Objective. To improve the estimation of healthcare expenditures by introducing a novel method that is well-suited to situations where data exhibit strong skewness and zero-inflation.
Data Sources. Simulations, and two sources of real-world data: the 2016-2017 Medical Expenditure Panel Survey (MEPS) and the Back Pain Outcomes using Longitudinal Data (BOLD) datasets.
Study Design. The super learner is an ensemble machine learning approach that can combine several algorithms to improve estimation. We propose a two-stage super learner that is well suited for use with healthcare expenditure data by separately estimating the probability of any healthcare expenditure and the mean amount of healthcare expenditure conditional on having healthcare expenditures. These estimates can be combined to yield a single estimate of expenditures for each observation. The method can flexibly incorporate a range of individual estimation approaches for each stage of estimation, including both regression-based approaches and machine learning algorithms such as random forests. We compare the performance of the two-stage super learner with a one-stage super learner, and with multiple individual algorithms for estimation of healthcare cost under a broad range of data settings in simulated and real data. The predictive performance was compared using Mean Squared Error and R2.
Data collection/Extraction methods. MEPS data include only adults and exclude observations with missingness, BOLD data include observations without missingness.
Principal Findings. Our results indicate that the two-stage super learner has a better performance compared with a one-stage super learner and individual algorithms, for healthcare cost estimation under a wide variety of settings in simulations and empirical analyses. The improvement of the two-stage super learner over the one-stage super learner was particularly evident in settings when zero-inflation is high.
Conclusions. The two-stage super learner provides researchers an effective approach for healthcare cost analyses in environments where they cannot know the best single algorithm a priori.
Keywords. Semicontinuous data, two-part models, zero-inflation, super learning, healthcare expenditure.
Two-stage circular-circular regression with zero inflation: Application to medical sciences
