Statistical methods to control for confounders in rare disease settings that use external control

ABSTRACT

In the drug development for rare disease, the number of treated subjects in the clinical trial is often very small, whereas the number of external controls can be relatively large. There is no clear guidance on choosing an appropriate statistical method to control baseline confounding in this situation. To fill this gap, we conduct extensive simulations to evaluate the performance of commonly used matching and weighting methods as well as the more recently developed targeted maximum likelihood estimation (TMLE) and cardinality matching in small sample settings, mimicking the motivating data from a pediatric rare disease. Among the methods examined, the performance of coarsened exact matching (CEM) and TMLE are relatively robust under various model specifications. CEM is only feasible when the number of controls far exceeds the number of treated, whereas TMLE has better performance with less extreme treatment allocation ratios. Our simulations suggest bootstrap is useful for variance estimation in small samples after matching.

KEYWORDS:

• Rare disease
• small sample
• external control
• real-world data
• coarsened exact matching
• TMLE

Acknowledgements

The authors would like to thank Mark Levenson for his comments and support of the project, Susan Gruber for her advice about the “tmle” R package, and Gurobi Optimization for providing the Gurobi software for the cardinality matching analysis.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Disclaimer

The views expressed in this article should not be construed to represent those of U.S. Food and Drug Administration.

Data sharing statement

Data for the motivation example are not shared.

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/10543406.2024.2341650.

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

Additional information

Funding

The author(s) reported there is no funding associated with the work featured in this article.