Analysis of uncertainty in health care cost-effectiveness studies: an introduction to statistical issues and methods

TLDR

Cost‑effectiveness analysis is now an integral part of health technology assessment and addresses whether a new treatment or program offers good value for money, yet methods for calculating confidence intervals for cost‑effectiveness ratios can be problematic when numerators or denominators are zero. This paper aims to introduce a basic decision‑making framework for cost‑effectiveness data and review recent statistical developments for analyzing uncertainty in estimates derived from clinical trials. The authors advocate visualizing joint cost‑effect differences with cost‑effectiveness acceptability curves and employ a net‑benefit formulation, which offers advantages over the standard incremental cost‑effectiveness ratio approach.

Abstract

Cost-effectiveness analysis is now an integral part of health technology assessment and addresses the question of whether a new treatment or other health care program offers good value for money. In this paper we introduce the basic framework for decision making with cost-effectiveness data and then review recent developments in statistical methods for analysis of uncertainty when cost-effectiveness estimates are based on observed data from a clinical trial. Although much research has focused on methods for calculating confidence intervals for cost-effectiveness ratios using bootstrapping or Fieller’s method, these calculations can be problematic with a ratio-based statistic where numerator and=or denominator can be zero. We advocate plotting the joint density of cost and effect differences, together with cumulative density plots known as cost-effectiveness acceptability curves (CEACs) to summarize the overall value-for-money of interventions. We also outline the net-benefit formulation of the cost-effectiveness problem and show that it has particular advantages over the standard incremental cost-effectiveness ratio formulation.