Predicting survival in non-small cell lung cancer by preceding tumor assessment data

Abstract

There has been research evaluating the relationship and predictability of progression-free survival (PFS) on overall survival (OS) for treatments of various cancers. Most studies reported low to moderate correlation between PFS and OS. One possible reason for this may be that PFS is time to a categorical outcome (derived by dichotomizing change in tumor size) and doesn’t fully capture the correlation between tumor assessment over time as a continuous variable and OS. In this paper, we developed a data-driven model to predict future survival status at time t using both pre-treatment covariates and tumor assessment data (tumor size change from pre-treatment assessment, disease progression status, appearance of new lesions) up to prior time $\mathit{t}\ast$ ( $\mathrm{<}\mathit{t}$ ). The method is illustrated with data from a phase III non-small cell lung cancer (NSCLC) trial.