002 - Review of statistical methods for analyzing healthcare cost outcomes in administrative data

Healthcare costs are increasing at alarming rates in the United States (US) putting a heavy burden to the healthcare reimbursement system. Cost and cost savings have become an important focus as health policy administrators are tasked with determining the most effective allocation of limited resources. The availability of large databases, such as administrative data, comes with many challenges for analyses, including: skewed data, inflated zero counts, and potential selection bias among comparison groups. Thus, it is imperative that they are evaluated correctly. There are many different methods currently being used to estimate costs including: generalized linear models with a log link, natural logarithm transformed costs, gamma distribution, median regression, two-part models, and Bayesian models. This review will identify which methods are statistically and mathematically appropriate for large claims data.
Scopus and Ovid were searched for potential statistical method papers using multivariable modelling of cost. Inclusion criteria required either a comparison of two or more statistical methods to analyze cost or one statistical method performed on two or more different types of cost data.
The review identified 1,048 potential papers, of which, 80 met the inclusion criteria for a full article review. There was a total of 9 papers included in the review; one paper looked at simulations and eight papers assessed real cost data. There were 28 models assessed across the nine papers with ordinary least squares (OLS) and generalized linear models (GLM) being the most common.
GLM using the gamma distribution was included in all but two of the comparisons. Most other models that were compared to the GLM Gamma distribution with log link found it to be the superior model in both simulated data and real administrative data.

Cost data

healthcare cost

healthcare cost analysis

generalized linear models

ordinary least squares models

administrative data 

Mid-Level

Knowledge

International Conference on Health Policy Statistics 2023