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

BMC Medical Research Methodology

Haitao Chu²  Muhammad Fareed K Suri¹  Xiaoye Ma² 

[1] Department of Neurology, University of Minnesota, MMC 295, 420 Delaware St. SE, 55455 Minneapolis, MN, USA;Division of Biostatistics, School of Public Health, University of Minnesota, A460 Mayo Building, MMC 303, 420 Delaware St. SE, 55455 Minneapolis, MN, USA

关键词: Non-evaluable subjects;    Diagnostic test;    Meta-analysis;   

Others  :  1090384
DOI  :  10.1186/1471-2288-14-128

 received in 2014-09-29, accepted in 2014-11-18,  发布年份 2014

【 摘 要 】

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.

【 授权许可】

   
2014 Ma et al.; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

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