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Bivariate Random Effects Meta-Analysis of Diagnostic Studies Using Generalized Linear Mixed Models

Author

Listed:
  • Haitao Chu

    (Department of Biostatistics and Lineberger Comprehensive Cancer Center, Univerity of North Carolina at Chapel Hill, hchu@bios.unc.edu)

  • Hongfei Guo

    (Division of Biostatistics and Clinical and Translational Science Institute, University of Minnesota, Minneapolis)

  • Yijie Zhou

    (Merck Research Laboratories, Merck & Co., Inc., Rahway, New Jersey)

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.

Suggested Citation

  • Haitao Chu & Hongfei Guo & Yijie Zhou, 2010. "Bivariate Random Effects Meta-Analysis of Diagnostic Studies Using Generalized Linear Mixed Models," Medical Decision Making, , vol. 30(4), pages 499-508, July.
    • Handle: RePEc:sae:medema:v:30:y:2010:i:4:p:499-508
    DOI: 10.1177/0272989X09353452
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    Citations

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    Cited by:

    1. Sandra Alvear-Vega & Héctor Vargas-Garrido, 2022. "Social Determinants of the Non-Utilization of the Supplementary Feeding Program (PACAM) Aimed at Older Adults’ Nutritional Support," IJERPH, MDPI, vol. 19(21), pages 1-10, November.
    2. Aristidis K. Nikoloulopoulos, 2022. "An one‐factor copula mixed model for joint meta‐analysis of multiple diagnostic tests," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(3), pages 1398-1423, July.
    3. Diaz, Mireya, 2015. "Performance measures of the bivariate random effects model for meta-analyses of diagnostic accuracy," Computational Statistics & Data Analysis, Elsevier, vol. 83(C), pages 82-90.

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