An interactive tool for individualized estimation of conditional survival in rectal cancer

Background: For rectal cancer patients who have already survived a period of time after diagnosis, survival probability changes and is more accurately depicted by conditional survival. The specific aim of this study was to develop an interactive tool for individualized estimation of changing prognosis for rectal cancer patients. Methods: A multivariate Cox proportional hazards (CPH) survival model was constructed using data from rectal cancer patients diagnosed from 1994 to 2003 from the Surveillance, Epidemiology, and End Results (SEER) database. Age, race, sex, and stage were used as covariates in the survival prediction model. The primary outcome variable was overall survival conditional on having survived up to 5 years from diagnosis. Results: Data from 42,830 rectal cancer patients met the inclusion criteria. The multivariate CPH model showed age, race, sex, and stage as significant independent predictors of survival. The survival prediction model demonstrated good calibration and discrimination, with a bootstrap-corrected concordance index of 0.75. A web-based prediction tool was built from this regression model that can compute individualized estimates of changing prognosis over time. Conclusions: An interactive prediction modeling tool can estimate prognosis for rectal cancer patients who have already survived a period of time after diagnosis and treatment. Having more accurate prognostic information can empower both patients and clinicians to be able to make more appropriate decisions regarding follow-up, surveillance testing, and future treatment.