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Activity Number: 479 - Survival Analysis II
Type: Contributed
Date/Time: Wednesday, August 1, 2018 : 8:30 AM to 10:20 AM
Sponsor: Biometrics Section
Abstract #327210
Title: Estimating Personal Cure in Colorectal Cancer Patients Using the SEER Data
Author(s): Margaret Stedman*
Companies: Stanford Univ
Keywords: Survival analysis; Cure models; Cancer Statistics; SEER data; lifetables; prediction

Personal cure is a statistic used to predict the long-term probability of dying of non-cancer related causes given the lifespan of the population. Current methods rely on adequate follow-up time and give outdated results. We propose a semi-parametric approach to modeling personal cure which combines the predicted cancer survival from the parametric mixture cure model with stratified rates of all-cause mortality from the US life tables (non-parametric). A competing risk model is used to join results from the two data sources. 2007 predictions from SEER colorectal cancer data were 83-89% for localized stage, 66-72% for regional stage, and 12-14% for distant stage. Accuracy of the mixture cure model was improved with the addition of the joinpoint method, however distant stage cure rates are likely underestimated due to less follow-up data. The probability of personal cure summarizes in one measure the risk of dying of cancer and dying of other causes making it an important statistical tool for clinical decision making.

Authors who are presenting talks have a * after their name.

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