Plain Language Summary

This research is important because it examines how clinical trials with no comparative group can be compared to external groups using individual patient data from real-world settings. These comparisons are increasingly necessary for regulatory and reimbursement decisions regarding new treatments. The study reveals a significant gap between advanced analytical methods discussed in academic literature and those used in practice. While guidelines and scientific literature endorse sophisticated methods, regulatory assessments often rely on simpler, less transparent approaches.

The review highlights several key challenges when using external comparative groups, such as data quality issues, biases, and the complexity of methodologies. It underscores the importance of engaging stakeholders early in the process, providing better guidance, and establishing a more consistent approach to enhance the rigor and acceptance of trials that make comparisons with external real-world data. Current guidelines do not specify when these are acceptable. However, the literature emphasizes the need for high-quality real-world data and suggests that different methods—like matching, weighting, and other advanced statistical techniques—should be employed to account for potential confounding factors.

There are notable variations in the guidelines provided by different organizations. Some focus more on research design than on the specific analytical methods to be used. While individual patient data is generally preferred for building external comparator groups, many reports still rely on aggregated data, which may not meet the rigorous standards set out in the guidelines.

Transparency is another critical issue, as regulatory reports frequently lack clear descriptions of the analytical methods applied, making it hard to evaluate the reliability of their findings. The study stresses the need for continuous dialogue among all parties involved to tackle uncertainties in the use of real-world data.

The review culminates in 12 recommendations aimed at improving the quality and acceptability of analytical methods in regulatory and reimbursement submissions: the necessity for a priori defined protocols, clarity in research questions, and the importance of testing the results in different contexts. Overall, the study calls for a more standardized approach to the use of real-world data in practice to ensure comparator groups are built according to the highest methodological standards.

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