A review of robust regression in biomedical science research

Sacha Varin, Demosthenes B. Panagiotakos

Research output: Contribution to journalLetterpeer-review

4 Citations (Scopus)


It is a fact that most real-world datasets in biomedical research contain outliers and leverage points. To define what an outlier and a leverage point is, let us assume a Y\X regression model where Y is the outcome variable and X the independent covariate(s). Outliers are Y outcome observations that are distant from the majority of the other observations (in terms of the y-axis). Outliers can sometimes be influential, meaning they can substantially impact the results of a regression analysis, i.e., the estimated b-coefficients and, consequently, the predicted outcome y variable. However, at this point we have to distinguish between (a) “non-influential” outliers i.e., those that have a minimal impact on the estimated regression model but will still lead to an overestimation of the standard error and (b) the “influential” outliers which seriously impact the estimated model because they “pull” the regression line towards themselves [1].
Original languageEnglish
Pages (from-to)1267-1269
Number of pages3
JournalArchives of Medical Science
Issue number5
Publication statusPublished - 6 Aug 2019
Externally publishedYes


Dive into the research topics of 'A review of robust regression in biomedical science research'. Together they form a unique fingerprint.

Cite this