Stata运行结果及分析

1、log： 赵耐青统计分析结果.loglog type: textopened on: 20 Jul 2002, 10:47:03./*调用数据库*/.use " 赵而寸青 clinical trial data(Chenfeng).dta ”, clear./*描述数据库*/.describe赵而寸青 clinical trial data(Chenfeng).dtaobs:240vars:607 Feb 2002 21:41size:35,760 (87.5% of memory free)storage display valuevariable name type format

2、 label variable labelNoint %8.0g编号Centerbyte %8.0g中心编号Namestr3 %9s姓名Genderbyte %8.0gsex性别Agebyte %8.0g年龄（岁）Hightint %8.0g身高（cm）Weightfloat %9.0g体重（kg）Baselbyte%8.0g糖尿病病程（年）Base2byte%8.0g神经病病变病程（年）mdns0byte%8.0g疗前MDNSmdnslbyte%8.0g疗后1月MDNSmdns2byte%8.0g疗后2月MDNSmdns3byte%8.0g疗后3月MDNSmdns4byte%8.0g疗后4月

3、MDNSmdns5byte%8.0g疗后5月MDNSmdns6byte%8.0g疗后6月MDNSfell0byte%8.0g疗前感觉障碍得分felllbyte%8.0g疗后1月感觉障碍得分fell2byte%8.0g疗后2月感觉障碍得分fell3byte%8.0g疗后3月感觉障碍得分fell4byte%8.0g疗后4月感觉障碍得分fell5byte%8.0g疗后5月感觉障碍得分fell6byte%8.0g疗后6月感觉障碍得分gca0float%9.0g疗前正中感觉N传导速度gcalfloat%9.0g疗后正中感觉N传导速度gcb0float%9.0g疗前尺感觉N传导速gcblfloat%9.0

4、g疗后尺感觉N传导速gcc0float%9.0g疗前腓感觉N传导速度gcclfloat%9.0g疗后腓感觉N传导速度yca0float%9.0g疗前正中运动N传导速度ycalfloat%9.0g疗后正中运动N传导速度ycb0float %9.0g疗前腓总运动N传导速度ycblfloat %9.0g疗后腓总运动N传导速度HbA1c0float %9.0g疗前糖化血红蛋白HbA1c1float %9.0g疗后糖化血红蛋白RBC0float %9.0g疗前红细胞数RBC1float %9.0g疗后红细胞数WBC0float %9.0g疗前白细胞数WBC1float %9.0g疗后白细胞数CRE0fl

5、oat %9.0g疗前肌酊CRE1float %9.0g疗后肌酊ALT0float %9.0g疗前谷丙转氨酶ALT1float %9.0g疗后谷丙转氨酶TG0float %9.0g疗前甘油三脂TG1float %9.0g疗后甘油三脂CHO0float %9.0g疗前总胆固醇CHO1float %9.0g疗后总胆固醇ECG0byte %8.0gECG疗前心电图ECG1byte %8.0gECG疗后心电图side1byte %8.0g不良反应之一degree1byte %8.0gdegree 程度relate1byte %8.0grelate 与药物的关系side2byte %8.0g不良反应之二

6、degree2byte %8.0gdegree 程度relate2byte %8.0grelate 与药物的关系side3byte %8.0g不良反应之三degree3byte%8.0gdegree程度relate3byte%8.0grelate与药物的关系Groupfloat%9.0ggroup分组变量dfloat %9.0gSorted by:./*各中心各组病例分配*/.tab Center Group|分组变量中心编号 | Treatment Placebo | Total+1 |2424 |482 |2424 |483 |2424 |484 |2424 |485 |2424 |48+

7、Total |120120 |240结果显示，各中心均按方案完成了规定的入组病例数。./*产生一变量表示脱落*/.gen loss=.(240 missing values generated)./*计算脱落时间*/.for num 1/ 6 : replace loss=X if mdnsX=. & loss=.-> replace loss=1 if mdns1=. & loss=.(0 real changes made)-> replace loss=2 if mdns2=. & loss=.(0 real changes made)-> re

8、place loss=3 if mdns3=. & loss=.(0 real changes made)-> replace loss=4 if mdns4=. & loss=.(3 real changes made)-> replace loss=5 if mdns5=. & loss=.(0 real changes made)-> replace loss=6 if mdns6=. & loss=.(0 real changes made)./*按中心排序 */. sort Center./*各中心各组脱落情况*/. by Cente

9、r : table Group loss-> Center = 1no observations-> Center = 2no observations-> Center = 3| loss分组变量| 4+Treatment |1-> Center = 4| loss分组变量| 4+Placebo | 2-> Center = 5no observations./*脱落情况列表*/.list No Center Name mdns0 Group loss if loss=.NoCenterNamemdns0 Grouploss139.139LFZ17Treatme

10、nt179.179TCY12Placebo192.192XXX18Placebo结果显示，共有3例脱落，其中,3中心在第4次随访时NGF组有1例脱落；第4中心在第4次随访时安慰剂组有2例脱落。./*两组性别比较 */. tab Group Gender, chi| 性别分组变量 | female male | Total+Treatment |6357 |120Placebo |6456 |120-L4-Total |127113 |240Pearson chi2(1) = 0.0167 Pr = 0.897./*两组年龄比较*/ .ttest Age, by(Group)Two-sample

11、 t test with equal variancesGroup | Obs Mean Std. Err. Std. Dev. 95% Conf. Interval +Treatmen |12060.06667.75840148.30787158.5649661.56838Placebo |12058.03333.80882168.86019656.4317959.63488+combined |24059.05.55711768.63082957.9525160.14749+diff |2.0333331.108767-.15091784.217585Degrees of freedom:

12、 238Ho: mean(Treatmen) - mean(Placebo) = diff = 0Ha: diff < 0t = 1.8339P < t = 0.9660Ha: diff = 0t = 1.8339P > |t| = 0.0679Ha: diff > 0t = 1.8339P > t = 0.0340./*扣除性别影响后两组的身高比较*/ . anova High Group GenderNumber of obs =240 R-squared0.4022Root MSE = 6.07288 Adj R-squared = 0.3971Source

13、 |-LPartial SSdfMSFProb > FModel |I5880.4695422940.2347779.720.0000|Group |3.5252739913.525273990.100.7575Gender |15874.1320415874.13204159.280.0000|Residual |8740.5262923736.8798578+Total | 14620.9958 239 61.1757148./*扣除性别影响后两组的体重比较*/. anova Weight Group GenderNumber of obs =240R-squared = 0.192

14、2Root MSE = 9.11056Adj R-squared = 0.1854Source | Partial SS df MS F Prob > F +Model | 4681.226192 2340.6130928.200.0000|Group | 188.5753431 188.5753432.270.1331Gender | 4476.984691 4476.9846953.940.0000|Residual | 19671.5512 237 83.0023256+Total | 24352.7774 239 101.894466./*各组基线情况比较 */.for var

15、Base1 Base2 mdns0 fell0 gca0 : ttest X , by(Group)-> ttest Base1 , by(Group)Two-sample t test with equal variancesGroup | Obs Mean Std. Err. Std. Dev. 95% Conf. Interval+Treatmen |1203.55.21198342.3221623.1302523.969748Placebo |120 3.258333.22469892.4614542.8134073.70326+combined |2403.404167.154

16、4209 2.3922783.099967 3.708366+diff |.2916667 .3089119-.3168841.9002175Degrees of freedom: 238Ho: mean(Treatmen) - mean(Placebo) = diff = 0Ha: diff < 0t = 0.9442P < t = 0.8270Ha: diff = 0t = 0.9442P > |t| = 0.3460Ha: diff > 0t = 0.9442P > t = 0.1730-> ttest Base2 , by(Group)Two-sam

17、ple t test with equal variancesGroup | Obs Mean Std. Err. Std. Dev. 95% Conf. Interval +Treatmen |1201.208333.16732191.832919 .87701931.539647Placebo |1201.066667.14673541.607406.77611591.357217+combined |2401.1375.11113581.721708.91856931.356431+diff |.1416667 .2225486-.29675.5800834Degrees of free

18、dom: 238Ho: mean(Treatmen) - mean(Placebo) = diff = 0Ha: diff < 0Ha: diff = 0Ha: diff > 0t = 0.6366t = 0.6366t = 0.6366P < t = 0.7375P > |t| = 0.5250P > t = 0.2625-> ttest mdns0 , by(Group)Two-sample t test with equal variancesGroup | Obs Mean Std. Err. Std. Dev. 95% Conf. Interval

19、+Treatmen |12014.21667.43227354.73531913.3607215.07261Placebo |12014.79167.45418934.97539513.8923315.69101+combined |24014.50417.31340344.85522413.8867815.12155+diff |-.575.6270154-1.810209.6602088Degrees of freedom: 238Ho: mean(Treatmen) - mean(Placebo) = diff = 0Ha: diff < 0t = -0.9170P < t

20、= 0.1800Ha: diff = 0t = -0.9170P > |t| = 0.3600Ha: diff > 0t = -0.9170P > t = 0.8200-> ttest fell0 , by(Group)Two-sample t test with equal variancesGroup | Obs Mean Std. Err. Std. Dev. 95% Conf. Interval+Treatmen |12010.375.51789585.6732649.34951511.40049Placebo |120 9.458333.47528495.20

21、64868.51722210.39944+combined |2409.916667.35198025.4528549.22328710.61005+diff |.9166667 .7029309-.46809412.301427Degrees of freedom: 238Ho: mean(Treatmen) - mean(Placebo) = diff = 0Ha: diff < 0t = 1.3041P < t = 0.9033Ha: diff = 0t = 1.3041P > |t| = 0.1935Ha: diff > 0t = 1.3041P > t

22、= 0.0967-> ttest gca0 , by(Group)Two-sample t test with equal variancesGroup | Obs Mean Std. Err. Std. Dev. 95% Conf. Interval +Treatmen |11449.625441.0553811.2683747.5345451.71634Placebo |115 47.367831.11262111.9315345.16373 49.57192+combined |22948.4917.768914711.6357946.9766250.00679+diff |2.2

23、576131.533926-.76494235.280168Degrees of freedom: 227Ho: mean(Treatmen) - mean(Placebo) = diff = 0Ha: diff < 0Ha: diff = 0Ha: diff > 0t = 1.4718t = 1.4718t = 1.4718P < t = 0.9288P > |t| = 0.1425P > t = 0.0712从上述分析可知，两组的基本情况和基线情况是一致的。即两组具有可比性。./*计算各时点MDNS的减分率*/.for var mdns1-mdns6 : ge

24、n pX = (mdns0-X)/mdns0-> gen pmdns1 = (mdns0-mdns1)/mdns0-> gen pmdns2 = (mdns0-mdns2)/mdns0-> gen pmdns3 = (mdns0-mdns3)/mdns0-> gen pmdns4 = (mdns0-mdns4)/mdns0(3 missing values generated)-> gen pmdns5 = (mdns0-mdns5)/mdns0(3 missing values generated)-> gen pmdns6 = (mdns0-mdns6)

25、/mdns0(3 missing values generated)./* 按 Group 排序 */ . sort Group./*各组各时间点的减分率描述*/.by Group : summ pmdns*-> Group = TreatmentVariable | Obs Mean Std. Dev. Min Max+pmdns1 |120.0977496.1776434-.375.6pmdns2 |120.1943255.2145272-.375 .8571429pmdns3 |120.2586773.2252976-.4285714.7333333pmdns4 |119.3505

26、427.2422572-.33333331pmdns5 |119.4200117.2478205-.33333331pmdns6 |119.4998484.2489689-.33333331-> Group = PlaceboVariable | Obs Mean Std. Dev. Min Maxpmdns1 |120.036402.1624928-.5714286.4615385pmdns2 |120.0810332.221016-.5714286.7142857pmdns3 |120.1347084.2422806-.57142861pmdns4 |118.1680638.2485

27、104-.57142861pmdns5 |118.1877292.2597217-.51pmdns6 |118.2154119.2516401-.51./*保存现有数据库*/赵耐青 clinical trial data(ChenFeng)1.dta"赵而寸青 clinical trial data(ChenFeng)1.dta saved./*疗前的减分率为0 */.gen pmdns0=0./*产生各时间点的均数以便画图*/.collapse pmdns0 pmdns1-pmdns6 , by(Group).drop Group./*将横向排列的数据库转为列向排列*/.xpose

28、, clear./*产生时间变量 */.gen week=2*(_n-1)./*绘制MDNS平均增加值随时间变化的曲线*/.gra v1 v2 week , c(ll-) xlab(0,2,4,6,8,10,12) ylab saving(" 赵耐青 图 1", replace)./*调用原数据库*/赵耐青 clinical trial data(ChenFeng)1.dta", clear./*各组各时间点MDNS的减分率比较*/.for var pmdns1-pmdns6 : ttest X, by(Group)-> ttest pmdns1, by(G

29、roup)Two-sample t test with equal variancesGroup | Obs Mean Std. Err. Std. Dev. 95% Conf. Interval+Treatmen |120.0977496.0162165.1776434.0656392.12986Placebo |120.036402.0148335.1624928.0070302.0657738+combined |240.0670758.0111438.1726386.0451232.0890284+diff |.0613475 .0219775.0180523.1046427Degre

30、es of freedom: 238Ho: mean(Treatmen) - mean(Placebo) = diff = 0Ha: diff < 0Ha: diff = 0Ha: diff > 0t = 2.7914t = 2.7914t = 2.7914P < t = 0.9972 P > |t| = 0.0057 P > t = 0.0028-> ttest pmdns2, by(Group)Two-sample t test with equal variancesGroup | Obs Mean Std. Err. Std. Dev. 95% Co

31、nf. IntervalTreatmen |120.1943255.0195836.2145272.1555481.233103Placebo |120.0810332.0201759.221016.0410829.1209835combined |240.1376794.0144998.2246302.1091156 .1662431diff |.1132924.0281173.0579018.168683Degrees of freedom: 238Ho: mean(Treatmen) - mean(Placebo) = diff = 0Ha: diff < 0Ha: diff =

32、0Ha: diff > 0t = 4.0293t = 4.0293t = 4.0293P < t = 1.0000 P > |t| = 0.0001P > t = 0.0000-> ttest pmdns3, by(Group)Two-sample t test with equal variancesGroup | Obs Mean Std. Err. Std. Dev. 95% Conf. Interval +Treatmen |120.2586773.0205668.2252976.217953.2994015Placebo |120.1347084.022

33、1171.2422806.0909144.1785025+combined |240.1966929.0155936.2415752.1659744.2274113+diff |.1239689 .0302019.0644716.1834661Degrees of freedom: 238Ha: diff < 0t = 4.1047P < t = 1.0000Ha: diff = 0t = 4.1047P > |t| = 0.0001Ha: diff > 0t = 4.1047P > t = 0.0000Ho: mean(Treatmen) - mean(Plac

34、ebo) = diff = 0-> ttest pmdns4, by(Group)Two-sample t test with equal variancesGroup | Obs+Treatmen |119.3505427.0222077.2422572.3065655.39452Placebo |118.1680638.0228772.2485104.1227567.213371+combined |237.2596883.0169786.261383.2262392.2931373diff |.1824789 .0318799.1196719.2452859Mean Std. Er

35、r. Std. Dev. 95% Conf. IntervalDegrees of freedom: 235Ho: mean(Treatmen) - mean(Placebo) = diff = 0Ha: diff < 0Ha: diff = 0Ha: diff > 0t = 5.7239t = 5.7239t = 5.7239P < t = 1.0000P > |t| = 0.0000P > t = 0.0000-> ttest pmdns5, by(Group)Two-sample t test with equal variancesGroup | O

36、bs Mean Std. Err. Std. Dev. 95% Conf. Interval +Treatmen |119.4200117.0227177.2478205 .3750246.4649989Placebo |118.1877292.0239093.2597217.140378.2350803+combined |237.3043605.018106.2787384.2686905.3400305+diff |.2322826 .0329745.1673193.2972459Degrees of freedom: 235Ho: mean(Treatmen) - mean(Place

37、bo) = diff = 0Ha: diff < 0t = 7.0443P < t = 1.0000Ha: diff = 0t = 7.0443P > |t| = 0.0000Ha: diff > 0t = 7.0443P > t = 0.0000-> ttest pmdns6, by(Group)Two-sample t test with equal variancesGroup | Obs Mean Std. Err. Std. Dev. 95% Conf. Interval +Treatmen |119.4998484.0228229.2489689

38、.4546528.545044Placebo |118.2154119.0231653.2516401.1695342.2612897+combined |237.3582302.0186798.2875711.3214299.3950306+diff |.2844365 .0325181.2203724.3485007Degrees of freedom: 235Ho: mean(Treatmen) - mean(Placebo) = diff = 0Ha: diff < 0Ha: diff = 0Ha: diff > 0t = 8.7470t = 8.7470t = 8.747

39、0P < t = 1.0000P > |t| = 0.0000P > t = 0.0000./*产生疗效等级变量 */.for num 1/ 6 : gen lxX=recode(pmdnsX,0.1599,0.4999,0.7499,1)-> gen lx1=recode(pmdns1,0.1599,0.4999,0.7499,1)-> gen lx2=recode(pmdns2,0.1599,0.4999,0.7499,1)-> gen lx3=recode(pmdns3,0.1599,0.4999,0.7499,1)-> gen lx4=reco

40、de(pmdns4,0.1599,0.4999,0.7499,1)(3 missing values generated)-> gen lx5=recode(pmdns5,0.1599,0.4999,0.7499,1)(3 missing values generated)-> gen lx6=recode(pmdns6,0.1599,0.4999,0.7499,1)(3 missing values generated)./* 1表示<0.16 2表示 0.16 表示0.500 4表示0.751 */.for num 1/6 : recode lxX 0.1599=1 0.

41、4999=2 0.7499=3 1=4-> recode lx1 0.1599=1 0.4999=2 0.7499=3 1=4(240 changes made)-> recode lx2 0.1599=1 0.4999=2 0.7499=3 1=4(240 changes made)-> recode lx3 0.1599=1 0.4999=2 0.7499=3 1=4(240 changes made)-> recode lx4 0.1599=1 0.4999=2 0.7499=3 1=4(237 changes made)-> recode lx5 0.15

42、99=1 0.4999=2 0.7499=3 1=4(237 changes made)-> recode lx6 0.1599=1 0.4999=2 0.7499=3 1=4(237 changes made)./*变量名定义 */.for num 1/6 : label var lxX "第 X 次随访疗效等级”-> label var lx1 '”第1次随访疗效等级"'-> label var lx2 '"第2次随访疗效等级"'-> label var lx3 '"第3次随访

43、疗效等级"'-> label var lx4 '"第4次随访疗效等级"'-> label var lx5 '"第5次随访疗效等级"'-> label var lx6 '"第6次随访疗效等级"'./*定义疗效等级变量的数值标签*/.lab define lxlab 1 "无效"2 "好转”3 "显效”4 ”近愈”. for num 1/6 : label value lxX lxlab-> label val

44、ue lx1 lxlab-> label value lx2 lxlab-> label value lx3 Ixlab-> label value lx4 lxlab-> label value lx5 lxlab-> label value lx6 lxlab./* 6次随访各组的疗效*/.for num 1/6 : tab Group lxX-> tab Group lx1| 第1次随访疗效等级分组变量| 无效 好转 显效| Total+Treatment |78402 |120Placebo |99210 |120+Total |177612 |24

45、0-> tab Group lx2Total| 第2次随访疗效等级 分组变量| 无效 好转 显效 近愈| +Treatment |5750121 |120Placebo |823440 |120+Total |13984161 |240-> tab Group lx3| 第3次随访疗效等级分组变量| 无效 好转 显效 近愈| Total+Treatment |4063170 |120Placebo |724233 |120+Total |112105203 |240-> tab Group lx4| 第4次随访疗效等级分组变量| 无效 好转 显效 近愈| Total+-+Tr

46、eatment |2459333 |119Placebo |+615052 |+118Total |85109385 |237-> tab Group lx5| 第5次随访疗效等级分组变量|无效好转显效近愈|Total+Treatment |1949447 |119Placebo |6044122 |118+Total |+2377993569 |-> tab Group lx6|第6次随访疗效等级分组变量| 无效好转显效J.J.近愈|Treatment |Placebo | +145329486313 |143 |+119118TotalTotal |67777716 |237.

47、/*降为4.0版本*/.version 4.0./*对两组疗效等级进行 Wilcoxon检验*/. wilcoxon lx1 , by(Group)Test: Group=2 has longer survival timeWilcoxon-Gehan statistic =-2562z =-3.12Pr>|z| =0.0018.wilcoxon lx2 , by(Group)Test: Group=2 has longer survival time Wilcoxon-Gehan statistic =-3246z =-3.46Pr>|z| =0.0005.wilcoxon lx

48、3 , by(Group)Test: Group=2 has longer survival time Wilcoxon-Gehan statistic =-4125z =-4.25Pr>|z| =0.0000.wilcoxon lx4 , by(Group)Test: Group=1 has longer survival time Wilcoxon-Gehan statistic =5763z =5.91Pr>|z| =0.0000. wilcoxon lx5 , by(Group)Test: Group=2 has longer survival time Wilcoxon-

49、Gehan statistic =-6452z =-6.48Pr>|z| =0.0000.wilcoxon lx6 , by(Group)Test: Group=1 has longer survival time Wilcoxon-Gehan statistic =7803z =7.76Pr>|z| =0.0000从上面的分析可见，MDNS的疗效等级，从第1次随访开始，两组即具有统计学差异。NGF组优于安慰剂对照组。./*回到7.0版本*/.version 7.0./*定义疗效变量 */. .for num 1/6 : recode lxX 1 2 =13 4 =2-> r

50、ecode lx1 1 2 =1 3 4 =2 (63 changes made)-> recode lx2 1 2 =1 3 4 =2(101 changes made) -> recode lx3 1 2 =1 3 4 =2 (128 changes made) -> recode lx4 1 2 =1 3 4 =2 (152 changes made)-> recode lx5 1 2 =1 3 4 =2(158 changes made)-> recode lx6 1 2 =1 3 4 =2(170 changes made).for num 1/6 :

51、label var lxX ”第 X 次随访疗效”-> label var lx1 '"第1次随访疗效"'-> label var lx2 '”第2次随访疗效”-> label var lx3 '"第3次随访疗效”-> label var lx4 '"第4次随访疗效"'-> label var lx5 '"第5次随访疗效"'-> label var lx6 '"第6次随访疗效"'./*定义疗

52、效变量数据标签*/.lab define lxlab0 1 "无效”2 ”有效.for num 1/6 : label value lxX lxlab0-> label value lx1 lxlab0-> label value lx2 lxlab0-> label value lx3 lxlab0-> label value lx4 lxlab0-> label value lx5 lxlab0-> label value lx6 lxlab0./*各组有效率比较*/.for num 1/6 : tab Group IxX , exact row -> tab Group lx1 , exact row| 第1次随访疗效分组变量| 无效 有效| Total+Treatment |1182 |120|98.331.67 |100.00+Placebo |1200 |120|100.000.00 |100.00+Total |2382 |240|99.170.83 |100.00Fisher'