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Targeted maximum likelihood estimation (TMLE) in regulatory submissions and research: a landscape analysis
Hana Lee1, Menglun Wang2, Spencer Haupert1
1Office of Biostatistics, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, USA.
The International Journal of Biostatistics
|June 17, 2026
Summary
Targeted Maximum Likelihood Estimation (TMLE) is a statistical method combining causal inference with machine learning. It is increasingly used in drug development for FDA regulatory submissions and research.
Area of Science:
- Biostatistics
- Causal Inference
- Machine Learning in Drug Development
Background:
- Targeted Maximum Likelihood Estimation (TMLE) integrates advanced machine learning with causal inference.
- TMLE offers flexible and robust statistical methods for analyzing complex data.
Purpose of the Study:
- To provide a comprehensive overview of TMLE applications in FDA Center for Drug Evaluation and Research (CDER) regulatory submissions.
- To summarize CDER's regulatory science initiatives supporting TMLE adoption.
Main Methods:
- Review of regulatory submissions where TMLE was considered for decision-making.
- Summary of FDA CDER's research and development in advanced analytical capabilities.
Main Results:
- TMLE has been applied in various clinical settings to support regulatory decision-making.
- CDER is actively advancing analytical methods to facilitate the use of TMLE.
Conclusions:
- TMLE is a valuable tool for causal inference in drug development.
- FDA CDER supports and promotes the use of advanced statistical methods like TMLE in regulatory science.
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