45: Robust and Meaningful Cost Estimates Using Medicare Fee-for-Service Data

Music City Center 

In real-world evidence studies, estimates of healthcare costs per patient per year are often of interest. However, conventional statistical methods frequently fail to address complexities such as variable follow-up time and non-constant cost accumulation rates. Traditional methods typically estimate average costs per patient per year and geometric mean cost ratios, which can be challenging for healthcare stakeholders to interpret and often overlook how costs accrue over time. Using Medicare Fee-for-service claims data, we demonstrate more relevant estimates of costs at the group-level rather than summarizing the distribution of individual-level rates. We innovatively model cumulative costs over time and use quantile regression to estimate median costs, providing more robust and meaningful interpretations. Additionally, we examine the suitability of these methods for acute and chronic conditions.

real-world evidence

real-world data

costs

Medicare

quantile regression

cumulative costs 

Biopharmaceutical Section