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A Nuisance-Free Inference Procedure Accounting for the Unknown Missingness with Application to Electronic Health Records
We study how to conduct statistical inference in a regression model where the outcome variable is prone to missing values and the missingness mechanism is unknown. The model we consider might be a traditional setting or a modern high-dimensional setting where the sparsity assumption is usually impos...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7597318/ https://www.ncbi.nlm.nih.gov/pubmed/33286923 http://dx.doi.org/10.3390/e22101154 |
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author | Zhao, Jiwei Chen, Chi |
author_facet | Zhao, Jiwei Chen, Chi |
author_sort | Zhao, Jiwei |
collection | PubMed |
description | We study how to conduct statistical inference in a regression model where the outcome variable is prone to missing values and the missingness mechanism is unknown. The model we consider might be a traditional setting or a modern high-dimensional setting where the sparsity assumption is usually imposed and the regularization technique is popularly used. Motivated by the fact that the missingness mechanism, albeit usually treated as a nuisance, is difficult to specify correctly, we adopt the conditional likelihood approach so that the nuisance can be completely ignored throughout our procedure. We establish the asymptotic theory of the proposed estimator and develop an easy-to-implement algorithm via some data manipulation strategy. In particular, under the high-dimensional setting where regularization is needed, we propose a data perturbation method for the post-selection inference. The proposed methodology is especially appealing when the true missingness mechanism tends to be missing not at random, e.g., patient reported outcomes or real world data such as electronic health records. The performance of the proposed method is evaluated by comprehensive simulation experiments as well as a study of the albumin level in the MIMIC-III database. |
format | Online Article Text |
id | pubmed-7597318 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-75973182020-11-09 A Nuisance-Free Inference Procedure Accounting for the Unknown Missingness with Application to Electronic Health Records Zhao, Jiwei Chen, Chi Entropy (Basel) Article We study how to conduct statistical inference in a regression model where the outcome variable is prone to missing values and the missingness mechanism is unknown. The model we consider might be a traditional setting or a modern high-dimensional setting where the sparsity assumption is usually imposed and the regularization technique is popularly used. Motivated by the fact that the missingness mechanism, albeit usually treated as a nuisance, is difficult to specify correctly, we adopt the conditional likelihood approach so that the nuisance can be completely ignored throughout our procedure. We establish the asymptotic theory of the proposed estimator and develop an easy-to-implement algorithm via some data manipulation strategy. In particular, under the high-dimensional setting where regularization is needed, we propose a data perturbation method for the post-selection inference. The proposed methodology is especially appealing when the true missingness mechanism tends to be missing not at random, e.g., patient reported outcomes or real world data such as electronic health records. The performance of the proposed method is evaluated by comprehensive simulation experiments as well as a study of the albumin level in the MIMIC-III database. MDPI 2020-10-14 /pmc/articles/PMC7597318/ /pubmed/33286923 http://dx.doi.org/10.3390/e22101154 Text en © 2020 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Zhao, Jiwei Chen, Chi A Nuisance-Free Inference Procedure Accounting for the Unknown Missingness with Application to Electronic Health Records |
title | A Nuisance-Free Inference Procedure Accounting for the Unknown Missingness with Application to Electronic Health Records |
title_full | A Nuisance-Free Inference Procedure Accounting for the Unknown Missingness with Application to Electronic Health Records |
title_fullStr | A Nuisance-Free Inference Procedure Accounting for the Unknown Missingness with Application to Electronic Health Records |
title_full_unstemmed | A Nuisance-Free Inference Procedure Accounting for the Unknown Missingness with Application to Electronic Health Records |
title_short | A Nuisance-Free Inference Procedure Accounting for the Unknown Missingness with Application to Electronic Health Records |
title_sort | nuisance-free inference procedure accounting for the unknown missingness with application to electronic health records |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7597318/ https://www.ncbi.nlm.nih.gov/pubmed/33286923 http://dx.doi.org/10.3390/e22101154 |
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