The clinical viewpoint: definitions, limitations of RECIST, practical considerations of measurement

Abstract

In selecting an endpoint in clinical trial design, it is important to consider that the endpoint is both reliably measured and clinically meaningful. As such, overall survival (OS) has traditionally been considered the most clinically relevant and convincing endpoint in clinical trial design as long as it is accompanied by preservation in quality of life. However, progression-free survival (PFS) is increasingly more prominent in clinical trial design because of feasibility issues (smaller sample sizes and shorter follow-up). PFS has the advantage of taking into account not only responsive disease, but stable disease as well, an issue of particular importance in the relapsed and refractory setting in which therapies are often associated with a minimal to nil response but may still confer a survival advantage. Finally, PFS has a significant advantage in molecularly selected populations, in whom OS advantages are difficult to detect due to the effects of crossover. With an understanding of the limitations and biases that are introduced with PFS as a primary endpoint, we believe that PFS is not only a viable but also a necessary alternative to OS in assessing the efficacy of selected novel-targeted therapies in molecularly defined cancer populations. Ultimately, the selection of a clinical trial endpoint should not be based on a one-size-fits all approach; rather, it should be based on the specifics of the therapeutic strategy being tested and the population under study.

Figure 1

Bias and censoring in clinical trials. A, if disease evaluations are conducted at the correct time, the date of a patient’s disease progression will coincide with the prespecified evaluation interval. However, this documented date of the disease progression is susceptible to (B) evaluation bias and (C) measurement bias. D, patients lost to follow-up may not have the same risk trajectory as those remaining on trial, resulting in attrition bias. Results can also be biased by (E) informative and (F) interval censoring.

Figure 2

Kaplan–Meier (K–M) curves for progression-free survival illustrating the bias associated with censoring. The middle curve represents the “real-world” scenario in which censored patients are considered to have the same risk trajectory as surviving patients; top, the curve that would have resulted if censored patients actually survived without event; bottom, the curve that would have resulted if censored patients actually experienced an event (progression or death) in the time interval in which they were censored.

Figure 3

PFS and OS in selected phase III clinical trials in lung cancer. The ability to detect either a PFS or an OS advantage is dependent on the nature of the therapy being tested and the selection criteria for the population under study. SCLC, small-cell lung cancer.