Earlier in December the FDA released a new framework outlining how they will use Real World Evidence (RWE) and Real World Data (RWD) in decision making for new drugs and biologics. Under the new framework, the FDA will use the following three criteria to evaluate RWE/RWD:
1. Whether the RWD are fit for use;
2. Whether the trial or study design used to generate RWE can provide adequate scientific evidence to answer or help answer the regulatory question; and
3. Whether the study conduct meets FDA regulatory requirements (e.g., for study monitoring and data collection)
In the context of retrospective observational studies using RWD, FDA will focus on critical questions such as the following:
1. What are the characteristics of the data (e.g., contain data on a relevant endpoint, consistency in documentation, lack of missing data) that improve the chance of a valid result?
2. What are the characteristics of the study design and analysis that improve the chance of a valid result?
a. Can an active comparator improve the chance of a valid result?
b. Given potential unmeasured confounders in non-randomized RWD studies, as well as potential measurement variability in RWD, is there a role for non-inferiority designs?
3. What sensitivity analyses and statistical diagnostics should be prespecified for observational studies using RWD to generate RWE for effectiveness?