Bivariate random effects meta-analysis of diagnostic studies using generalized linear mixed models.

TitleBivariate random effects meta-analysis of diagnostic studies using generalized linear mixed models.
Publication TypeJournal Article
Year of Publication2010
AuthorsChu, Haitao, Hongfei Guo, and Yijie Zhou
JournalMed Decis Making
Volume30
Issue4
Pagination499-508
Date Published2010 Jul-Aug
ISSN1552-681X
KeywordsLinear Models, ROC Curve
Abstract

Bivariate random effect models are currently one of the main methods recommended to synthesize diagnostic test accuracy studies. However, only the logit transformation on sensitivity and specificity has been previously considered in the literature. In this article, the authors consider a bivariate generalized linear mixed model to jointly model the sensitivities and specificities, and they discuss the estimation of the summary receiver operating characteristic curve (ROC) and the area under the ROC curve (AUC). As the special cases of this model, the authors discuss the commonly used logit, probit, and complementary log-log transformations. To evaluate the impact of misspecification of the link functions on the estimation, they present 2 case studies and a set of simulation studies. Their study suggests that point estimation of the median sensitivity and specificity and AUC is relatively robust to the misspecification of the link functions. However, the misspecification of link functions has a noticeable impact on the standard error estimation and the 95% confidence interval coverage, which emphasizes the importance of choosing an appropriate link function to make statistical inference.

DOI10.1177/0272989X09353452
Alternate JournalMed Decis Making
Original PublicationBivariate random effects meta-analysis of diagnostic studies using generalized linear mixed models.
PubMed ID19959794
PubMed Central IDPMC3035476
Grant ListP01 CA142538-01 / CA / NCI NIH HHS / United States
P01 CA142538-02 / CA / NCI NIH HHS / United States
P30 CA016086 / CA / NCI NIH HHS / United States
P01 CA142538 / CA / NCI NIH HHS / United States
CA16086 / CA / NCI NIH HHS / United States
Project: