PROGRESSION-FREE SURVIVAL AND OVERALL SURVIVAL (PFS-OS)
Progression-free survival (PFS), time-to-progression (TTP) and overall survival (OS) are commonly used endpoints in randomised controlled trials (RCTs) and observational studies of treatments for metastatic disease in solid tumour cancers. Within health economic models of cancer interventions information is needed on both PFS/TTP and OS. PFS or TTP are sometimes regarded as valid surrogate outcomes when establishing the clinical benefit of a treatment in the absence of mature data on OS. Similarly, some quantification of the relationship between PFS/TTP and OS may be used to populate the economic model as an alternative to directly modelling OS from the trial data.
The aim of this review was to examine the evidence available concerning the relationship between PFS/TTP and OS in advanced or metastatic cancer, with a view to determining the extent to which PFS/TTP can be considered a robust surrogate endpoint for OS.
This review suggests that the level of evidence available supporting a relationship between PFS/TTP and OS varies considerably by cancer type and is not always consistent even within one specific cancer type. Furthermore, even where strong consistent evidence supporting a correlation between the treatment effects is available, it is unclear how that should be converted into a quantified relationship between PFS and OS treatment effects within a cost-effectiveness model. Therefore, any cost-effectiveness analysis which makes a strong assumption regarding the relationship between PFS and OS should be treated with caution. We would support Elston and Taylor in recommending that any cost-effectiveness analysis based on a surrogate relationship between PFS and OS should be supported with a transparent explanation of how the relationship is quantified in the model and should be accompanied by sensitivity analysis exploring the uncertainty associated with that relationship and a systematic review of papers examining the relationship between PFS and OS in the relevant setting. This would allow decision makers to judge the appropriateness of the model in light of the evidence available in that specific disease area.
O Ciani, S Davis, P Tappenden, R Garside, K Stein, A Cantrell, E D Saad, M Buse, R S Taylor. Validation of surrogate endpoints in advanced solid tumors: systematic review of statistical methods, results, and implications for policy makers International Journal of Technology Assessment in Health Care 2014; 30 (3): 312-324