Table 1.

Examples of factors to consider to determine whether data is fit‐for‐purpose for a labeling update

Factors	Questions to Consider
Volume of data	How many data are available to consider a proposed labeling update? Do the data come from one source or multiple sources? Is the volume of data sufficient to draw an appropriate conclusion, acknowledging that this decision may vary depending on the population under consideration (e.g., a lower volume of data may be appropriate for rare cancers or special populations, such as pediatric patients with cancer)?
Volume of experience	What is the clinical familiarity with the drug? How many years has the drug been in use? Is the proposed labeling update considered standard of care in oncology practice? Is the experience with the drug reflective of modern oncology practice, or is the experience reflective of historical practice?
Longitudinal follow‐up	Are data available to assess patient outcomes over extended periods of time? Is longitudinal follow‐up reflective of current oncology practice?
Disparate studies	Are there conflicting studies that call into question a proposed labeling update? How do the quality and quantity of conflicting studies compare to those that support a labeling update?
Quality of the data	What is the rigor of the study design? Is the proportion of missing data reasonable? What is the follow‐up period? What are the number of events for the primary endpoint? Can data quality be systematically assessed?
Relevance of the data	Are the data available directly related to the proposed labeling update, or are the data being extrapolated from a differing population or geographic region, or clinical question? Are the data able to be directly derived from the drug, as opposed to a drug combination?
Risk of being incorrect	If the labeling update is later determined to be wrong, what are the clinical consequences?