An overview of robust methods in medical research
- 25 October 2010
- journal article
- review article
- Published by SAGE Publications in Statistical Methods in Medical Research
- Vol. 21 (2), 111-133
- https://doi.org/10.1177/0962280210385865
Abstract
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.Keywords
This publication has 72 references indexed in Scilit:
- Weighted Likelihood Equations with Bootstrap Root SearchJournal of the American Statistical Association, 1998
- Proportional hazards tests and diagnostics based on weighted residualsBiometrika, 1994
- Robust Bounded-Influence Tests in General Parametric ModelsJournal of the American Statistical Association, 1994
- The Use and Interpretation of Residuals Based on Robust EstimationJournal of the American Statistical Association, 1993
- Goodness-of-Fit Analysis for the Cox Regression Model Based on a Class of Parameter EstimatorsJournal of the American Statistical Association, 1991
- Martingale-based residuals for survival modelsBiometrika, 1990
- Robust Bounded-Influence Tests in Linear ModelsJournal of the American Statistical Association, 1990
- Some New Estimation Methods for Weighted Regression When There Are Possible OutliersTechnometrics, 1986
- Influence functions for proportional hazards regressionBiometrika, 1985
- Partial residuals for the proportional hazards regression modelBiometrika, 1982