Bayesian regression models for the estimation of net cost of disease using aggregate data

Stat Methods Med Res. 2017 Jun;26(3):1110-1129. doi: 10.1177/0962280214568110. Epub 2015 Jan 23.

Abstract

Estimation of net costs attributed to a disease or other health condition is very important for health economists and policy makers. Skewness and heteroscedasticity are well-known characteristics for cost data, making linear models generally inappropriate and dictating the use of other types of models, such as gamma regression. Additional hurdles emerge when individual level data are not available. In this paper, we consider the latter case were data are only available at the aggregate level, containing means and standard deviations for different strata defined by a number of demographic and clinical factors. We summarize a number of methods that can be used for this estimation, and we propose a Bayesian approach that utilizes the sample stratum specific standard deviations as stochastic. We investigate the performance of two linear mixed models, comparing them with two proposed gamma regression mixed models, to analyze simulated data generated by gamma and log-normal distributions. Our proposed Bayesian approach seems to have significant advantages for net cost estimation when only aggregate data are available. The implemented gamma models do not seem to offer the expected benefits over the linear models; however, further investigation and refinement is needed.

Keywords: Aggregated data; Bayesian methods; gamma regression; net cost of disease; random effects.

MeSH terms

  • Bayes Theorem*
  • Cost of Illness*
  • Humans
  • Linear Models
  • Stochastic Processes