OBJECTIVES: Concept saliency and ranking tasks are used in qualitative patient-centered research to understand the “bothersomeness” of a condition or relative importance of symptoms and impacts to how patients feel and function. The findings from these tasks can provide additional context to patient preference or patient experience data in concept elicitation interviews. In saliency tasks, participants rate each symptom/impact individually on a 0-10 scale, whereas ranking tasks determine the relative bother of concepts in order. Both tasks serve similar purposes and are commonly used in patient-reported outcome (PRO) measure development. This work aimed to provide recommendations for selecting a methodology, and outline considerations to facilitate optimal data collection.

METHODS: Recent relevant experience and published studies using both methodologies across multiple therapeutic areas were reviewed. Advantages, disadvantages and practical considerations for saliency and ranking tasks were compared, to provide an evidence base for selecting the most appropriate method.

RESULTS: Saliency tasks can be time consuming and increase participant and interviewer burden, particularly if used in conditions with heterogeneous symptoms where numerous, varied experiences may be described. Additionally, consensus is lacking for published thresholds to determine when concepts are “salient”, causing difficulties for interpretation and comparisons across studies. Ranking tasks may be a more simplistic, less time-consuming alternative, as participants usually rank a small number (e.g., 5) of their most “bothersome” symptoms/impacts. However, there is no published guidance supporting the number selected, and concepts that are ranked more lowly, but still considered important, could be overlooked. The task instruction wording can also influence how they are completed.

CONCLUSIONS: Making an informed selection for saliency or ranking tasks in concept elicitation research may help to improve patient engagement in these tasks, improve data quality, and potentially lead to better understanding of patient priorities for their health.