DBP7 AG/G vs A
Dihua Xu
December 24, 2015
data source: “ArMorr DBP DNA 12 23 2015.xlsx”
Matched Sample
matched on age (+/- 2 years), albumin (+/- 0.2 mg/dl), catheter status, and diabetes status
dat.group1 <- subset(dat2, group==1)
dat.group0 <- subset(dat2, group==0)
dat.match.id <- numeric(0)
for(i in 1:nrow(dat.group1)){
temp <- subset(dat.group0, catheter == dat.group1$catheter[i] &
diabetes == dat.group1$diabetes[i] &
Age <= dat.group1$Age[i] + 1 &
Age >= dat.group1$Age[i] - 1 &
alb14 <= dat.group1$alb14[i] + .1 &
alb14 >= dat.group1$alb14[i] - .1
)
if(nrow(temp) != 0){
dat.match.id <- append(dat.match.id, c(dat.group1$MRN[i], temp$MRN))
}
}
dat.match <- subset(dat2, MRN %in% unique(dat.match.id))Baseline information: matched sample
## Variable DBP7.A DBP7.AG.G P.Value
## 1 Age 65.2 + 13.5 (684) 64.2 + 13.6 (760) 0.142
## 2 Sex 398 / 684 (58.2 %) 392 / 760 (51.6 %) 0.014
## 3 Race2 594 / 684 (86.8 %) 291 / 760 (38.3 %) <.001
## 4 Ethnic 101 / 684 (14.8 %) 95 / 760 (12.5 %) 0.239
## 5 diabetes 306 / 684 (44.7 %) 353 / 760 (46.4 %) 0.549
## 6 catheter 493 / 684 (72.1 %) 536 / 760 (70.5 %) 0.554
## 7 BMI 27.7 + 7.5 (683) 28.1 + 8.4 (760) 0.301
## 8 sbp14 140.7 + 21.9 (684) 145.0 + 22.8 (760) <.001
## 9 dbp14 71.2 + 12.8 (684) 73.9 + 12.8 (760) <.001
## 10 ca14 8.5 + 0.8 (680) 8.5 + 0.8 (755) 0.663
## 11 cr14 5.8 + 2.4 (661) 6.6 + 2.8 (746) <.001
## 12 alk14 98.6 + 67.7 (641) 97.8 + 61.4 (713) 0.815
## 13 alb14 3.5 + 0.4 (684) 3.5 + 0.4 (760) 0.17
## 14 pth14 264.6 + 239.7 (533) 315.1 + 245.1 (585) <.001
## 15 phos14 4.7 + 1.6 (681) 4.7 + 1.5 (752) 0.501
## 16 wbc14 9.0 + 3.2 (664) 8.5 + 3.0 (742) 0.003
## 17 ferr14 300.9 + 353.1 (641) 312.2 + 314.7 (709) 0.535
## 18 Ivvitd 449 / 684 (65.6 %) 559 / 760 (73.6 %) 0.001Kaplan Meier Curve: matched sample
km.m <- survfit(Surv(fu, Death)~group, data=dat.match)
ggkmTable(km.m, ystratalabs=c("A", "AG/G"), timeby=50, main="DBP 7: A vs. AG/G")
Cox model: matched sample
cox.match.fit <- coxph(Surv(fu, Death)~group+Age+Race2+diabetes+pth14+
ca14+phos14+alb14+catheter+Ivvitd, dat.match)
summary(cox.match.fit)## Call:
## coxph(formula = Surv(fu, Death) ~ group + Age + Race2 + diabetes +
## pth14 + ca14 + phos14 + alb14 + catheter + Ivvitd, data = dat.match)
##
## n= 1109, number of events= 154
## (335 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## group0 -0.3755543 0.6869084 0.1859876 -2.019 0.0435 *
## Age 0.0482319 1.0494140 0.0075437 6.394 1.62e-10 ***
## Race2NW 0.0753797 1.0782935 0.2012293 0.375 0.7080
## diabetes0 0.0490441 1.0502667 0.1690487 0.290 0.7717
## pth14 0.0006003 1.0006005 0.0003099 1.937 0.0528 .
## ca14 0.2564089 1.2922810 0.1152583 2.225 0.0261 *
## phos14 0.0673271 1.0696453 0.0574055 1.173 0.2409
## alb14 -1.1051179 0.3311718 0.2316147 -4.771 1.83e-06 ***
## catheter0 -0.2248141 0.7986647 0.1876544 -1.198 0.2309
## Ivvitd0 -0.1561226 0.8554543 0.1983388 -0.787 0.4312
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## group0 0.6869 1.4558 0.4771 0.9890
## Age 1.0494 0.9529 1.0340 1.0650
## Race2NW 1.0783 0.9274 0.7269 1.5996
## diabetes0 1.0503 0.9521 0.7541 1.4628
## pth14 1.0006 0.9994 1.0000 1.0012
## ca14 1.2923 0.7738 1.0310 1.6198
## phos14 1.0696 0.9349 0.9558 1.1970
## alb14 0.3312 3.0196 0.2103 0.5214
## catheter0 0.7987 1.2521 0.5529 1.1537
## Ivvitd0 0.8555 1.1690 0.5799 1.2619
##
## Concordance= 0.692 (se = 0.023 )
## Rsquare= 0.064 (max possible= 0.854 )
## Likelihood ratio test= 73.28 on 10 df, p=1.025e-11
## Wald test = 67.4 on 10 df, p=1.402e-10
## Score (logrank) test = 69.61 on 10 df, p=5.275e-11Logistic model: matched sample
logit.match.fit <- glm(Death~group+Age+Race2+diabetes+pth14+ca14+phos14+alb14+catheter+Ivvitd,
dat.match, family="binomial")
summary(logit.match.fit)##
## Call:
## glm(formula = Death ~ group + Age + Race2 + diabetes + pth14 +
## ca14 + phos14 + alb14 + catheter + Ivvitd, family = "binomial",
## data = dat.match)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.2630 -0.5945 -0.4448 -0.3109 2.6766
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.9827697 1.2780070 -3.116 0.00183 **
## group0 -0.4261659 0.2060856 -2.068 0.03865 *
## Age 0.0534824 0.0083848 6.378 1.79e-10 ***
## Race2NW 0.1091614 0.2229966 0.490 0.62447
## diabetes0 0.0422514 0.1878069 0.225 0.82200
## pth14 0.0007587 0.0003848 1.972 0.04864 *
## ca14 0.2763062 0.1285721 2.149 0.03163 *
## phos14 0.0694453 0.0626583 1.108 0.26773
## alb14 -1.1880181 0.2589360 -4.588 4.47e-06 ***
## catheter0 -0.2322685 0.2063424 -1.126 0.26032
## Ivvitd0 -0.1382625 0.2220417 -0.623 0.53349
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 893.62 on 1108 degrees of freedom
## Residual deviance: 821.00 on 1098 degrees of freedom
## (335 observations deleted due to missingness)
## AIC: 843
##
## Number of Fisher Scoring iterations: 5