Methods of data extraction in meta-analysis of diagnostic accuracy study_Chinese Journal of Evidence-Based Medicine

Authors：

LIU Haining ¹ , WU Hao ¹ , ZHANG Ningping ¹ , LI Yu ¹ , ZENG Yuzhen ² , SHEN Xizhong ¹ ,  LIU Taotao ¹

1. Department of Gastroenterology, Zhongshan Hospital, Fudan University, Shanghai, 200032, P.R.China;
2. Department of Gastroenterology, Huashan Hospital, Fudan University, Shanghai, 200032, P.R.China;

Corresponding?author：

LIU Taotao, Email: liu.taotao@zs-hospital.sh.cn

Keywords：

Meta-analysis; Diagnostic accuracy study; Evidence-based medicine; Data extraction

DOI：

10.7507/1672-2531.201805153

Video：

Export PDF Favorites Scan Get Citation

Abstract Full text Figures/Tables Video References Cited by

Receiving the amount of true positives, false positives, false negatives and true negatives is necessary when conducting meta-analysis of diagnostic tests. Advanced methods of data extraction are required if these data could not be directly obtained from a literature. We introduced three methods and discussed the theories. An example was then given to illustrate how to apply the methods.

Citation： LIU Haining, WU Hao, ZHANG Ningping, LI Yu, ZENG Yuzhen, SHEN Xizhong, LIU Taotao. Methods of data extraction in meta-analysis of diagnostic accuracy study. Chinese Journal of Evidence-Based Medicine, 2018, 18(9): 995-1000. doi: 10.7507/1672-2531.201805153 Copy

1.	Bossuyt PM, Reitsma JB, Bruns DE, et al. Towards complete and accurate reporting of studies of diagnostic accuracy: the STARD initiative. BMJ, 2003, 326(7379): 41-44.
2.	Bossuyt PM, Reitsma JB, Bruns DE, et al. STARD 2015: an updated list of essential items for reporting diagnostic accuracy studies. BMJ, 2015, 351: h5527.
3.	Hajian-Tilaki K. The choice of methods in determining the optimal cut-off value for quantitative diagnostic test evaluation. Stat Methods Med Res, 2018, 27(8): 2374-2383.
4.	陳世耀, 王吉耀. 診斷試驗的研究與評價. 林果為, 王小欽, 陳世耀, 主編. 現代臨床流行病學(第3版). 上海: 復旦大學出版社, 2014: 48-73.
5.	Habibzadeh F, Habibzadeh P, Yadollahie M. On determining the most appropriate test cut-off value: the case of tests with continuous results. Biochem Med (Zagreb), 2016, 26(3): 297-307.
6.	瞿振, 胡翠蘋. 一種 ROC 曲線數據提取方法并用于 miRNA-122 診斷慢性病毒性肝炎的 Meta 分析. 循證醫學, 2016, 16(3): 159-164.
7.	Higgins J, Green S. Cochrane handbook for systematic reviews of interventions, Version 5.1.0. The Cochrane Collaboration, 2011. Available at: http://handbook.cochrane.org/.
8.	劉海寧, 吳昊, 姚燦, 等. Meta 分析中連續性數據的深度提取方法. 中國循證醫學雜志, 2017, 17(1): 117-121.
9.	何寒青, 陳坤. Meta 分析中的異質性檢驗方法. 中國衛生統計, 2006, 23(6): 486-487,490.
10.	Liu HN, Liu TT, Wu H, et al. Serum microRNA signatures and metabolomics have high diagnostic value in colorectal cancer using two novel methods. Cancer Sci, 2018, 109(4): 1185-1194.
11.	Chang PY, Chen CC, Chang YS, et al. MicroRNA-223 and microRNA-92a in stool and plasma samples act as complementary biomarkers to increase colorectal cancer detection. Oncotarget, 2016, 7(9): 10663-10675.
12.	Zheng G, Du L, Yang X, et al. Serum microRNA panel as biomarkers for early diagnosis of colorectal adenocarcinoma. Br J Cancer, 2014, 111(10): 1985-1992.
13.	陳玉琢, 劉海寧, 劉韜韜. 結直腸癌患者血清 miRNA-767-3p 水平及其診斷意義. 復旦學報(醫學版), 2018, 45(1): 52-56.
14.	瞿振, 尹長青, 胡翠蘋. 基于 EXCEL 軟件的診斷性 Meta 分析中缺失數據的提取方法. 循證醫學, 2017, 17(2): 116-119,124.

1. Bossuyt PM, Reitsma JB, Bruns DE, et al. Towards complete and accurate reporting of studies of diagnostic accuracy: the STARD initiative. BMJ, 2003, 326(7379): 41-44.
2. Bossuyt PM, Reitsma JB, Bruns DE, et al. STARD 2015: an updated list of essential items for reporting diagnostic accuracy studies. BMJ, 2015, 351: h5527.
3. Hajian-Tilaki K. The choice of methods in determining the optimal cut-off value for quantitative diagnostic test evaluation. Stat Methods Med Res, 2018, 27(8): 2374-2383.
4. 陳世耀, 王吉耀. 診斷試驗的研究與評價. 林果為, 王小欽, 陳世耀, 主編. 現代臨床流行病學(第3版). 上海: 復旦大學出版社, 2014: 48-73.
5. Habibzadeh F, Habibzadeh P, Yadollahie M. On determining the most appropriate test cut-off value: the case of tests with continuous results. Biochem Med (Zagreb), 2016, 26(3): 297-307.
6. 瞿振, 胡翠蘋. 一種 ROC 曲線數據提取方法并用于 miRNA-122 診斷慢性病毒性肝炎的 Meta 分析. 循證醫學, 2016, 16(3): 159-164.
7. Higgins J, Green S. Cochrane handbook for systematic reviews of interventions, Version 5.1.0. The Cochrane Collaboration, 2011. Available at: http://handbook.cochrane.org/.
8. 劉海寧, 吳昊, 姚燦, 等. Meta 分析中連續性數據的深度提取方法. 中國循證醫學雜志, 2017, 17(1): 117-121.
9. 何寒青, 陳坤. Meta 分析中的異質性檢驗方法. 中國衛生統計, 2006, 23(6): 486-487,490.
10. Liu HN, Liu TT, Wu H, et al. Serum microRNA signatures and metabolomics have high diagnostic value in colorectal cancer using two novel methods. Cancer Sci, 2018, 109(4): 1185-1194.
11. Chang PY, Chen CC, Chang YS, et al. MicroRNA-223 and microRNA-92a in stool and plasma samples act as complementary biomarkers to increase colorectal cancer detection. Oncotarget, 2016, 7(9): 10663-10675.
12. Zheng G, Du L, Yang X, et al. Serum microRNA panel as biomarkers for early diagnosis of colorectal adenocarcinoma. Br J Cancer, 2014, 111(10): 1985-1992.
13. 陳玉琢, 劉海寧, 劉韜韜. 結直腸癌患者血清 miRNA-767-3p 水平及其診斷意義. 復旦學報(醫學版), 2018, 45(1): 52-56.
14. 瞿振, 尹長青, 胡翠蘋. 基于 EXCEL 軟件的診斷性 Meta 分析中缺失數據的提取方法. 循證醫學, 2017, 17(2): 116-119,124.

Previous Article
Establishment of quality criteria for clinical nursing practice guidelines in China

Chinese Journal of Evidence-Based Medicine

Methods of data extraction in meta-analysis of diagnostic accuracy study

Abstract Full text Figures/Tables Video References Cited by

Previous Article

Format

Content