Estimating health-state utility values for patients with recurrent ovarian cancer using Functional Assessment of Cancer Therapy – General mapping algorithms

OBJECTIVES: In the absence of EuroQol 5D data, mapping algorithms can be used to predict health-state utility values (HSUVs) for use in economic evaluation. In a placebo-controlled Phase II study of olaparib maintenance therapy (NCT00753545), health-related quality of life was measured using the Functional Assessment of Cancer Therapy – Ovarian (FACT-O) questionnaire. Our objective was to generate HSUVs from the FACT-O data using published mapping algorithms. MATERIALS AND METHODS: Algorithms were identified from a review of the literature. Goodness-of-fit and patient characteristics were compared to select the best-performing algorithm, and this was used to generate base-case HSUVs for the intention-to-treat population of the olaparib study and for patients with breast cancer antigen mutations. RESULTS: Four FACT – General (the core component of FACT-O) mapping algorithms were identified and compared. Under the preferred algorithm, treatment-related adverse events had no statistically significant effect on HSU (P>0.05). Discontinuation of the study treatment and breast cancer antigen mutation status were both associated with a reduction in HSUVs (–0.06, P=0.0009; and –0.03, P=0.0511, respectively). The mean HSUV recorded at assessment visits was 0.786. CONCLUSION: FACT – General mapping generated credible HSUVs for an economic evaluation of olaparib. As reported in other studies, different algorithms may produce significantly different estimates of HSUV. For this reason, it is important to test whether the choice of a specific algorithm changes the conclusions of an economic evaluation.