Non-Gaussian Berkson Errors in Bioassay

Research output: Contribution to journalArticle


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.

Bibliographical metadata

Original languageEnglish
Pages (from-to)430-445
Number of pages15
JournalStatistical Methods in Medical Research
Issue number1
StatePublished - 1 Feb 2016