Do economic evaluations of targeted therapy provide support for decision makers?

Decision makers must make decisions without complete information. That uncertainty can be decreased when economic evaluations use local data and can be quantified by considering the variability of all model inputs concurrently per international evaluation guidelines. It is unclear how these recommendations have been implemented in evaluations of targeted cancer therapy. By using economic evaluations of adjuvant trastuzumab, we have assessed the extent to which decision support recommendations were adopted.

Systematic review.

Published economic evaluations of adjuvant trastuzumab treatment in early-stage breast cancer were examined as an established example of targeted therapy. Canadian, United Kingdom, and US economic evaluation guidelines were reviewed to establish extraction criteria. Extraction characterized the use of effectiveness evidence and local data sources for model parameters, sensitivity analysis methods (scenario, univariate, multivariate, and probabilistic), and uncertainty representation (ie, cost-effectiveness plane, scatterplot, confidence ellipses, tornado diagrams, cost-effectiveness acceptability curve).

Fifteen economic evaluations of adjuvant trastuzumab were identified in the literature. Local data were used to estimate costs (15 of 15) and utilities rarely (2 of 15) but not trastuzumab efficacy. Univariate sensitivity analysis was most common (12 of 15), whereas probabilistic analysis was less frequent (10 of 15). Two-thirds of all studies provided visual representation of results and decision uncertainty.

Authors of adjuvant trastuzumab economic evaluations rarely use local data beyond costs. Quantification of uncertainty and its representation also fell short of guideline recommendations. This review demonstrates that economic evaluations of adjuvant trastuzumab, as an example of targeted cancer therapy, can be improved for decision-making support.