A better alternative to non-parametric approaches for adjusting for covariate measurement errors in logistic regression.
Article
Article Title | A better alternative to non-parametric approaches for adjusting for covariate measurement errors in logistic regression. |
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ERA Journal ID | 790 |
Article Category | Article |
Authors | Hossain, Shahadut, Hoque, Zahirul and Hasan, A. H. M. Saidul |
Journal Title | Communications in Statistics: Simulation and Computation |
Journal Citation | 45 (8), pp. 2659-2677 |
Number of Pages | 19 |
Year | 2016 |
Publisher | Taylor & Francis |
Place of Publication | United States |
ISSN | 0361-0918 |
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. |
Keywords | Model misspecification; Measurement error; Exposure mo |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 490508. Statistical data science |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | United Arab Emirates University, United Arab Emirates |
Dhaka Commerce College, Bangladesh |
https://research.usq.edu.au/item/z3qz7/a-better-alternative-to-non-parametric-approaches-for-adjusting-for-covariate-measurement-errors-in-logistic-regression
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