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

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.

Validated competing event model for the stage I-II endometrial cancer population.

PURPOSE/OBJECTIVES(S): Early-stage endometrial cancer patients are at higher risk of noncancer mortality than of cancer mortality. Competing event models incorporating comorbidity could help identify women most likely to benefit from treatment intensification. METHODS AND MATERIALS: 67,397 women with stage I-II endometrioid adenocarcinoma after total hysterectomy diagnosed from 1988 to 2009 were identified in Surveillance, Epidemiology, and End Results (SEER) and linked SEER-Medicare databases. Using demographic and clinical information, including comorbidity, we sought to develop and validate a risk score to predict the incidence of competing mortality. RESULTS: In the validation cohort, increasing competing mortality risk score was associated with increased risk of noncancer mortality (subdistribution hazard ratio [SDHR], 1.92; 95% confidence interval [CI], 1.60-2.30) and decreased risk of endometrial cancer mortality (SDHR, 0.61; 95% CI, 0.55-0.78). Controlling for other variables, Charlson Comorbidity Index (CCI) = 1 (SDHR, 1.62; 95% CI, 1.45-1.82) and CCI >1 (SDHR, 3.31; 95% CI, 2.74-4.01) were associated with increased risk of noncancer mortality. The 10-year cumulative incidences of competing mortality within low-, medium-, and high-risk strata were 27.3% (95% CI, 25.2%-29.4%), 34.6% (95% CI, 32.5%-36.7%), and 50.3% (95% CI, 48.2%-52.6%), respectively. With increasing competing mortality risk score, we observed a significant decline in omega (ω), indicating a diminishing likelihood of benefit from treatment intensification. CONCLUSION: Comorbidity and other factors influence the risk of competing mortality among patients with early-stage endometrial cancer. Competing event models could improve our ability to identify patients likely to benefit from treatment intensification.