Objectives/purpose: To review Patient Reported Outcome (PRO) labelling claims achieved in oncology in Europe and in the United States and consider the benefits, and challenges faced.

Methods: PROLabels database was searched to identify oncology products with PRO labelling approved in Europe since 1995 or in the United States since 1998. The US Food and Drug Administration (FDA) and the European Medicines Agency (EMA) websites and guidance documents were reviewed. PUBMED was searched for articles on PRO claims in oncology.

Results: Among all oncology products approved, 22 were identified with PRO claims; 10 in the United States, 7 in Europe, and 5 in both. The language used in the labelling was limited to benefit (e.g. “…resulted in symptom benefits by significantly prolonging time to deterioration in cough, dyspnoea, and pain, versus placebo”) and equivalence (e.g. “no statistical differences were observed between treatment groups for global QoL”). Seven products used a validated HRQoL tool; two used symptom tools; two used both; seven used single-item symptom measures (one was unknown). The following emerged as likely reasons for success: ensuring systematic PRO data collection; clear rationale for pre-specified endpoints; adequately powered trials to detect differences and clinically significant changes; adjusting for multiplicity; developing an a priori statistical analysis plan including primary and subgroup analyses, dealing with missing data, pooling multiple-site data; establishing clinical versus statistical significance; interpreting failure to detect change. End-stage patient drop-out rates and cessation of trials due to exceptional therapeutic benefit pose significant challenges to demonstrating treatment PRO improvement.

Conclusions: PRO labelling claims demonstrate treatment impact and the trade-off between efficacy and side effects ultimately facilitating product differentiation. Reliable and valid instruments specific to the desired language, claim, and target population are required. Practical considerations include rationale for study endpoints, transparency in assumptions, and attention to subtle variations in data.