STATA-只有OR值的meta分析方法

1、meta-analysis in stata,stata简介,由美国计算机资源中心（computer resource center）研制； 现在为stata公司的产品； 目前最高版本9.0； 与sas、spss一起并称为三大权威统计软件。 操作灵活、简单、易学易用，同时具有数据管理软件、统计分析软件、绘图软件、矩阵计算软件和程序语言的特点,stata的进入和退出,进入：双击stata软件图标 退出：菜单方式: fileexit 窗口方式 命令方式:exit,stata的用户界面,命令回顾,变量列表,结果输出,stata数据管理,数据的输入与储存 数据库的排序 删除变量或记录 保留变量或记录

2、产生新变量 替换已存在的变量值,数据的输入与存储,数据输入 数据编辑窗口； 复制和粘贴。 数据储存 save命令 filesave as,数据编辑窗口,使用数据编辑窗口输入,数据库的排序,sort 变量名1 变量名2 gsort +/- 变量清单 sort x 对变量x中数值从小到大进行排列 gsort x 对变量x中数值从小到大进行排列 gsort -x 对变量x中数值从大到小进行排列,删除（或保留）变量或记录,删除变量或记录 drop varlist drop if exp drop in range 保留变量或记录 keep varlist keep if exp keep in ran

3、ge if exp,删除变量或记录,drop x1 x2 /* 删除变量x1和x2 drop x1-x3 /* 删除数据库中介于x1和x3间的所 有变量 (包括x1和x3) drop if x0 /* 删去x0的所有记录 drop in 1/3 /* 删去第13个记录 drop if x=. /* 删去x为缺失值的所有记录 drop if x=.|y=. /* 删去x或y之一为缺失值的所有记录 drop if x=.&y=. /* 删去x和y同时为缺失值的所有记录 drop _all /* 删掉数据库中所有变量和数据,保留变量或记录,keep in 1/5 /* 保留第15个记录，其余记录删除

4、 keep x1-x3 /* 保留数据库中介于x1和x3间的所有变量 (包括x1和x3)，其余变量删除 keep if x0 /* 保留x0的所有记录，其余记录删除,用generate产生新变量,generate 新变量表达式 gen yx+1 /*产生新变量y，其值为x+1。 gen y=log(x) if x0 /* 产生新变量y，其值为所有x0的对数log(x)，当x=0时，用缺失值代替,替换已存在的变量值,replace 变量表达式 replace bolck=6 if block=0 /* 将block0的数全部替换为6。 replace z=. if z0 /* 将所有小于0的z值

5、用缺失值代替。 replace age = 25 in 2 /* 将第2条记录中的变量age替换为25,fixed and random effects meta-analysis,metan varlist if exp in range , rr or rd fixed random fixedi randomi peto by(byvar) label(namevar=name, yearvar=year) rr pools risk ratios default or pools odds ratios rd pools risk differences fixed specifies

6、 a fixed effect model random specifies a random effects model,the following new variables to the data set,_es effect size (es) _sees standard error of es or, when or or rr are specfied: _seloges the standard error of its logarithm _lci lower confidence limit for es _uci upper confidence limit for es

7、 _wt study percentage weight _ss study sample size,example 1,stata result,metan casec casea controlc controla if country=european,or label(namevar=study) metan a b c d,or label(namevar=study) study | or 95% conf. interval % weight -+- misra rr | 1.060 0.658 1.707 14.60 hou sm | 1.111 0.606 2.036 8.8

8、5 zhou w | 1.166 0.907 1.498 50.23 spitz mr | 1.359 0.840 2.199 12.68 david-beabes gl1 | 1.724 1.039 2.859 9.72 david-beabes gl2 | 1.392 0.595 3.258 3.92 -+- m-h pooled or | 1.233 1.035 1.469 100.00 -+- heterogeneity chi-squared = 2.61 (d.f. = 5) p = 0.760 i-squared (variation in or attributable to

9、heterogeneity) = 0.0% test of or=1 : z= 2.34 p = 0.019,forest plot,stata result,metan casec casea controlc controla if country=asian,or label(namevar=study) study | or 95% conf. interval % weight -+- ling g | 2.358 0.902 6.166 25.55 chen s | 0.442 0.190 1.027 71.85 park jy | 1.980 0.080 48.927 2.60

10、-+- m-h pooled or | 0.972 0.544 1.737 100.00 -+- heterogeneity chi-squared = 6.81 (d.f. = 2) p = 0.033 i-squared (variation in or attributable to heterogeneity) = 70.6% test of or=1 : z= 0.10 p = 0.923,stata result,metan casec casea controlc controla if country=asian,or label(namevar=study) random s

11、tudy | or 95% conf. interval % weight -+- ling g | 2.358 0.902 6.166 42.15 chen s | 0.442 0.190 1.027 44.27 park jy | 1.980 0.080 48.927 13.57 -+- d+l pooled or | 1.097 0.279 4.316 100.00 -+- heterogeneity chi-squared = 6.81 (d.f. = 2) p = 0.033 i-squared (variation in or attributable to heterogenei

12、ty) = 70.6% estimate of between-study variance tau-squared = 0.9175 test of or=1 : z= 0.13 p = 0.894,forest plot,stata result,metan casec casea controlc controla ,or by(country) label(namevar=study) study | or 95% conf. interval % weight -+- asian ling g | 2.358 0.902 6.166 2.38 chen s | 0.442 0.190

13、 1.027 6.69 park jy | 1.980 0.080 48.927 0.24 sub-total m-h pooled or | 0.972 0.544 1.737 9.32 -+- european misra rr | 1.060 0.658 1.707 13.24 hou sm | 1.111 0.606 2.036 8.02 zhou w | 1.166 0.907 1.498 45.55 spitz mr | 1.359 0.840 2.199 11.50 david-beabes gl1 | 1.724 1.039 2.859 8.81 david-beabes gl

14、2 | 1.392 0.595 3.258 3.56 sub-total m-h pooled or | 1.233 1.035 1.469 90.68 -+- overall m-h pooled or | 1.209 1.022 1.430 100.00 -+- test(s) of heterogeneity: heterogeneity degrees of statistic freedom p i-squared* asian 6.81 2 0.033 70.6% european 2.61 5 0.760 0.0% overall 10.09 8 0.259 20.7% over

15、all test for heterogeneity between sub-groups : 0.67 1 0.415 * i-squared: the variation in or attributable to heterogeneity) significance test(s) of or=1 asian z= 0.10 p = 0.923 european z= 2.34 p = 0.019 overall z= 2.21 p = 0.027,forest plot,example 2,stata result,metan n1 mean1 sd1 n2 mean2 sd2 st

16、udy | smd 95% conf. interval % weight -+- 1 | -0.329 -0.820 0.162 15.34 2 | -0.677 -1.178 -0.176 14.74 3 | -0.357 -0.855 0.141 14.91 4 | -0.658 -1.162 -0.154 14.57 5 | -0.305 -0.806 0.196 14.73 6 | -0.408 -0.787 -0.029 25.71 -+- i-v pooled smd | -0.449 -0.642 -0.257 100.00 -+- heterogeneity chi-squa

17、red = 2.18 (d.f. = 5) p = 0.824 i-squared (variation in smd attributable to heterogeneity) = 0.0% test of smd=0 : z= 4.58 p = 0.000,forest plot,tests for publication bias in meta-analysis,metabias theta se_theta , by(by_var) graph(begg,stata result,gen logor=log(_es) gen selogor= _seloges metabias l

18、ogor selogor, graph(begg) note: default data input format (theta, se_theta) assumed. tests for publication bias beggs test adj. kendalls score (p-q) = 6 std. dev. of score = 9.59 number of studies = 9 z = 0.63 pr |z| = 0.532 z = 0.52 (continuity corrected) pr |z| = 0.602 (continuity corrected) eggers test - std_eff | coef. std. err. t p|t| 95% conf. interval -+- slope |.1546435 .2095145 0.74 0.484 -.3407797 .6500667 bias |.1631062 .8120607 0.20 0.847 -1.757112 2.083325,lung cancer2005