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Machine Learning for Exposure-Response Analysis: Methodological Considerations and Confirmation of Their Importance

Rashed Harun1, Eric Yang1,2, Nastya Kassir1

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|May 27, 2023
PubMed
Summary
This summary is machine-generated.

This study outlines best practices for using machine learning (ML) to analyze exposure-response (E-R) relationships, ensuring unbiased causal inference for drug development. Following these guidelines improves the reliability of E-R modeling and dose selection.

Keywords:
causal inferenceexposure-responsemachine learning

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Area of Science:

  • Pharmacometrics and Computational Biology
  • Machine Learning in Drug Discovery
  • Causal Inference Methodologies

Background:

  • Exposure-response (E-R) analysis is crucial for drug dose selection in pharmacometrics.
  • Current methods lack clear technical guidance for unbiased E-R estimation.
  • Machine learning (ML) shows promise for causal inference due to recent explainability advances.

Purpose of the Study:

  • To establish best practices for developing ML models for unbiased causal inference in E-R analysis.
  • To provide a framework for obtaining reliable E-R relationship insights.
  • To address the need for improved technical considerations in pharmacometric E-R modeling.

Main Methods:

  • Utilized simulated datasets with known E-R ground truth to develop and test ML practices.
  • Employed causal diagrams for variable selection and E-R insight generation.
  • Implemented strict data separation for model training and inference.
  • Performed hyperparameter tuning and bootstrap sampling for confidence intervals.

Main Results:

  • Developed a set of good practices for ML model development to avoid bias in causal inference.
  • Demonstrated the benefits of the proposed ML workflow using simulated data.
  • Successfully analyzed nonlinear and non-monotonic E-R relationships.

Conclusions:

  • The proposed ML workflow provides a reliable method for unbiased E-R analysis.
  • Adherence to these practices enhances the accuracy of drug dose selection.
  • This approach advances the application of ML in pharmacometrics for causal inference.