Clinical Cancer Research Publishes Review of Regulatory Real-World Evidence in Successful Oncology Product Approvals

Analysis Group announced the publication of research examining the US Food and Drug Administration's (FDA's) commentary on the use of real-world evidence (RWE) in successful oncology product approvals between 2015 and 2020.

A team of researchers from Analysis Group, Pfizer, and the Dana-Farber Cancer Institute analyzed 133 original and 573 supplemental oncology new drug application and biologics license application approvals to identify the attributes of a successful RWE study that contributes to an accelerated or full drug approval.

Drug developers may prepare stronger submission dossiers for RWE studies by drawing on the research's key insights:

• Engage the FDA early to confirm appropriate data sources and whether the RWE study should be designed as a natural history study for contextualization, or as an external control study for comparison with the pivotal trial. A hybrid study design to combine trial with external control data through Bayesian or frequentist methods, and ambi-directional RWE data collection (both prospective and retrospective) are study designs worth considering.
• Select appropriate data sources to ensure that real-world data (RWD) are high quality and fit for purpose. Although chart review was the most common source for RWE, the FDA also commented that data from such studies could have limited generalizability and are subject to selection bias.
• Align the RWE and pivotal trial populations by matching on trial inclusion and exclusion criteria to the extent possible and adjusting for the remaining imbalance in baseline characteristics with propensity score weighting methodology, such as inverse probability treatment weighting. Critically, the study protocol needs to be developed a priori.
• Describe methods to minimize residual confounding and unmeasured confounding, including appropriate index date and reduction in missing values. If imputation methods are used to address missing values, validation of the imputation algorithms is recommended. The impact of unmeasured confounding should be evaluated through quantitative bias analysis.
• The analysis, "Real-world Evidence in Support of Oncology Product Registration: A Systematic Review of New Drug Application and Biologics License Application Approvals from 2015-2020," was published in Clinical Cancer Research, an American Association of Cancer Research journal. Coauthors include Bhakti Arondekar, Bryon Wornson, and Alexander Niyazov of Pfizer; and George D. Demetri of the Dana-Farber Cancer Institute and the Ludwig Center at Harvard Medical School. Funding for the work was provided by Pfizer.

Clinical Cancer Research Review