On the global identifiability of logistic regression models with misclassified outcomes Article Swipe
Rui Duan
,
Yang Ning
,
Jiasheng Shi
,
Raymond J. Carroll
,
Tianxi Cai
,
Yong Chen
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2103.12846
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2103.12846
In the last decade, the secondary use of large data from health systems, such as electronic health records, has demonstrated great promise in advancing biomedical discoveries and improving clinical decision making. However, there is an increasing concern about biases in association studies caused by misclassification in the binary outcomes derived from electronic health records. We revisit the classical logistic regression model with misclassified outcomes. Despite that local identification conditions in some related settings have been previously established, the global identification of such models remains largely unknown and is an important question yet to be answered. We derive necessary and sufficient conditions for global identifiability of logistic regression models with misclassified outcomes, using a novel approach termed as the submodel analysis, and a technique adapted from the Picard-Lindelöf existence theorem in ordinary differential equations. In particular, our results are applicable to logistic models with discrete covariates, which is a common situation in biomedical studies, The conditions are easy to verify in practice. In addition to model identifiability, we propose a hypothesis testing procedure for regression coefficients in the misclassified logistic regression model when the model is not identifiable under the null.
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2103.12846
- https://arxiv.org/pdf/2103.12846
- OA Status
- green
- Cited By
- 1
- References
- 20
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3137863733
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https://openalex.org/W3137863733Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2103.12846Digital Object Identifier
- Title
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On the global identifiability of logistic regression models with misclassified outcomesWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
- Publication date
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2021-03-23Full publication date if available
- Authors
-
Rui Duan, Yang Ning, Jiasheng Shi, Raymond J. Carroll, Tianxi Cai, Yong ChenList of authors in order
- Landing page
-
https://arxiv.org/abs/2103.12846Publisher landing page
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https://arxiv.org/pdf/2103.12846Direct link to full text PDF
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2103.12846Direct OA link when available
- Concepts
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Identifiability, Logistic regression, Covariate, Identification (biology), Econometrics, Null hypothesis, Statistics, Computer science, Mathematics, Biology, BotanyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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20Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.association | 40 |
| abstract_inverted_index.covariates, | 144 |
| abstract_inverted_index.discoveries | 25 |
| abstract_inverted_index.particular, | 134 |
| abstract_inverted_index.coefficients | 174 |
| abstract_inverted_index.demonstrated | 19 |
| abstract_inverted_index.differential | 131 |
| abstract_inverted_index.established, | 76 |
| abstract_inverted_index.identifiable | 186 |
| abstract_inverted_index.misclassified | 62, 109, 177 |
| abstract_inverted_index.identification | 67, 79 |
| abstract_inverted_index.identifiability | 103 |
| abstract_inverted_index.Picard-Lindelöf | 126 |
| abstract_inverted_index.identifiability, | 165 |
| abstract_inverted_index.misclassification | 44 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/16 |
| sustainable_development_goals[0].score | 0.8299999833106995 |
| sustainable_development_goals[0].display_name | Peace, Justice and strong institutions |
| citation_normalized_percentile |