Claims-Based Approach to Predict Cause-Specific Survival in Men With Prostate Cancer

Treatment decisions about localized prostate cancer depend on accurate estimation of the patient's life expectancy. Current cancer and noncancer survival models use a limited number of predefined variables, which could restrict their predictive capability. We explored a technique to create more comprehensive survival prediction models using insurance claims data from a large administrative data set. These data contain substantial information about medical diagnoses and procedures, and thus may provide a broader reflection of each patient's health.

We identified 57,011 Medicare beneficiaries with localized prostate cancer diagnosed between 2004 and 2009. We constructed separate cancer survival and noncancer survival prediction models using a training data set and assessed performance on a test data set. Potential model inputs included clinical and demographic covariates, and 8,971 distinct insurance claim codes describing comorbid diseases, procedures, surgeries, and diagnostic tests. We used a least absolute shrinkage and selection operator technique to identify predictive variables in the final survival models. Each model's predictive capacity was compared with existing survival models with a metric of explained randomness (ρ²) ranging from 0 to 1, with 1 indicating an ideal prediction.

Our noncancer survival model included 143 covariates and had improved survival prediction (ρ² = 0.60) compared with the Charlson comorbidity index (ρ² = 0.26) and Elixhauser comorbidity index (ρ² = 0.26). Our cancer-specific survival model included nine covariates, and had similar survival predictions (ρ² = 0.71) to the Memorial Sloan Kettering prediction model (ρ² = 0.68).

Survival prediction models using high-dimensional variable selection techniques applied to claims data show promise, particularly with noncancer survival prediction. After further validation, these analyses could inform clinical decisions for men with prostate cancer.