Improved Method to Stratify Elderly Patients With Cancer at Risk for Competing Events

J Clin Oncol. 2016 Apr 10;34(11):1270-7. doi: 10.1200/JCO.2015.65.0739. Epub 2016 Feb 16.


Purpose: To compare a novel generalized competing event (GCE) model versus the standard Cox proportional hazards regression model for stratifying elderly patients with cancer who are at risk for competing events.

Methods: We identified 84,319 patients with nonmetastatic prostate, head and neck, and breast cancers from the SEER-Medicare database. Using demographic, tumor, and clinical characteristics, we trained risk scores on the basis of GCE versus Cox models for cancer-specific mortality and all-cause mortality. In test sets, we examined the predictive ability of the risk scores on the different causes of death, including second cancer mortality, noncancer mortality, and cause-specific mortality, using Fine-Gray regression and area under the curve. We compared how well models stratified subpopulations according to the ratio of the cumulative cause-specific hazard for cancer mortality to the cumulative hazard for overall mortality (ω) using the Akaike Information Criterion.

Results: In each sample, increasing GCE risk scores were associated with increased cancer-specific mortality and decreased competing mortality, whereas risk scores from Cox models were associated with both increased cancer-specific mortality and competing mortality. GCE models created greater separation in the area under the curve for cancer-specific mortality versus noncancer mortality (P < .001), indicating better discriminatory ability between these events. Comparing the GCE model to Cox models of cause-specific mortality or all-cause mortality, the respective Akaike Information Criterion scores were superior (lower) in each sample: prostate cancer, 28.6 versus 35.5 versus 39.4; head and neck cancer, 21.1 versus 29.4 versus 40.2; and breast cancer, 24.6 versus 32.3 versus 50.8.

Conclusion: Compared with standard modeling approaches, GCE models improve stratification of elderly patients with cancer according to their risk of dying from cancer relative to overall mortality.

Publication types

  • Comparative Study
  • Research Support, N.I.H., Extramural

MeSH terms

  • Aged
  • Aged, 80 and over
  • Breast Neoplasms / complications
  • Breast Neoplasms / mortality
  • Cause of Death
  • Female
  • Head and Neck Neoplasms / complications
  • Head and Neck Neoplasms / mortality
  • Humans
  • Male
  • Medicare
  • Models, Statistical*
  • Neoplasms / complications*
  • Neoplasms / mortality*
  • Proportional Hazards Models
  • Prostatic Neoplasms / complications
  • Prostatic Neoplasms / mortality
  • Risk Assessment
  • Risk Factors
  • SEER Program
  • United States / epidemiology