Premium
Summary ROC curve based on a weighted Youden index for selecting an optimal cutpoint in meta‐analysis of diagnostic accuracy
Author(s) -
Rücker Gerta,
Schumacher Martin
Publication year - 2010
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.3937
Subject(s) - youden's j statistic , statistics , receiver operating characteristic , logit , mathematics , bivariate analysis , sensitivity (control systems) , correlation , correlation coefficient , meta analysis , index (typography) , econometrics , computer science , medicine , geometry , electronic engineering , engineering , world wide web
Established approaches for analyzing meta‐analyses of diagnostic accuracy model the bivariate distribution of the observed pairs of specificity Sp and sensitivity Se , thus accounting for across‐study correlation. However, it is still a matter of debate how to define a summary ROC (SROC) curve. It was recently pointed out that the SROC curve is in principle unidentifiable if only one ( Sp , Se ) pair per study is known. We evaluate an alternative approach, modeling the study‐specific ROC curves based on the assumption of linearity in logit space. A setting is considered in which the pair ( Sp , Se ) that is selected for publication in a particular study maximizes a weighted Youden index λ Se +(1−λ) Sp with a given weight λ.This leads to a fixed slope (1−λ)/λ of the ROC curve in (1− Sp , Se ), equivalent to a slope of (1−λ) Sp (1− Sp )/(λ Se (1− Se )) for the corresponding straight line in logit space. While the slope depends on the variance ratio of the underlying distributions, the intercept is a function of the mean difference. Our approach leads in a natural way to a new, model‐based proposal for a summary ROC curve. It is illustrated using an example from the literature. Copyright © 2010 John Wiley & Sons, Ltd.