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The Optimal Machine Learning-Based Missing Data Imputation for the Cox Proportional Hazard Model
An adequate imputation of missing data would significantly preserve the statistical power and avoid erroneous conclusions. In the era of big data, machine learning is a great tool to infer the missing values. The root means square error (RMSE) and the proportion of falsely classified entries (PFC) a...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8289437/ https://www.ncbi.nlm.nih.gov/pubmed/34291028 http://dx.doi.org/10.3389/fpubh.2021.680054 |
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author | Guo, Chao-Yu Yang, Ying-Chen Chen, Yi-Hau |
author_facet | Guo, Chao-Yu Yang, Ying-Chen Chen, Yi-Hau |
author_sort | Guo, Chao-Yu |
collection | PubMed |
description | An adequate imputation of missing data would significantly preserve the statistical power and avoid erroneous conclusions. In the era of big data, machine learning is a great tool to infer the missing values. The root means square error (RMSE) and the proportion of falsely classified entries (PFC) are two standard statistics to evaluate imputation accuracy. However, the Cox proportional hazards model using various types requires deliberate study, and the validity under different missing mechanisms is unknown. In this research, we propose supervised and unsupervised imputations and examine four machine learning-based imputation strategies. We conducted a simulation study under various scenarios with several parameters, such as sample size, missing rate, and different missing mechanisms. The results revealed the type-I errors according to different imputation techniques in the survival data. The simulation results show that the non-parametric “missForest” based on the unsupervised imputation is the only robust method without inflated type-I errors under all missing mechanisms. In contrast, other methods are not valid to test when the missing pattern is informative. Statistical analysis, which is improperly conducted, with missing data may lead to erroneous conclusions. This research provides a clear guideline for a valid survival analysis using the Cox proportional hazard model with machine learning-based imputations. |
format | Online Article Text |
id | pubmed-8289437 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-82894372021-07-20 The Optimal Machine Learning-Based Missing Data Imputation for the Cox Proportional Hazard Model Guo, Chao-Yu Yang, Ying-Chen Chen, Yi-Hau Front Public Health Public Health An adequate imputation of missing data would significantly preserve the statistical power and avoid erroneous conclusions. In the era of big data, machine learning is a great tool to infer the missing values. The root means square error (RMSE) and the proportion of falsely classified entries (PFC) are two standard statistics to evaluate imputation accuracy. However, the Cox proportional hazards model using various types requires deliberate study, and the validity under different missing mechanisms is unknown. In this research, we propose supervised and unsupervised imputations and examine four machine learning-based imputation strategies. We conducted a simulation study under various scenarios with several parameters, such as sample size, missing rate, and different missing mechanisms. The results revealed the type-I errors according to different imputation techniques in the survival data. The simulation results show that the non-parametric “missForest” based on the unsupervised imputation is the only robust method without inflated type-I errors under all missing mechanisms. In contrast, other methods are not valid to test when the missing pattern is informative. Statistical analysis, which is improperly conducted, with missing data may lead to erroneous conclusions. This research provides a clear guideline for a valid survival analysis using the Cox proportional hazard model with machine learning-based imputations. Frontiers Media S.A. 2021-07-05 /pmc/articles/PMC8289437/ /pubmed/34291028 http://dx.doi.org/10.3389/fpubh.2021.680054 Text en Copyright © 2021 Guo, Yang and Chen. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Public Health Guo, Chao-Yu Yang, Ying-Chen Chen, Yi-Hau The Optimal Machine Learning-Based Missing Data Imputation for the Cox Proportional Hazard Model |
title | The Optimal Machine Learning-Based Missing Data Imputation for the Cox Proportional Hazard Model |
title_full | The Optimal Machine Learning-Based Missing Data Imputation for the Cox Proportional Hazard Model |
title_fullStr | The Optimal Machine Learning-Based Missing Data Imputation for the Cox Proportional Hazard Model |
title_full_unstemmed | The Optimal Machine Learning-Based Missing Data Imputation for the Cox Proportional Hazard Model |
title_short | The Optimal Machine Learning-Based Missing Data Imputation for the Cox Proportional Hazard Model |
title_sort | optimal machine learning-based missing data imputation for the cox proportional hazard model |
topic | Public Health |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8289437/ https://www.ncbi.nlm.nih.gov/pubmed/34291028 http://dx.doi.org/10.3389/fpubh.2021.680054 |
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