An overview of robust methods in medical research.
Robust statistics is an extension of classical parametric statistics that specifically takes into account the fact that the assumed parametric models used by the researchers are only approximate. In this article, we review and outline how robust inferential procedures may routinely be applied in practice in the biomedical research. Numerical illustrations are given for the t-test, regression models, logistic regression, survival analysis and ROC curves, showing that robust methods are often more appropriate than standard procedures.
Department of Public Health and Infectious Diseases, Sapienza University of Rome, Rome, Italy. alessio.farcomeni@uniroma1.it
Alessio
A
Farcomeni
Laura
L
Ventura
D035843:Biomedical Research / Q000706:statistics & numerical data*; D056808:Biostatistics / Q000379:methods*; D003627:Data Interpretation, Statistical; D005523:Food Supply; D005919:Glomerular Filtration Rate; D006801:Humans; D016013:Likelihood Functions; D016015:Logistic Models; D017728:Lymphoma, Large-Cell, Anaplastic / Q000175:diagnosis; D008297:Male; D015233:Models, Statistical; D011471:Prostatic Neoplasms / Q000188:drug therapy; D012372:ROC Curve; D020521:Stroke / Q000503:physiopathology; D000071939:Stroke Rehabilitation; D016019:Survival Analysis