Modelling of overall survival by an association between progression-free and post-progression survival using a conditional distribution. (February 2014)

• Record Type: Journal Article
• Authors: Belkacemi, Mohamed C; Castelli, Christel; Remita, Mohamed R; Fournel, Pierre; Daurès, Jean P
• Is Part Of: Statistical modelling. Volume 14:Number 1(2014)
• Page Start: 77
• Page End: 98
• Publication Date: 2014-02
• Subjects: Oncology -- overall survival -- parametric modelling -- post-progression survival -- progression-free survival; Linear models (Statistics) -- Periodicals; Mathematical models -- Periodicals; Modèles linéaires (Statistique) -- Périodiques; Modèles mathématiques -- Périodiques; Modèle statistique; Modèle linéaire; Modélisation statistique; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 519.5011
• DOI: 10.1177/1471082X13497642
• Languages: English
• ISSNs: 1471-082X

Abstract

In oncology, overall survival (OS) is the optimal endpoint for measuring the clinical benefit. However, and contrary to progression-free survival (PFS) which represents a potential surrogate endpoint of OS in clinical trials, OS often requires a long follow-up where the effect of the studied treatment may be diluted by subsequent therapies. In the literature, the relationship between PFS and OS was investigated more analytically than theoretically. We propose a new statistical modelling for OS based on the two survival times: PFS and post-progression survival (PPS) which we assumed to be linked using a conditional exponential distribution. This model allows us to test the existence of an association between PFS and PPS to better understand the process of improvement or decrement of OS. We found a closed form of the correlation coefficient between PFS and both PPS and OS. We expressed them as simple formulas in function of model parameters. One of the model parameters proved to be a correlation indicator between these survival times. We also defined the likelihood of the model in order to use the maximum likelihood estimator to estimate the model parameters from Phase III randomized clinical trial data, involving patients with locally advanced non-small cell lung cancer. The results showed a significant link between PFS and PPS and a strong association between improvements in PFS and OS.