A trivariate meta-analysis of diagnostic studies accounting for prevalence and non-evaluable subjects: Re-evaluation of the meta-analysis of coronary CT angiography studies

Xiaoye Ma, Muhammad Fareed Suri, Haitao Chu

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5 Scopus citations

Abstract

Background: A recent paper proposed an intent-to-diagnose approach to handle non-evaluable index test results and discussed several alternative approaches, with an application to the meta-analysis of coronary CT angiography diagnostic accuracy studies. However, no simulation studies have been conducted to test the performance of the methods. Methods: We propose an extended trivariate generalized linear mixed model (TGLMM) to handle non-evaluable index test results. The performance of the intent-to-diagnose approach, the alternative approaches and the extended TGLMM approach is examined by extensive simulation studies. The meta-analysis of coronary CT angiography diagnostic accuracy studies is re-evaluated by the extended TGLMM. Results: Simulation studies showed that the intent-to-diagnose approach under-estimate sensitivity and specificity. Under the missing at random (MAR) assumption, the TGLMM gives nearly unbiased estimates of test accuracy indices and disease prevalence. After applying the TGLMM approach to re-evaluate the coronary CT angiography meta-analysis, overall median sensitivity is 0.98 (0.967, 0.993), specificity is 0.875 (0.827, 0.923) and disease prevalence is 0.478 (0.379, 0.577). Conclusions: Under MAR assumption, the intent-to-diagnose approach under-estimate both sensitivity and specificity, while the extended TGLMM gives nearly unbiased estimates of sensitivity, specificity and prevalence. We recommend the extended TGLMM to handle non-evaluable index test subjects.

Original languageEnglish (US)
Article number128
JournalBMC Medical Research Methodology
Volume14
Issue number1
DOIs
StatePublished - 2014

Bibliographical note

Funding Information:
XM and HC were supported in part by the US NIAID AI103012, NCI P01CA142538, NCI P30CA077598, and U54-MD008620. XM, FS and HC were supported by NHLBI 1R01HL105626. The opinions,results and conclusions reported in this paper are those of the authors and are independent from the funding sources 1Division of Biostatistics, School of Public Health, University of Minnesota, A460 Mayo Building, MMC 303, 420 Delaware St. SE, 55455 Minneapolis, MN, USA.2Department of Neurology, University of Minnesota, MMC 295, 420 Delaware St. SE, 55455 Minneapolis, MN, USA.

Publisher Copyright:
© 2014 Ma et al.; licensee BioMed Central Ltd.

Keywords

  • Diagnostic test
  • Meta-analysis
  • Non-evaluable subjects

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