Multivariate Random Effects Model in Meta-Analysis of Diagnostic Tests and Its SAS Programs_Chinese Journal of Evidence-Based Medicine

Authors：

LIU Wenhua , WU Jiali , YANG Yang , SONG Tingting , ZHANG Shunyue , CHEN Yuanfang ,  YIN Ping

Department of Epidemiology and Health Statistics, School of Public Health, Tongji Medical College of Huazhong University of Science and Technology, Wuhan 430030, China;

Corresponding author：

YIN Ping, Email: ping_y2000@163.com

Keywords：

Multivariate random effects model; Bivariate random effects model; Meta-analysis; Multiple threshold; Diagnostic test

DOI：

10.7507/1672-2531.20120037

Video：

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Abstract Full text Figures/Tables Video References Cited by

Objective 　To introduce the multivariate random effects model (MREM) in the meta-analysis of diagnostic tests with multiple thresholds.
Methods 　This paper expanded and extended the bivariate random effects model (BREM) to develop the MREM, and implemented it in the SAS Proc NLMIXED procedure.
Results 　The MREM could obtain the study specific ROC curve for each study through empirical Bayes estimation, and the summary ROC curve located in between all study specific ROC curves evenly, while the BREM couldn’t obtain the study specific ROC curve. In addition, in the aspect of parameters estimation, the MREM didn’t depend on the choice of the diagnosis threshold and the type of SROC. The MREM could get only one SROC curve and its AUC was between the AUC of the 5 types of SROC from BREM, so it could avoid overestimation or underestimation.
Conclusion 　The MREM can fully exploit the data, obtain stable and reliable results, and have a good application value in meta-analysis of diagnostic tests with multiple thresholds.

Citation： LIU Wenhua,WU Jiali,YANG Yang,SONG Tingting,ZHANG Shunyue,CHEN Yuanfang,YIN Ping. Multivariate Random Effects Model in Meta-Analysis of Diagnostic Tests and Its SAS Programs. Chinese Journal of Evidence-Based Medicine, 2012, 12(2): 231-237. doi: 10.7507/1672-2531.20120037 Copy

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5.	Arends LR, Hamza TH, van Houwelingen JC, et al. Bivariate random effects meta-analysis of ROC curves. Med Decis Making, 2008, 28(5): 621-638.
6.	Chu H, Cole SR. Bivariate meta-analysis of sensitivity and specificity with sparse data: a generalized linear mixed model approach. J Clin Epidemiol, 2006, 59(12): 1331-1312.
7.	Hamza TH, van Houwelingen HC, Stijnen T. The binomial distribution of meta-analysis was preferred to model within-study variability. J Clin Epidemiol, 2008, 61(1): 41-51.
8.	Hamza TH, Reitsma JB, Stijnen T. Meta-analysis of diagnostic studies: a comparison of random intercept, normal-normal, and binomial-normal bivariate summary ROC approaches. Med Decis Making, 2008, 28(5): 639-649.
9.	Dukic V, Gatsonis C. Meta-analysis of diagnostic test accuracy assessment studies with varying number of thresholds. Biometrics, 2003, 59(4): 936-946.
10.	Bipat S, Zwinderman AH, Bossuyt PM, et al. Multivariate random-effects approach: for meta-analysis of cancer staging studies. Acad Radiol, 2007, 14(8): 974-984.
11.	van Houwelingen HC, Arends LR, et al. Advanced methods in meta-analysis: multivariate approach and meta-regression. Stat Med, 2002, 21(4): 589-624.
12.	Hamza TH, Arends LR, van Houwelingen HC, et al. Multivariate random effects meta-analysis of diagnostic tests with multiple thresholds. BMC Med Res Methodol, 2009, 9: 73.
13.	SAS Institute Inc:SAS/STAT(r)9.1 user’s Guide Cary, NC:SAS Institute Inc; 2004.
14.	Menke J. Bivariate Random-effects Meta-analysis of Sensitivity and Specificity with SAS PROC GLIMMIX. Method Inf Med, 2010.
15.	Kobayashi T, Inoue Y, Takeuchi K, et al. Prediction of intravenous immunoglobulin unresponsiveness in patients with Kawasaki disease. Circulation, 2006, 113(22): 2606-2612.

1. 颜虹. 八年制及七年制医学统计学. 第2版. 北京: 人民卫生出版社, 2010: 444.
2. Littenberg B, Moses LE. Estimating diagnostic accuracy from multiple conflicting reports: a new meta-analytic method. Med Decis Making, 1993, 13(4): 313-321.
3. Moses LE, Shapiro D, Littenberg B. Combining independent studies of a diagnostic test into a summary ROC curve: data-analytic approaches and some additional considerations. Stat Med, 1993, 12(14): 1293-1316.
4. Reitsma JB, Glas AS, Rutjes AW, et al. Bivariate analysis of sensitivity and specificity produces informative summary measures in diagnostic reviews. J Clin Epidemiol, 2005, 58(10): 982-990.
5. Arends LR, Hamza TH, van Houwelingen JC, et al. Bivariate random effects meta-analysis of ROC curves. Med Decis Making, 2008, 28(5): 621-638.
6. Chu H, Cole SR. Bivariate meta-analysis of sensitivity and specificity with sparse data: a generalized linear mixed model approach. J Clin Epidemiol, 2006, 59(12): 1331-1312.
7. Hamza TH, van Houwelingen HC, Stijnen T. The binomial distribution of meta-analysis was preferred to model within-study variability. J Clin Epidemiol, 2008, 61(1): 41-51.
8. Hamza TH, Reitsma JB, Stijnen T. Meta-analysis of diagnostic studies: a comparison of random intercept, normal-normal, and binomial-normal bivariate summary ROC approaches. Med Decis Making, 2008, 28(5): 639-649.
9. Dukic V, Gatsonis C. Meta-analysis of diagnostic test accuracy assessment studies with varying number of thresholds. Biometrics, 2003, 59(4): 936-946.
10. Bipat S, Zwinderman AH, Bossuyt PM, et al. Multivariate random-effects approach: for meta-analysis of cancer staging studies. Acad Radiol, 2007, 14(8): 974-984.
11. van Houwelingen HC, Arends LR, et al. Advanced methods in meta-analysis: multivariate approach and meta-regression. Stat Med, 2002, 21(4): 589-624.
12. Hamza TH, Arends LR, van Houwelingen HC, et al. Multivariate random effects meta-analysis of diagnostic tests with multiple thresholds. BMC Med Res Methodol, 2009, 9: 73.
13. SAS Institute Inc:SAS/STAT(r)9.1 user’s Guide Cary, NC:SAS Institute Inc; 2004.
14. Menke J. Bivariate Random-effects Meta-analysis of Sensitivity and Specificity with SAS PROC GLIMMIX. Method Inf Med, 2010.
15. Kobayashi T, Inoue Y, Takeuchi K, et al. Prediction of intravenous immunoglobulin unresponsiveness in patients with Kawasaki disease. Circulation, 2006, 113(22): 2606-2612.

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