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.	陈世耀, 王吉耀. 诊断试验的研究与评价. 林果为, 王小钦, 陈世耀, 主编. 现代临床流行病学(第3版). 上海: 复旦大学出版社, 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, 胡翠苹. 一种 ROC 曲线数据提取方法并用于 miRNA-122 诊断慢性病毒性肝炎的 Meta 分析. 循证医学, 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.	刘海宁, 吴昊, 姚灿, 等. Meta 分析中连续性数据的深度提取方法. 中国循证医学杂志, 2017, 17(1): 117-121.
9.	何寒青, 陈坤. Meta 分析中的异质性检验方法. 中国卫生统计, 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.	陈玉琢, 刘海宁, 刘韬韬. 结直肠癌患者血清 miRNA-767-3p 水平及其诊断意义. 复旦学报(医学版), 2018, 45(1): 52-56.
14.	瞿振, 尹长青, 胡翠苹. 基于 EXCEL 软件的诊断性 Meta 分析中缺失数据的提取方法. 循证医学, 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. 陈世耀, 王吉耀. 诊断试验的研究与评价. 林果为, 王小钦, 陈世耀, 主编. 现代临床流行病学(第3版). 上海: 复旦大学出版社, 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, 胡翠苹. 一种 ROC 曲线数据提取方法并用于 miRNA-122 诊断慢性病毒性肝炎的 Meta 分析. 循证医学, 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. 刘海宁, 吴昊, 姚灿, 等. Meta 分析中连续性数据的深度提取方法. 中国循证医学杂志, 2017, 17(1): 117-121.
9. 何寒青, 陈坤. Meta 分析中的异质性检验方法. 中国卫生统计, 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. 陈玉琢, 刘海宁, 刘韬韬. 结直肠癌患者血清 miRNA-767-3p 水平及其诊断意义. 复旦学报(医学版), 2018, 45(1): 52-56.
14. 瞿振, 尹长青, 胡翠苹. 基于 EXCEL 软件的诊断性 Meta 分析中缺失数据的提取方法. 循证医学, 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