OBJECTIVES: Value frameworks in health care are proliferating often with very little input from economists. In particular, a number of oncology value frameworks have emerged, perhaps due to the greater costs of novel therapies in the cancer space. But none of these value frameworks include weights that economists would recognise as legitimate values. Building on previous qualitative work that identified health and non-health attributes of cancer treatment, the aim of this study was to design a discrete choice experiment (DCE) to identify trade-offs between identified attributes. METHODS: Previously reported qualitative focus group work with cancer patients, oncology clinicians and oncology nurses identified treatment convenience, existence of treatment alternatives, disease rarity, quality of evidence and prognosis without treatment as important attributes alongside the traditional health gain associated with treatment. These results informed the design and undertaking of a DCE piloted in June 2017 with a convenience sample (n=45) where subjects where asked to choose between covering two alternative treatments in a new health plan. Quality-adjusted life expectancy (QALE) is used as a payment vehicle and the pilot tested whether respondents would be willing to give up health gains associated with treatment for the other identified attributes. RESULTS: Conditional logistic regression identified statistically significant preferences for less inconvenient treatments, treatments with no alternatives, higher evidence quality, helping those with shorter prognosis and provide more QALE gains. Only the number of people affected (rarity of disease) was insignificant. CONCLUSIONS: The pilot phase of this ongoing study demonstrates that whilst the health-gains of cancer therapies are predominantly prioritised, other attributes are important and participants were prepared to trade health gains. The pilot results will be used to inform the design of a population study to identify the relative weight for the creation of a value framework in oncology with economically robust weights.