Q-Learning in Dynamic Treatment Regimes With Misclassified Binary Outcome
Dan Liu1, Wenqing He1,2
1Department of Statistical and Actuarial Sciences, University of Western Ontario, London, N6A 5B7, Ontario, Canada.
Statistics in Medicine
|November 20, 2024
Summary
This study addresses noisy data in precision medicine, specifically misclassified outcomes affecting dynamic treatment regimes (DTRs). A new Q-learning correction method improves optimal DTR identification with inaccurate data.
Area of Science:
- Biostatistics
- Precision Medicine
- Machine Learning
Background:
- Precision medicine utilizes dynamic treatment regimes (DTRs) to optimize clinical outcomes.
- Q-learning is a key statistical method for estimating optimal DTRs.
- Existing Q-learning methods are sensitive to noisy data, particularly misclassified outcomes.
Purpose of the Study:
- To investigate the impact of outcome misclassification on identifying optimal DTRs using Q-learning.
- To propose and validate a novel correction method for Q-learning in the presence of misclassified outcomes.
Main Methods:
- Investigated the effect of outcome misclassification on Q-learning for DTR estimation.
- Developed a statistical correction method to adjust for misclassification bias.
- Conducted simulation studies to evaluate the proposed method's performance.
- Applied the method to real-world datasets: NHANES I and PATH.
Main Results:
- Outcome misclassification significantly affects the identification of optimal DTRs.
- The proposed correction method demonstrates satisfactory performance in simulation studies.
- The method effectively accommodates misclassification effects, improving DTR estimation.
Conclusions:
- Accurate DTR identification in precision medicine requires addressing data quality issues like misclassification.
- The proposed Q-learning correction method offers a robust solution for noisy outcome data.
- This work enhances the reliability of DTRs in real-world clinical applications.
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