Generalized linear mixed models for multi-reader multi-case studies of diagnostic tests

Diagnostic tests are often compared in multi-reader multi-case (MRMC) studies in which a number of cases (subjects with or without the disease in question) are examined by several readers using all tests to be compared. One of the commonly used methods for analyzing MRMC data is the Obuchowski-Rocke...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Statistical methods in medical research Ročník 26; číslo 3; s. 1373 - 1388
Hlavní autori: Liu, Wei, Pantoja-Galicia, Norberto, Zhang, Bo, Kotz, Richard M, Pennello, Gene, Zhang, Hui, Jacob, Jessie, Zhang, Zhiwei
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: England 01.06.2017
Predmet:
Algorithms
Area Under Curve
Breast Neoplasms - diagnosis
Diagnostic Tests, Routine - methods
Female
Humans
Likelihood Functions
Linear Models
Monte Carlo Method
ROC Curve
random effect
diagnostic medicine
ROC curve
EM algorithm
pseudo-likelihood
AUC
ISSN:1477-0334
On-line prístup:Zistit podrobnosti o prístupe
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:Diagnostic tests are often compared in multi-reader multi-case (MRMC) studies in which a number of cases (subjects with or without the disease in question) are examined by several readers using all tests to be compared. One of the commonly used methods for analyzing MRMC data is the Obuchowski-Rockette (OR) method, which assumes that the true area under the receiver operating characteristic curve (AUC) for each combination of reader and test follows a linear mixed model with fixed effects for test and random effects for reader and the reader-test interaction. This article proposes generalized linear mixed models which generalize the OR model by incorporating a range-appropriate link function that constrains the true AUCs to the unit interval. The proposed models can be estimated by maximizing a pseudo-likelihood based on the approximate normality of AUC estimates. A Monte Carlo expectation-maximization algorithm can be used to maximize the pseudo-likelihood, and a non-parametric bootstrap procedure can be used for inference. The proposed method is evaluated in a simulation study and applied to an MRMC study of breast cancer detection.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1477-0334
DOI:10.1177/0962280215579476