Cost Prediction Using a Survival Grouping Algorithm: An Application to Incident XXXX Cases.

@entity1 @entity263 @entity5
BACKGROUND: Prognostic classification approaches are commonly used in clinical practice to predict health outcomes. However, there has been limited focus on use of the general approach for predicting costs. We applied a grouping algorithm designed for large-scale data sets and multiple prognostic factors to investigate whether it improves cost prediction among older Medicare beneficiaries diagnosed with @entity263 . METHODS: We analysed the linked Surveillance, Epidemiology and End Results (SEER)-Medicare data, which included data from 2000 through 2009 for @entity1 diagnosed with @entity263 between 2000 and 2007. We split the survival data into two data sets (D0 and D1) of equal size. We trained the classifier of the Grouping Algorithm for @entity5 Data (GACD) on D0 and tested it on D1. The prognostic factors included @entity5 stage, age, race and performance status proxies. We calculated the average difference between observed D1 costs and predicted D1 costs at 5  years post-diagnosis with and without the GACD. RESULTS: The sample included 110,843 @entity1 with @entity263 . The median age of the sample was 74  years, and 10  % were African American. The average difference (mean absolute error [MAE]) per @entity1 between the real and predicted total 5-year cost was US 41,525 (MAE US 41,790; 95  % confidence interval [CI] US 41,421-42,158) with the GACD and US 43,113 (MAE US 43,639; 95  % CI US 43,062-44,217) without the GACD. The 5-year cost prediction without grouping resulted in a sample overestimate of US 79,544,508. CONCLUSION: The grouping algorithm developed for complex, large-scale data improves the prediction of 5-year costs. The prediction accuracy could be improved by utilization of a richer set of prognostic factors and refinement of categorical specifications.
