Nonparametric empirical Bayes biomarker imputation and estimation
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Wiley Open Access
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| Format: | Artículo Open Access |
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Wiley
2024
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| _version_ | 1867002829307117568 |
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| author | Alton Barbehenn Sihai Dave Zhao |
| author_facet | Alton Barbehenn Sihai Dave Zhao Alton Barbehenn Sihai Dave Zhao |
| collection | Wiley Open Access |
| contents | Nonparametric empirical Bayes biomarker imputation and estimation Alton Barbehenn Sihai Dave Zhao Statistics in Medicine Biomarkers are often measured in bulk to diagnose patients, monitor patient conditions, and research novel drug pathways. The measurement of these biomarkers often suffers from detection limits that result in missing and untrustworthy measurements. Frequently, missing biomarkers are imputed so that down‐stream analysis can be conducted with modern statistical methods that cannot normally handle data subject to informative censoring. This work develops an empirical Bayes ‐modeling method for imputing and denoising biomarker measurements. We establish superior estimation properties compared to popular methods in simulations and with real data, providing the useful biomarker measurement estimations for down‐stream analysis. 10.1002/sim.10150 http://creativecommons.org/licenses/by-nc-nd/4.0/ |
| doi_str_mv | 10.1002/sim.10150 |
| format | Artículo Open Access |
| id | wiley_oa_10_1002_sim_10150 |
| institution | Wiley Open Access |
| license_str_mv | http://creativecommons.org/licenses/by-nc-nd/4.0/ |
| publishDate | 2024 |
| publisher | Wiley |
| record_format | wiley_oa |
| spellingShingle | Nonparametric empirical Bayes biomarker imputation and estimation Alton Barbehenn Sihai Dave Zhao Statistics in Medicine Nonparametric empirical Bayes biomarker imputation and estimation Alton Barbehenn Sihai Dave Zhao Statistics in Medicine Biomarkers are often measured in bulk to diagnose patients, monitor patient conditions, and research novel drug pathways. The measurement of these biomarkers often suffers from detection limits that result in missing and untrustworthy measurements. Frequently, missing biomarkers are imputed so that down‐stream analysis can be conducted with modern statistical methods that cannot normally handle data subject to informative censoring. This work develops an empirical Bayes ‐modeling method for imputing and denoising biomarker measurements. We establish superior estimation properties compared to popular methods in simulations and with real data, providing the useful biomarker measurement estimations for down‐stream analysis. 10.1002/sim.10150 http://creativecommons.org/licenses/by-nc-nd/4.0/ |
| title | Nonparametric empirical Bayes biomarker imputation and estimation |
| topic | Statistics in Medicine |
| url | https://onlinelibrary.wiley.com/doi/10.1002/sim.10150 |