PURPOSE: Life expectancy has become a core consideration in prostate cancer care. While multiple prediction tools exist to support decision making, their discriminative ability remains modest, which hampers usage and utility. We examined whether combining patient reported and claims based health measures into prediction models improves performance. MATERIALS AND METHODS: Using SEER (Surveillance, Epidemiology, and End Results)-CAHPS (Consumer Assessment of Healthcare Providers and Systems) we identified men 65 years old or older diagnosed with prostate cancer from 2004 to 2013 and extracted 4 types of data, including demographics, cancer information, claims based health measures and patient reported health measures. Next, we compared the performance of 5 nested competing risk regression models for other cause mortality. Additionally, we assessed whether adding new health measures to established prediction models improved discriminative ability. RESULTS: Among 3,240 cases 246 (7.6%) died of prostate cancer while 631 (19.5%) died of other causes. The National Cancer Institute Comorbidity Index score was associated but weakly correlated with patient reported overall health (p