A Better Alternative to Non-parametric Approaches for Adjusting for Covariate Measurement Errors in Logistic Regression

Shahadut Hossain, Zahirul Hoque, A. H.M.Saidul Hasan

    Research output: Contribution to journalArticlepeer-review

    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.

    Original languageEnglish
    Pages (from-to)2659-2677
    Number of pages19
    JournalCommunications in Statistics: Simulation and Computation
    Volume45
    Issue number8
    DOIs
    Publication statusPublished - Sep 13 2016

    Keywords

    • Exposure model
    • Measurement error
    • Model misspecification

    ASJC Scopus subject areas

    • Statistics and Probability
    • Modelling and Simulation

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