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Impact of Integrating Machine Learning in Comparative Effectiveness Research of Oral Anticoagulants in Patients with Atrial Fibrillation
We aimed to compare the ability to balance baseline covariates and explore the impact of residual confounding between conventional and machine learning approaches to derive propensity scores (PS). The Health Insurance Review and Assessment Service database (January 2012–September 2019) was used. Pat...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9566283/ https://www.ncbi.nlm.nih.gov/pubmed/36232216 http://dx.doi.org/10.3390/ijerph191912916 |
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author | Han, Sola Suh, Hae Sun |
author_facet | Han, Sola Suh, Hae Sun |
author_sort | Han, Sola |
collection | PubMed |
description | We aimed to compare the ability to balance baseline covariates and explore the impact of residual confounding between conventional and machine learning approaches to derive propensity scores (PS). The Health Insurance Review and Assessment Service database (January 2012–September 2019) was used. Patients with atrial fibrillation (AF) who initiated oral anticoagulants during July 2015–September 2018 were included. The outcome of interest was stroke/systemic embolism. To estimate PS, we used a logistic regression model (i.e., a conventional approach) and a generalized boosted model (GBM) which is a machine learning approach. Both PS matching and inverse probability of treatment weighting were performed. To evaluate balance achievement, standardized differences, p-values, and boxplots were used. To explore residual confounding, E-values and negative control outcomes were used. In total, 129,434 patients were identified. Although all baseline covariates were well balanced, the distribution of continuous variables seemed more similar when GBM was applied. E-values ranged between 1.75 and 2.70 and were generally higher in GBM. In the negative control outcome analysis, slightly more nonsignificant hazard ratios were observed in GBM. We showed GBM provided a better ability to balance covariates and had a lower impact of residual confounding, compared with the conventional approach in the empirical example of comparative effectiveness analysis. |
format | Online Article Text |
id | pubmed-9566283 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-95662832022-10-15 Impact of Integrating Machine Learning in Comparative Effectiveness Research of Oral Anticoagulants in Patients with Atrial Fibrillation Han, Sola Suh, Hae Sun Int J Environ Res Public Health Article We aimed to compare the ability to balance baseline covariates and explore the impact of residual confounding between conventional and machine learning approaches to derive propensity scores (PS). The Health Insurance Review and Assessment Service database (January 2012–September 2019) was used. Patients with atrial fibrillation (AF) who initiated oral anticoagulants during July 2015–September 2018 were included. The outcome of interest was stroke/systemic embolism. To estimate PS, we used a logistic regression model (i.e., a conventional approach) and a generalized boosted model (GBM) which is a machine learning approach. Both PS matching and inverse probability of treatment weighting were performed. To evaluate balance achievement, standardized differences, p-values, and boxplots were used. To explore residual confounding, E-values and negative control outcomes were used. In total, 129,434 patients were identified. Although all baseline covariates were well balanced, the distribution of continuous variables seemed more similar when GBM was applied. E-values ranged between 1.75 and 2.70 and were generally higher in GBM. In the negative control outcome analysis, slightly more nonsignificant hazard ratios were observed in GBM. We showed GBM provided a better ability to balance covariates and had a lower impact of residual confounding, compared with the conventional approach in the empirical example of comparative effectiveness analysis. MDPI 2022-10-09 /pmc/articles/PMC9566283/ /pubmed/36232216 http://dx.doi.org/10.3390/ijerph191912916 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Han, Sola Suh, Hae Sun Impact of Integrating Machine Learning in Comparative Effectiveness Research of Oral Anticoagulants in Patients with Atrial Fibrillation |
title | Impact of Integrating Machine Learning in Comparative Effectiveness Research of Oral Anticoagulants in Patients with Atrial Fibrillation |
title_full | Impact of Integrating Machine Learning in Comparative Effectiveness Research of Oral Anticoagulants in Patients with Atrial Fibrillation |
title_fullStr | Impact of Integrating Machine Learning in Comparative Effectiveness Research of Oral Anticoagulants in Patients with Atrial Fibrillation |
title_full_unstemmed | Impact of Integrating Machine Learning in Comparative Effectiveness Research of Oral Anticoagulants in Patients with Atrial Fibrillation |
title_short | Impact of Integrating Machine Learning in Comparative Effectiveness Research of Oral Anticoagulants in Patients with Atrial Fibrillation |
title_sort | impact of integrating machine learning in comparative effectiveness research of oral anticoagulants in patients with atrial fibrillation |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9566283/ https://www.ncbi.nlm.nih.gov/pubmed/36232216 http://dx.doi.org/10.3390/ijerph191912916 |
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