric one. In conclusion, we recommend the nonparametric method, but the parametric method can be used as a supplementary tool.

Highlights

In oncology, if a new treatment is found to be acceptably safe in a phase I clinical trial, it can be tested in a phase II trial to look for evidence of efficacy
As the time to progression (TTP) is highly variable across patients and the degree of correlation between the paired failure times is a key feature, Von Hoff [1] proposed to evaluate the growth modulation index (GMI = TTP2/TTP1) instead, so that each patient serves as his/her own historical control
We present methods to estimate the proportion of patients having a GMI greater than a given threshold by handling censored observations, we explore their operating characteristics via simulations and we show an application on a real data set

Summary

Introduction

If a new treatment is found to be acceptably safe in a phase I clinical trial, it can be tested in a phase II trial to look for evidence of efficacy. The tumor shrinkage was the primary endpoint in phase II trials for cytotoxic cancer drugs. Von Hoff [1] and Mick et al [2] advocated for rather evaluating the time to progression (TTP) as the primary endpoint in a one-stage design. Since patients being offered phase II studies of new agents have typically failed a previous regimen, all first progressions are observed and TTP before experimental treatment, say TTP1, is known for all the patients enrolled. As the TTP is highly variable across patients and the degree of correlation between the paired failure times is a key feature, Von Hoff [1] proposed to evaluate the growth modulation index (GMI = TTP2/TTP1) instead, so that each patient serves as his/her own historical control. Because of the natural history of the disease, one expects that in general TTP2 is shorter than TTP1, which would indicate a null ratio value

Methods

Results

Conclusion