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Cost Prediction Using a Survival Grouping Algorithm: An Application to Incident Prostate Cancer Cases
Eberechukwu Onukwugha1, Ran Qi2, Jinani Jayasekera3
1Department of Pharmaceutical Health Services Research, University of Maryland School of Pharmacy, 220 Arch Street, Baltimore, MD, 21201, USA. eonukwug@rx.umaryland.edu.
Pharmacoeconomics
|December 31, 2015
Summary
A new grouping algorithm accurately predicts 5-year healthcare costs for prostate cancer patients. This method improves cost prediction accuracy for older Medicare beneficiaries, aiding clinical and financial planning.
Area of Science:
- Health economics
- Biostatistics
- Oncology
Background:
- Prognostic classification is vital for predicting health outcomes but underutilized for cost prediction.
- Older Medicare beneficiaries with prostate cancer represent a significant population for cost analysis.
Purpose of the Study:
- To evaluate a novel grouping algorithm for improving 5-year cost prediction in prostate cancer patients.
- To assess the algorithm's effectiveness using a large dataset of Medicare beneficiaries.
Main Methods:
- Utilized linked Surveillance, Epidemiology, and End Results (SEER)-Medicare data (2000-2009).
- Employed the Grouping Algorithm for Cancer Data (GACD) on a training set (D0) and tested on an independent set (D1).
- Included prognostic factors such as cancer stage, age, race, and performance status proxies.
Main Results:
- The GACD reduced the mean absolute error (MAE) in 5-year cost prediction from US$43,639 to US$41,790.
- The algorithm demonstrated a significant improvement over traditional methods, avoiding a substantial sample overestimate of over US$79 million.
Conclusions:
- The developed grouping algorithm enhances the prediction of 5-year healthcare costs for prostate cancer patients.
- Future improvements may involve incorporating a broader range of prognostic factors and refining categorical specifications for greater accuracy.
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