Population-Based Brain Tumor Survival Analysis via Spatial- and Temporal-Smoothing

In cancer research, population-based survival analysis has played an important role. In this article, we conduct survival analysis on patients with brain tumors using the SEER (Surveillance, Epidemiology, and End Results) database from the NCI (National Cancer Institute). It has been recognized that cancer survival models have spatial and temporal variations which are caused by multiple factors, but such variations are usually not “abrupt” (that is, they should be smooth). As such, spatially and temporally pooling all data and analyzing each spatial/temporal point separately are either inappropriate or ineffective. In this article, we develop and implement a spatial- and temporal-smoothing technique, which can effectively accommodate spatial/temporal variations and realize information borrowing across spatial/temporal points. Simulation demonstrates effectiveness of the proposed approach in improving estimation. Data on a total of 123,571 patients with brain tumors diagnosed between 1911 and 2010 from 16 SEER sites is analyzed. Findings different from separate estimation and simple pooling are made. Overall, this study may provide a practically useful way for modeling the survival of brain tumor (and other cancers) using population data.