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Computational and Mathematical Methods in Medicine
Volume 2018 (2018), Article ID 1672176, 8 pages
https://doi.org/10.1155/2018/1672176
Research Article

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

1Biostatistics and Epidemiology Unit, Gustave Roussy, Université Paris-Saclay, 94805 Villejuif, France
2CESP INSERM U1018, Paris-Sud University, 94805 Villejuif, France
3Gastrointestinal Oncology Unit, Gustave Roussy, Université Paris-Saclay, 94805 Villejuif, France
4Gastrointestinal Unit, University Hospital, Reims, France

Correspondence should be addressed to Stefan Michiels; rf.yssuorevatsug@sleihcim.nafets

Received 2 May 2017; Accepted 28 November 2017; Published 11 January 2018

Academic Editor: Lev Klebanov

Copyright © 2018 Matthieu Texier et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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.