Econometric approaches in evaluating costs and outcomes within pharmacoeconomic analyses

Abstract

Cost and outcomes data within pharmacoeconomic analyses often possess distributional properties that require advanced statistical approaches to yield robust findings. An analyst’s failure to recognize and control for these characteristics may result in inappropriate evaluations of statistical associations or causal effects which may ultimately support incorrect policy decisionmaking. Given the importance of appropriate analysis and interpretation in pharmacoeconomics, the purpose of this paper is to address the more common statistical issues encountered in assessing healthcare costs or outcomes, emphasizing approaches that may be employed to analyze these data. More specifically, statistical methods used commonly with retrospective cohort analyses are presented including least squares (e.g., ordinary least squares, OLS), logarithmic transformations, log-plus-constant models, two-part models, maximum likelihood estimation (MLE), and generalized linear models (GLM) and extensions, among others.