Vertaile menetelmiä

	Vankka ROC-analyysi	Robust Effect Size Analysis
Tieteenala	Tilastotiede	Tilastotiede
Menetelmäperhe	Hypothesis test	Hypothesis test
Syntyvuosi	1990s–2000s	2005 (formalized)
Kehittäjä	Multiple contributors (Pepe, Qin, Zhou, and others)	Algina, Keselman & Penfield; Wilcox
Tyyppi	Robust diagnostic accuracy evaluation	Robust effect size estimation
Alkuperäislähde	Pepe, M. S. (2000). An interpretation for the ROC curve and inference using GLM procedures. Biometrics, 56(2), 352–359.	Algina, J., Keselman, H. J., & Penfield, R. D. (2005). An alternative to Cohen's standardized mean difference effect size: A robust parameter and confidence interval in the two independent groups case. Psychological Methods, 10(3), 317–328.
Rinnakkaisnimet	robust AUC analysis, outlier-resistant ROC, robust diagnostic accuracy analysis, robust sensitivity-specificity analysis	robust Cohen's d, trimmed-mean effect size, outlier-resistant effect size, robust standardized mean difference
Liittyvät	3	5
Tiivistelmä	Robust ROC analysis evaluates the diagnostic accuracy of a continuous or ordinal biomarker in distinguishing between two groups (e.g., diseased vs. healthy) while protecting against the distorting effects of outliers, non-normality, or distributional violations that can bias standard parametric ROC estimates and AUC confidence intervals.	Robust effect size analysis quantifies the magnitude of a difference or association using estimators that are resistant to outliers and violations of normality. Rather than relying on classical statistics such as Cohen's d based on sample means and standard deviations, robust variants use trimmed means and Winsorized standard deviations to produce effect size estimates that accurately reflect the typical effect rather than being inflated by extreme values.