A better alternative to non-parametric approaches for adjusting for covariate measurement errors in logistic regression.

Article


Hossain, Shahadut, Hoque, Zahirul and Hasan, A. H. M. Saidul. 2016. "A better alternative to non-parametric approaches for adjusting for covariate measurement errors in logistic regression." Communications in Statistics: Simulation and Computation. 45 (8), pp. 2659-2677. https://doi.org/10.1080/03610918.2014.917675
Article Title

A better alternative to non-parametric approaches for adjusting for covariate measurement errors in logistic regression.

ERA Journal ID790
Article CategoryArticle
AuthorsHossain, Shahadut, Hoque, Zahirul and Hasan, A. H. M. Saidul
Journal TitleCommunications in Statistics: Simulation and Computation
Journal Citation45 (8), pp. 2659-2677
Number of Pages19
Year2016
PublisherTaylor & Francis
Place of PublicationUnited States
ISSN0361-0918
1532-4141
Digital Object Identifier (DOI)https://doi.org/10.1080/03610918.2014.917675
Web Address (URL)https://www.tandfonline.com/doi/abs/10.1080/03610918.2014.917675
Abstract

In this article, we propose a flexible parametric (FP) approach for adjusting for covariate measurement errors in regression that can accommodate replicated measurements on the surrogate (mismeasured) version of the unobserved true covariate on all the study subjects or on a sub-sample of the study subjects as error assessment data. We utilize the general framework of the FP approach proposed by Hossain and Gustafson in 2009 for adjusting for covariate measurement errors in regression. The FP approach is then compared with the existing non-parametric approaches when error assessment data are available on the entire sample of the study subjects (complete error assessment data) considering covariate measurement error in a multiple logistic regression model. We also developed the FP approach when error assessment data are available on a sub-sample of the study subjects (partial error assessment data) and investigated its performance using both simulated and real life data. Simulation results reveal that, in comparable situations, the FP approach performs as good as or better than the competing non-parametric approaches in eliminating the bias that arises in the estimated regression parameters due to covariate measurement errors. Also, it results in better efficiency of the estimated parameters. Finally, the FP approach is found to perform adequately well in terms of bias correction, confidence coverage, and in achieving appropriate statistical power under partial error assessment data.

KeywordsModel misspecification; Measurement error; Exposure mo
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
Dhaka Commerce College, Bangladesh
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