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

Shahadut Hossain, Zahirul Hoque, Jacek Wesolowski

    Research output: Contribution to journalArticlepeer-review

    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.

    Original languageEnglish
    Pages (from-to)371-386
    Number of pages16
    JournalCommunications in Statistics - Theory and Methods
    Volume50
    Issue number2
    DOIs
    Publication statusPublished - 2021

    Keywords

    • Detection limit
    • left-truncation
    • logistic regression
    • multiple lmputation

    ASJC Scopus subject areas

    • Statistics and Probability

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