多變量隨機效應模型在診斷試驗Meta分析中的應用與SAS實現_《中國循證醫學雜志》

作者：

劉文華 , 吳家利 , 楊揚 , 宋婷婷 , 章順悅 , 陳遠方 ,  尹平

華中科技大學同濟醫學院公共衛生學院流行病與衛生統計學系（武漢 430030）;

關鍵詞：

多變量隨機效應模型雙變量隨機效應模型 Meta分析多閾值診斷試驗

DOI：

10.7507/1672-2531.20120037

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目的　介紹多變量隨機效應模型（MREM）在多個閾值診斷試驗Meta分析中的應用。
方法　對雙變量隨機效應模型（BREM）進行擴展和延伸，構建MREM，并通過SAS的PROC NLMIXED過程進行統計實現。
結果　MREM可根據貝葉斯估計得到各研究的ROC曲線，且其SROC曲線均勻分布在各研究的ROC曲線之間，而BREM無法獲得各研究的ROC曲線。同時，MREM的參數估計不依賴于診斷閾值和構建SROC曲線方法的選擇，可以獲得唯一的SROC曲線，其AUC介于BREM的5種SROC曲線的AUC之間，可避免高估或低估的情形。
結論　MREM能夠充分利用各研究的原始數據信息，得到穩定可靠的結果，在多閾值診斷試驗Meta分析中有較好的應用價值。

引用本文： 劉文華,吳家利,楊揚,宋婷婷,章順悅,陳遠方,尹平. 多變量隨機效應模型在診斷試驗Meta分析中的應用與SAS實現. 中國循證醫學雜志, 2012, 12(2): 231-237. doi: 10.7507/1672-2531.20120037 復制

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1. 顏虹. 八年制及七年制醫學統計學. 第2版. 北京: 人民衛生出版社, 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.

《中國循證醫學雜志》

多變量隨機效應模型在診斷試驗Meta分析中的應用與SAS實現

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《中國循證醫學雜志》

多變量隨機效應模型在診斷試驗Meta分析中的應用與SAS實現

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