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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

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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.

Keywords:

AI TMLE causal inference regulatory initiatives regulatory submissions

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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.