Machine learning methods for propensity and disease risk score estimation in high-dimensional data: a plasmode simulation and real-world data cohort analysis
Introduction: Machine learning (ML) methods are promising and scalable alternatives for propensity score (PS) estimation, but their comparative performance in disease risk score (DRS) estimation remains unexplored. Methods: We used real-world data comparing antihypertensive users to non-users with 6...
| Main Authors: | , , , , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
Frontiers Media
2024
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