Comparation of accuracy of different diagnostic tests: an introduction of network meta-analysis methods_Chinese Journal of Evidence-Based Medicine

Authors：

WU Jingling ¹ , GE Long ² , ZHANG Junhua ³ , ZHOU Jianguo ⁴ , GAO Ya ⁵ , MA Xueni ⁵ , LI Jipin ⁵ , LIN Xiaoyan ⁵ ,  TIAN Jinhui ^6,7

1. Department of Ultrasound, the First Hospital of PLA, Lanzhou, 730000, P.R.China;
2. The First Clinical Medicine College of Lanzhou University, Lanzhou, 730000, P.R.China;
3. Evidence-based Medicine Center of Tianjin University of Traditional Chinese Medicine, Tianjin, 300193, P.R.China;
4. Department of Oncology, the Affiliated Hospital of Zunyi Medical University, Zunyi, 563000, P.R.China;
5. The Second Clinical Medicine College of Lanzhou University, Lanzhou, 730000, P.R.China;
6. Evidence-based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou, 730000, P.R.China;
7. Key Laboratory of Clinical Translational Research and Evidence-based Medicine of Gansu Province, Lanzhou, 730000, P.R.China;

Corresponding author：

TIAN Jinhui, Email: tjh996@163.com

Keywords：

Diagnostic test; Network meta-analysis; Accuracy; Indirect comparison

DOI：

10.7507/1672-2531.201706041

Video：

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Abstract Full text Figures/Tables Video References Cited by

It is a challenge for clinicians and diagnostic systematic reviewers to determine the best test in clinical diagnosis and screening. Meanwhile, it also becomes the new chance and challenge for diagnostic test meta-analysis. Network meta-analysis has been commonly used in intervention systematic reviews, which can compare the effect size of all available interventions and to choose the best intervention. Network meta-analysis of diagnostic test can be defined as comparing all available diagnostic technologies in the same conditions based on the common reference tests. In order to provide the guide for diagnostic systematic reviewers, we aims to introduce four methods of conducting diagnostic test accuracy network meta-analysis, and to explore two ranking methods of network meta-analysis of diagnostic test accuracy.

Citation： WU Jingling, GE Long, ZHANG Junhua, ZHOU Jianguo, GAO Ya, MA Xueni, LI Jipin, LIN Xiaoyan, TIAN Jinhui. Comparation of accuracy of different diagnostic tests: an introduction of network meta-analysis methods. Chinese Journal of Evidence-Based Medicine, 2017, 17(8): 987-992. doi: 10.7507/1672-2531.201706041 Copy

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4.	张家华, 葛龙, 田金徽, 等. PET/CT 和 CT 诊断宫颈癌腹主动脉淋巴结转移间接比较的 Meta 分析. 兰州大学学报(医学版), 2015, 41(1): 34-42.
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6.	杨品, 闫瑞峰, 闫坤, 等. 18F-FDG PET/CT 和增强 MSCT 诊断胰腺癌价值的 Meta 分析. 中国 CT 和 MRI 杂志, 2017, 15(2): 58-61.
7.	刘关键, 吴泰相. 诊断性试验的 Meta 分析—SROC 曲线法介绍. 中国循证医学杂志, 2003, 3(1): 41-44.
8.	Carpenter B, Gelman A, Hoffman MD, et al. Stan: a probabilistic programming language. J Stat Softw, 2017, 76(1): 1-32.
9.	Glas AS, Lijmer JG, Prins MH, et al. The diagnostic odds ratio: a single indicator of test performance. J Clin Epidemiol, 2003, 56(11): 1129-1135.
10.	Deutsch R, Mindt MR, Xu R, et al. Quantifying relative superiority among many binary-valued diagnostic tests in the presence of a gold standard. J Data Sci, 2009, 7: 161-177.
11.	田金徽, 陈耀龙, 杨克虎, 等. SR/MA 研究进展与挑战. 兰州大学学报(医学版), 2016, 42(1): 42-47.

1. 田金徽, Fujian Song, 李伦, 等. 间接比较方法简介. 中国循证医学杂志, 2014, 4(3): 365-368.
2. Josephson CB, White PM, Krishan A, et al. Computed tomography angiography or magnetic resonance angiography for detection of intracranial vascular malformations in patients with intracerebral haemorrhage. Cochrane Database Syst Rev, 2014, 9:CD009372.
3. Weng WW, Dong MJ, Zhang J, et al. A systematic review of MRI, scintigraphy, FDG-PET and PET/CT for diagnosis of multiple myeloma related bone disease - which is best? Asian Pac J Cancer Prev, 2014, 15(22): 9879-9884.
4. 张家华, 葛龙, 田金徽, 等. PET/CT 和 CT 诊断宫颈癌腹主动脉淋巴结转移间接比较的 Meta 分析. 兰州大学学报(医学版), 2015, 41(1): 34-42.
5. Nyaga VN, Aerts M, Arbyn M. ANOVA model for network meta-analysis of diagnostic test accuracy data. Stat Methods Med Res, 2016, pii: 0962280216669182.
6. 杨品, 闫瑞峰, 闫坤, 等. 18F-FDG PET/CT 和增强 MSCT 诊断胰腺癌价值的 Meta 分析. 中国 CT 和 MRI 杂志, 2017, 15(2): 58-61.
7. 刘关键, 吴泰相. 诊断性试验的 Meta 分析—SROC 曲线法介绍. 中国循证医学杂志, 2003, 3(1): 41-44.
8. Carpenter B, Gelman A, Hoffman MD, et al. Stan: a probabilistic programming language. J Stat Softw, 2017, 76(1): 1-32.
9. Glas AS, Lijmer JG, Prins MH, et al. The diagnostic odds ratio: a single indicator of test performance. J Clin Epidemiol, 2003, 56(11): 1129-1135.
10. Deutsch R, Mindt MR, Xu R, et al. Quantifying relative superiority among many binary-valued diagnostic tests in the presence of a gold standard. J Data Sci, 2009, 7: 161-177.
11. 田金徽, 陈耀龙, 杨克虎, 等. SR/MA 研究进展与挑战. 兰州大学学报(医学版), 2016, 42(1): 42-47.

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