Revman,Stata,Meta-disc在诊断试验准确性（DTA）系统评价中的应用文献数据摘自《ProGRP与NSE对小细胞肺癌诊断价值的meta分析》文中提取数据注：表中10 个原始研究均使用酶联免疫吸附测定法检测阳性界值；TP= 真阳性数；FP= 假阳性数；FN= 假阴性数；TN= 真阴性数a:ProGRP,b: NSERevman5.2新建诊断试验准确性(DTA)系统评价模板添加所有纳入研究此处对每篇文献QUADAS2质量特征进行描述，以便探讨异质性来源及作表图数据分析里面添加所要研究的待评价诊断试验可计算相关指标（似然比及诊断比值比和单独在干预系统评价里面作森林图）添加分析里面制作SEN和SPE森林图及SROC曲线，可对数据进行重新制定StudyLamy 2000Molina 2009Nissan 2009Schneider 2003Shibayama 2001Stieber 1999Sun 2005Takada 1996Yamaguchi 1995Yang 2005TP3541134297411773802546FP18979611222669FN164641840292847917TN229218548119234972034696072Sensitivity (95% CI)0.69 [0.54, 0.81]0.47 [0.36, 0.58]0.77 [0.70, 0.83]0.78 [0.62, 0.90]0.65 [0.55, 0.74]0.80 [0.73, 0.86]0.72 [0.62, 0.81]0.63 [0.54, 0.71]0.74 [0.56, 0.87]0.73 [0.60, 0.83]Specificity (95% CI)0.93 [0.89, 0.96]0.96 [0.93, 0.98]0.87 [0.85, 0.90]0.95 [0.90, 0.98]0.96 [0.92, 0.98]0.98 [0.93, 1.00]0.90 [0.86, 0.94]0.99 [0.97, 1.00]0.91 [0.81, 0.97]0.89 [0.80, 0.95]Sensitivity (95% CI)00.20.40.60.81Specificity (95% CI)00.20.40.60.81设置参数若用户对上图窗口中的统计分析显示的结果不满意，可点击右上角的属性按钮); 或依次展开树形目录分支"Data and Analyses→Analyses→ProGRP"，选中"ProGRP"并单击右键，选择"Properties …"，弹出属性设置对话框。

在图对话框中，可对统计指标(General)、SROC图、森林图和异质性来源的参数进行设置，并点击"Apply"使其生效，见下图。

亚组分析（假如，原文没做）ProGRP(高质量)StudyLamy 2000Molina 2009Nissan 2009Schneider 2003Shibayama 2001TP35411342974FP18979611FN164641840TN229218548119234Sensitivity (95% CI)0.69 [0.54, 0.81]0.47 [0.36, 0.58]0.77 [0.70, 0.83]0.78 [0.62, 0.90]0.65 [0.55, 0.74]Specificity (95% CI)0.93 [0.89, 0.96]0.96 [0.93, 0.98]0.87 [0.85, 0.90]0.95 [0.90, 0.98]0.96 [0.92, 0.98]Sensitivity (95% CI)00.20.40.60.81Specificity (95% CI)00.20.40.60.81ProGRP(低质量)StudyStieber 1999Sun 2005Takada 1996Yamaguchi 1995Yang 2005TP11773802546FP222669FN292847917TN972034696072Sensitivity (95% CI)0.80 [0.73, 0.86]0.72 [0.62, 0.81]0.63 [0.54, 0.71]0.74 [0.56, 0.87]0.73 [0.60, 0.83]Specificity (95% CI)0.98 [0.93, 1.00]0.90 [0.86, 0.94]0.99 [0.97, 1.00]0.91 [0.81, 0.97]0.89 [0.80, 0.95]Sensitivity (95% CI)00.20.40.60.81Specificity (95% CI)00.20.40.60.81 QUADAS-2偏倚表（图）制作Patient Selection14463 Index Test424451 Reference Standard24446 Flow and Timing2530%25%50%75%100%0%25%50%75%100%Risk of Bias Applicability Concerns High Unclear LowP a t i e n t S e l e c t i o nLamy 2000?Molina 2009Nissan 2009+Schneider 2003?Shibayama 2001+Stieber 1999–Sun 2005+Takada 1996?Yamaguchi 1995+Yang 2005?I n d e x T e s t–?+–+––?++R e f e r e n c e S t a n d a r d??+–+?–?++F l o w a n d T i m i n g??+??––?++Risk of Bias P a t i e n t S e l e c t i o n––?–––?–?I n d e x T e s t??–?–?+?––R e f e r e n c e S t a n d a r d–?–?–???–?Applicability Concerns–High ?Unclear +Low异质性来源在DTA系统评价里面不能直接进行似然比、诊断比值比的森林图以及各指标漏斗图制作，但可以改变四个表数据模式或直接计算相关指标，添加入干预性系统评价模板中进行制作及查看异质性、发表偏倚（漏斗图）。

Stata12一拟合双变量混合效应模型：midas命令1.计算所有诊断试验统计学指标（敏感度、特异度、似然比、诊断比值比等）及异质性检验统计量:Diagnostic Odds Ratio 39.071 [ 25.251, 60.456]Diagnostic Score 3.665 [ 3.229, 4.102]Negative Likelihood Ratio 0.316 [ 0.262, 0.381]Positive Likelihood Ratio 12.348 [ 8.245, 18.494]Specificity 0.943 [ 0.913, 0.963]Sensitivity 0.702 [ 0.641, 0.756]Parameter Estimate 95% CIInconsistency (I-square): LRT_I2 = 96.39, 95% CI = [93.72-99.06]Heterogeneity (Chi-square): LRT_Q = 55.419, df =2.00, LRT_p =0.000ROC Area, AUROC = 0.89 [0.86 - 0.92]Correlation (Mixed Model)= -0.491Between-study variance(varlogitSPE)= 0.406, 95% CI = [0.133-1.241]Between-study variance(varlogitSEN) =0.136, 95% CI = [0.041-0.452]Pretest Prob of Disease =0.279Reference-negative Subjects = 2417Reference-positive Subjects = 935Number of studies = 10Bivariate Binomial Mixed ModelSUMMARY DATA AND PERFORMANCE ESTIMATES2. 绘制敏感度、特异度森林图:SENSITIVITY (95% CI)0.70[0.64 - 0.76]0.69 [0.54 - 0.81]0.47 [0.36 - 0.58]0.77 [0.70 - 0.83]0.78 [0.62 - 0.90]0.65 [0.55 - 0.74]0.80 [0.73 - 0.86]0.72 [0.62 - 0.81]0.63 [0.54 - 0.71]0.74 [0.56 - 0.87]0.73 [0.60 - 0.83]STUDY(YEAR)COMBINEDQ = 39.00, df = 9.00, p = 0.00I2 = 76.92 [62.83 - 91.01]Schneider 2003Stieber 1999Molina 2009Nissan 2009Shibayama 2001Lamy 2000Takada 1996Yamaguchi 1995Sun 2005Yang 20050.40.9SENSITIVITYSPECIFICITY (95% CI)0.94[0.91 - 0.96]0.93 [0.89 - 0.96]0.96 [0.93 - 0.98]0.87 [0.85 - 0.90]0.95 [0.90 - 0.98]0.96 [0.92 - 0.98]0.98 [0.93 - 1.00]0.90 [0.86 - 0.94]0.99 [0.97 - 1.00]0.91 [0.81 - 0.97]0.89 [0.80 - 0.95]STUDY(YEAR)COMBINEDQ = 75.58, df = 9.00, p = 0.00I2 = 88.09 [82.01 - 94.17]Schneider 2003Stieber 1999Molina 2009Nissan 2009Shibayama 2001Lamy 2000Takada 1996Yamaguchi 1995Sun 2005Yang 20050.8 1.0SPECIFICITY3. 绘制ROC 曲线图：0.00.51.0Sensitivity0.00.51.0SpecificityObserved DataSummary Operating PointSENS = 0.70 [0.64 - 0.76]SPEC = 0.94 [0.91 - 0.96]SROC CurveAUC = 0.89 [0.86 - 0.92]95% Confidence Ellipse95% Prediction EllipseSROC with Confidence and Predictive Ellipses4. 绘制漏斗图，识别发表偏倚:STATISTICAL TESTS FOR SMALL S TUDY EFFECTS/PUBLICATION B IAS> 206Intercept 3.78234 1.105751 3.42 0.009 1.232475 6.332> 146Bias -.7005376 17.62442 -0.04 0.969 -41.34253 39.94>> al]yb Coef. Std. Err. t P>|t| [95% Conf. Interv>11110.040.060.080.100.121/root(ESS)StudyRegressionLineLog Odds Ratio versus 1/sqrt(Effective Sample Size)(Deeks)5. 绘制似然比森林图:DLR POSITIVE (95% CI)12.35[8.24 - 18.49]9.42 [5.82 - 15.25]11.89 [6.03 - 23.41]6.08 [4.87 -7.59]16.33 [7.35 - 36.30]14.46 [7.99 - 26.16]39.67 [10.04 - 156.77]7.39 [4.88 - 11.19]49.87 [22.27 - 111.67]8.09 [3.67 - 17.81]6.57 [3.49 - 12.39]STUDY(YEAR)COMBINEDQ = 51.60, df = 9.00, p = 0.00I2 = 72.67 [72.67 - 92.45]Schneider 2003Stieber 1999Molina 2009Nissan 2009Shibayama 2001Lamy 2000Takada 1996Yamaguchi 1995Sun 2005Yang 20053.5156.8DLR POSITIVEDLR NEGATIVE (95% CI)0.32[0.26 - 0.38]0.34 [0.23 - 0.51]0.55 [0.45 - 0.67]0.27 [0.20 - 0.35]0.23 [0.12 - 0.42]0.37 [0.29 - 0.47]0.20 [0.15 - 0.28]0.31 [0.22 - 0.42]0.37 [0.30 - 0.47]0.29 [0.17 - 0.51]0.30 [0.20 - 0.46]STUDY(YEAR)COMBINEDQ = 42.30, df = 9.00, p = 0.00I2 = 78.72 [66.00 - 91.44]Schneider 2003Stieber 1999Molina 2009Nissan 2009Shibayama 2001Lamy 2000Takada 1996Yamaguchi 1995Sun 2005Yang 200501DLR NEGATIVE6. 绘制诊断比值比森林图：ODDS RATIO (95% CI)39.07[25.25 - 60.46]27.83 [12.99 - 59.60]21.59 [9.81 - 47.50]22.67 [14.87 - 34.57]71.90 [23.14 - 223.38]39.35 [19.22 - 80.58]195.67 [45.54 - 840.84]24.06 [12.95 - 44.68]133.05 [55.07 - 321.46]27.78 [8.94 - 86.29]21.65 [8.90 - 52.64]STUDY(YEAR)COMBINEDQ =10306.42, df = 9.00, p = 0.00I2 = 99.91 [99.90 - 99.92]Schneider 2003Stieber 1999Molina 2009Nissan 2009Shibayama 2001Lamy 2000Takada 1996Yamaguchi 1995Sun 2005Yang 20059841ODDS RATIODIAGNOSTIC SCORE (95% CI)3.67[3.23 -4.10]3.33 [1.41 - 3.33]3.07 [1.26 - 3.07]3.12 [1.49 - 3.12]4.28 [1.73 - 4.28]3.67 [1.63 - 3.67]5.28 [2.11 - 5.28]3.18 [1.41 - 3.18]4.89 [2.21 - 4.89]3.32 [1.21 - 3.32]3.07 [1.21 - 3.07]STUDY(YEAR)COMBINEDQ = 23.94, df = 9.00, p = 0.00I2 = 62.41 [36.63 - 88.18]Schneider 2003Stieber 1999Molina 2009Nissan 2009Shibayama 2001Lamy 2000Takada 1996Yamaguchi 1995Sun 2005Yang 2005 1.25.3DIAGNOSTIC SCORE7.绘制验前概率、 验后概率图：验前概率=患病率，验后概率=验前概率*似然比0.0010.0020.0050.010.020.050.10.20.51251020501002005001000Likelihood Ratio0.10.20.30.50.712357102030405060708090939597989999.399.599.799.899.9P o s t -t e s t P r o b a b i l i t y (%)0.10.20.30.50.712357102030405060708090939597989999.399.599.799.899.9Prior Prob (%) = 20LR_Positive = 12Post_Prob_Pos (%) = 76LR_Negative = 0.32Post_Prob_Neg (%) = 7Fagan's Nomogram二 拟合HSROC 模型：metandi 命令 1.合并统计量命令Covariance between estimates of E(logitSe) & E(logitSp) -.0117264>> 9451/LR- 3.164112 .3007045 2.626377 3.811> 526LR- .3160444 .0300356 .2623332 .3807> 296LR+ 12.34813 2.544551 8.245105 18.49> 194DOR 39.07088 8.70083 25.25202 60.45> 602Sp .9431557 .0123616 .9134894 .9630> 599Se .7019209 .029502 .6410901 .7563Summary pt.>> 023s2theta .1750668 .0938807 .0611986 .5008> 701s2alpha .2388279 .1917059 .0495257 1.151> 426beta .5485361 .3866854 1.42 0.156 -.2093534 1.306> 583Theta -.5042014 .3386717 -1.167986 .159> 009Lambda 3.261869 .2776274 2.71773 3.806HSROC>> 676Corr(logits) -.4913658 .3666173 -.9024235 .3879> 996Var(logitSp) .4063271 .2312948 .1331508 1.23> 984Var(logitSe) .1356511 .0833172 .0407018 .4520> 826E(logitSp) 2.808916 .230571 2.357005 3.260> 825E(logitSe) .8564618 .1410043 .5800985 1.132Bivariate>> al]Coef. Std. Err. z P>|z| [95% Conf. Interv>> 10Log likelihood = -67.370744 Number of studies =Meta-analysis of diagnostic accuracyIteration 3: log likelihood = -67.370744Iteration 2: log likelihood = -67.370744Iteration 1: log likelihood = -67.370761Iteration 0: log likelihood = -67.378598Performing gradient-based optimization:Iteration 3: log likelihood = -67.378598Iteration 2: log likelihood = -67.980313Iteration 1: log likelihood = -69.302533Iteration 0: log likelihood = -73.728348 (not concave)Refining starting values:. metandi tp fp fn tn2.绘制SROC曲线.2.4.6.81Sensitivity.2.4.6.81SpecificityStudy estimate Summary pointHSROC curve95% confidenceregion95% predictionregionMeta-disc14表2 Meta-Disc软件的主要功能主要功能说明Describing primary results and exploringheterogeneity描述原始结果和探索异质性●Tabular results ●将结果以表格形式列出●Forest plots(sensitivity,specificity,LRs,dOR) ●以森林图形式显示灵敏度、特异度、似然比和诊断比值比●ROC plane scatter-plots ●ROC平面散状图●Cochran-Q,Chi-Square, Inconsistency index ●判断研究间异质性●Filtering/subgrouping capacities ●亚组分析Exporing Threshold effect 探讨阈值效应●Spearman correlation coefficient ●Spearman相关系数●ROC plane plots ●ROC平面图SROC curve fitting.Area under the curve(AUC) andQ拟合SROC曲线、计算AUC和Q指数Meta-regression analysis 回归分析，探讨异质性来源●Univariate and multivariate Moses andLitteenberg model(weight and unweight) ●（加权或未加权）单变量及多变量MosesLitteenberg模型Statistical polling of indices 合并统计量●Fixed effect model ●固定效应模型●Random effect model ●随机效应模型数据录入Meta分析1.探索阈值效应2.合并效应量、探讨异质性Summary SpecificityStudy | Spe [95% Conf. Iterval.] TP/(TP+FN) TN/(TN+FP)--------------------------------------------------------------------------------------Schneider | 0.927 0.887 - 0.956 35/51 229/247Stieber | 0.960 0.926 - 0.982 41/87 218/227Molina |0.874 0.845 - 0.899 134/175 548/627 Nissan | 0.952 0.898 - 0.982 29/37 119/125 Shibayama | 0.955 0.921 - 0.977 74/114 234/245 Lamy | 0.980 0.929 - 0.998 117/146 97/99 Takada | 0.902 0.856 - 0.938 73/101 203/225 Yamaguchi | 0.987 0.973 - 0.995 80/127 469/475 Sun | 0.909 0.813 - 0.966 25/34 60/66Yang | 0.889 0.800 - 0.948 46/63 72/81--------------------------------------------------------------------------------------Pooled Spe | 0.930 0.920 - 0.940--------------------------------------------------------------------------------------Heterogeneity chi-squared = 77.68 (d.f.= 9) p = 0.000Inconsistency (I-square) = 88.4 %No. studies = 10.Filter OFFAdd 1/2 to all cells of the studies with zero3.绘制森林图4.绘制SROC曲线5.meta回归分析。