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》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Study Lamy 2000Molina 2009Nissan 2009Schneider 2003Shibayama 2001Stieber 1999Sun 2005Takada 1996Yamaguchi 1995Yang 2005TP 3541134297411773802546FP 18979611222669FN 164641840292847917TN 229218548119234972034696072Sensitivity (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置，并点击"Apply"使其生效，见下图。

亚组分析（假如，原文没做）QUADAS-2偏倚表（图）制作P 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:SUMMARY DATA AND PERFORMANCE ESTIMATESBivariate Binomial Mixed ModelNumber of studies = 10Reference-positive Subjects = 935Reference-negative Subjects = 2417Pretest Prob of Disease =0.279Between-study variance(varlogitSEN) =0.136, 95% CI = [0.041-0.452] Between-study variance(varlogitSPE)= 0.406, 95% CI = [0.133-1.241] Correlation (Mixed Model)= -0.491ROC Area, AUROC = 0.89 [0.86 - 0.92]Heterogeneity (Chi-square): LRT_Q = 55.419, df =2.00, LRT_p =0.000 Inconsistency (I-square): LRT_I2 = 96.39, 95% CI = [93.72-99.06]Parameter Estimate 95% CISensitivity 0.702 [ 0.641, 0.756]Specificity 0.943 [ 0.913, 0.963]Positive Likelihood Ratio 12.348 [ 8.245, 18.494]Negative Likelihood Ratio 0.316 [ 0.262, 0.381]Diagnostic Score 3.665 [ 3.229, 4.102]Diagnostic Odds Ratio 39.071 [ 25.251, 60.456]2. 绘制敏感度、特异度森林图:3. 绘制 ROC 曲线图：4. 绘制漏斗图， 识别发表偏倚:STATISTICAL TESTS FOR SMALL STUDY EFFECTS/PUBLICATIONBIASS e n s i ti v i t y> 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>5.绘制似然比森林图:6.绘制诊断比值比森林图：7.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*似然比二拟合HSROC模型：metandi命令1.合并统计量命令2.绘制SROC 曲线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 = -67.370744 Iteration 2: log likelihood = -67.370744 Iteration 1: log likelihood = -67.370761 Iteration 0: log likelihood = -67.378598 Performing gradient-based optimization:Iteration 3: log likelihood = -67.378598 Iteration 2: log likelihood = -67.980313 Iteration 1: log likelihood = -69.302533 Iteration 0: log likelihood = -73.728348 (not concave)Refining starting values:. metandi tp fp fn tnS e n s i t i v it yMeta-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●Forestplots(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)and Q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3.绘制森林图4.绘制SROC曲线5.meta回归分析。