Response-based multiple imputation method for minimizing the impact of covariate detection limit in logistic regression

Article


Hossain, Shahadut, Hoque, Zahirul and Wesolowski, Jacek. 2021. "Response-based multiple imputation method for minimizing the impact of covariate detection limit in logistic regression." Communications in Statistics: Theory and Methods. 50 (2), pp. 371-386. https://doi.org/10.1080/03610926.2019.1635699
Article Title

Response-based multiple imputation method for minimizing the impact of covariate detection limit in logistic regression

ERA Journal ID791
Article CategoryArticle
AuthorsHossain, Shahadut, Hoque, Zahirul and Wesolowski, Jacek
Journal TitleCommunications in Statistics: Theory and Methods
Journal Citation50 (2), pp. 371-386
Number of Pages16
Year2021
PublisherTaylor & Francis
Place of PublicationUnited States
ISSN0361-0926
1532-415X
Digital Object Identifier (DOI)https://doi.org/10.1080/03610926.2019.1635699
Web Address (URL)https://www.tandfonline.com/doi/abs/10.1080/03610926.2019.1635699
Abstract

Presence of detection limit (DL) in covariates causes inflated bias and inaccurate mean squared error to the estimators of the regression parameters. This paper suggests a response-driven multiple imputation method to correct the deleterious impact introduced by the covariate DL in the estimators of the parameters of simple logistic regression model. The performance of the method has been thoroughly investigated, and found to outperform the existing competing methods. The proposed method is computationally simple and easily implementable by using three existing R libraries. The method is robust to the violation of distributional assumption for the covariate of interest.

KeywordsDetection limit; left-truncation; logistic regression; multiple lmputation
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 2020490508. Statistical data science
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Byline AffiliationsUnited Arab Emirates University, United Arab Emirates
Warsaw University of Technology, Poland
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