OBJECTIVES Where direct or indirect estimates of health state utility values (HSUVs) are not available, mapping algorithms can be used to generate HSUVs from health related quality of life data. In a phase II randomised study of olaparib maintenance therapy in platinum-sensitive recurrent ovarian cancer, the Functional Assessment of Cancer Therapy Ovarian (FACT-O) questionnaire was used. Although no FACT-O mapping algorithms are currently available, several algorithms using FACT-General (G) domains of FACT-O have been published. In this analysis, we applied FACT-G mapping algorithms to the FACT-O data collected in the olaparib study and compared the HSUVs generated. METHODS FACT-O data were collected at scheduled visits, and on treatment discontinuation. Three algorithms mapping FACT-G to EuroQol (EQ-5D) [(Cheung, 2009), Ordinary Least Squares (OLS) and Tobit (Longworth, 2014)] and one from FACT-G to Time-Trade-Off (Dobrez, 2007) were applied to data from the phase II study. The agreement between HSUVs was assessed using concordance correlation coefficients (CCCs), and paired t-tests for mean HSUVs. RESULTS HSUVs were generated for 93% of patients in the study. Mean predicted HSUVs using OLS and Tobit were statistically consistent (p-value=0.947), whilst Cheung and Dobrez HSUVs were different from other algorithms (p-values < 0.05). The CCCs comparing OLS to Tobit and OLS to Cheung were 0.915 and 0.851, respectively. The CCCs comparing Dobrez to the EQ-5D algorithms were 0.629 (OLS), 0.619 (Tobit) and 0.783 (Cheung). The lowest and highest mean predicted HSUVs were estimated using OLS and Dobrez, respectively. CONCLUSIONS HSUVs can be estimated from FACT-O using FACT-G mapping algorithms. Comparable HSUVs were generated using OLS and Tobit algorithms, whilst Cheung and Dobrez generated distinct HSUVs profiles. Without trial data directly comparing EQ-5D to FACT-O, it is difficult to identify the optimal mapping algorithm. Instead, a range of plausible mean HSUVs can be derived for use in cost-utility analyses.