Medication Prescription Policy for US Veterans With Metastatic Castration-Resistant Prostate Cancer: Causal Machine Learning Approach

This study of 2886 veterans showed evidence of heterogeneity and that survival days may be improved for certain patients with mCRPC based on the medication prescribed. Findings suggest that prescription rules based on the patient characteristics, laboratory test results, and comorbidities available to the physician at the time of prescription could improve survival by providing personalized treatment decisions.

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Publication Details

Journal
JMIR Medical Informatics
Published
2024-11-19
DOI
https://doi.org/10.2196/59480
Primary Topic
Prostate Cancer Treatment and Research
Type
article
Field-Weighted Citation Impact
0.00
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article

Medication Prescription Policy for US Veterans With Metastatic Castration-Resistant Prostate Cancer: Causal Machine Learning Approach

Martin William Schoen, Nirup M. Menon, Deepika Gopukumar
JMIR Medical Informatics
Prostate Cancer Treatment and Research
article

Medication Prescription Policy for US Veterans With Metastatic Castration-Resistant Prostate Cancer: Causal Machine Learning Approach

Martin William Schoen, Nirup M. Menon, Deepika Gopukumar
article en

Abstract

This study of 2886 veterans showed evidence of heterogeneity and that survival days may be improved for certain patients with mCRPC based on the medication prescribed. Findings suggest that prescription rules based on the patient characteristics, laboratory test results, and comorbidities available to the physician at the time of prescription could improve survival by providing personalized treatment decisions.

JMIR Medical InformaticsVol. 12
George Mason University (US), St. Louis VA Medical Center (US), Saint Louis University (US)
Good health and well-being
Openalex Percentile: Top 61%
Prostate Cancer Treatment and Research
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