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

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‐fr...

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Bibliographic Details
Published in:Statistics in medicine 2023-02, Vol.42 (3), p.388-406
Main Authors: Chen, Li, Burkard, Mark, Wu, Jianrong, Kolesar, Jill M., Wang, Chi
Format: Article
Language:English
Subjects:
Antineoplastic Agents - therapeutic use
Cancer
Cancer therapies
Clinical trials
Clinical Trials, Phase II as Topic
Computer Simulation
dependent censoring
Humans
Medical Oncology
Neoplasms - drug therapy
nonparametric and parametric estimators
Nonparametric statistics
Oncology
paired event times
Phase II trial
progression‐free survival
Ratios
Survival Analysis
time to progression
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Summary: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.
ISSN:0277-6715
1097-0258
DOI:10.1002/sim.9622