Non-Gaussian Berkson Errors in Bioassay

Alaa Althubaiti, Alexander Donev

    Research output: Contribution to journalArticlepeer-review


    The experimental design plays an important role in every experimental study. However, if errors in the settings of the studied factors cannot be avoided, i.e. Berkson errors occur, the estimates of the model parameters may be biased and the variability in the study increased. Correction methods for the effect of Berkson errors are compared. The emphasis is on the study of correlated Berkson errors which follow non-Gaussian distribution as this appears to have been a neglected, yet important, area. It is shown that the regression calibration approach bias correction methods are useful when the Berkson errors are independent. However, when these errors are dependent, the newly proposed method B-SIMEX clearly outperforms the other methods.
    Original languageEnglish
    Pages (from-to)430-445
    Number of pages15
    JournalStatistical Methods in Medical Research
    Issue number1
    Publication statusPublished - 1 Feb 2016


    • Errors-in-variables, regression calibration, serial dilution designs, dilution errors, SIMEX, B-SIMEX


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