It is important that patient-reported outcome (PRO) measures used to assess cancer therapies adequately capture the benefits and risks experienced by patients, particularly when adverse event profiles differ across therapies. This study explores the case for augmenting preference-based utility measures to capture the impact of cancer treatment-related symptoms. Arguments for or against the adaptation of utility measures were identified via a focused review of the literature on PROs in cancer and modifications of measures (e.g., EQ-5D ‘bolt-ons’, QLU-C10D, FACT-8D).

Additional cancer treatment-related items could be specific (e.g., rash) or global. While specific items are easier to describe and understand, their use may miss rarer symptoms and those that are currently unknown but will arise from future medical advancements. The appropriate number of additional items, the independence of those items, and their impact on the psychometric properties of the core instrument require consideration. Alternatively, a global item could encompass all potential symptoms associated with any treatment for any disease. However, such an item may not be well-understood by general public respondents in valuation exercises.

Further challenges include the decision about whether to generate de novo value sets for the modified instrument or to map to existing tariffs. The fluctuating and transient nature of treatment-related symptoms (e.g., nausea) may be inconsistent with the methods used in conventional valuation exercises. Fluctuating symptoms could be missed by sub-optimal measure administration timing. The addition of items also poses double-counting risks.

In summary, the addition of treatment-related symptom items could increase the sensitivity of existing utility measures to capture known and unknown treatment-related symptoms in oncology, while retaining the core domains. However, more research is needed to investigate the challenges, particularly regarding valuation. We present a novel schematic to guide investigators in determining whether adapting an existing measure is necessary.