DBP7 AG/G vs A
Dihua Xu
December 24, 2015
data source: “ArMorr DBP DNA 12 23 2015.xlsx”
Original Data
Baseline information
## Variable DBP7.A DBP7.AG.G P.Value
## 1 Age 64.5 + 14.9 (1065) 61.2 + 15.7 (1210) <.001
## 2 Sex 613 / 1065 (57.6 %) 627 / 1210 (51.8 %) 0.007
## 3 Race2 920 / 1065 (86.4 %) 441 / 1210 (36.4 %) <.001
## 4 Ethnic 151 / 1065 (14.2 %) 136 / 1210 (11.2 %) 0.041
## 5 diabetes 481 / 1058 (45.5 %) 521 / 1207 (43.2 %) 0.291
## 6 catheter 666 / 985 (67.6 %) 717 / 1113 (64.4 %) 0.135
## 7 BMI 27.3 + 7.5 (1064) 28.1 + 8.2 (1210) 0.026
## 8 sbp14 141.1 + 22.4 (1052) 145.8 + 22.6 (1205) <.001
## 9 dbp14 71.6 + 13.1 (1052) 75.2 + 13.2 (1205) <.001
## 10 ca14 8.5 + 0.8 (1041) 8.4 + 0.9 (1195) 0.224
## 11 cr14 5.8 + 2.4 (1019) 6.8 + 3.0 (1179) <.001
## 12 alk14 98.5 + 73.6 (978) 98.5 + 64.2 (1128) 0.988
## 13 alb14 3.5 + 0.5 (1049) 3.5 + 0.5 (1202) 0.076
## 14 pth14 266.1 + 265.6 (822) 314.8 + 259.5 (949) <.001
## 15 phos14 4.7 + 1.6 (1045) 4.7 + 1.6 (1192) 0.879
## 16 wbc14 8.8 + 3.2 (1018) 8.4 + 3.0 (1179) 0.003
## 17 ferr14 300.5 + 369.2 (972) 325.0 + 344.0 (1130) 0.116
## 18 Ivvitd 684 / 1065 (64.2 %) 881 / 1210 (72.8 %) <.001Kaplan Meier Curve
km <- survfit(Surv(fu, Death)~group, data=dat)
ggkmTable(km, ystratalabs=c("A", "AG/G"), timeby=50, main="DBP 7: A vs. AG/G")
Cox model
cox.fit <- coxph(Surv(fu, Death)~group+Age+Race2+diabetes+pth14+ca14+phos14+alb14+catheter+Ivvitd, dat)
summary(cox.fit)## Call:
## coxph(formula = Surv(fu, Death) ~ group + Age + Race2 + diabetes +
## pth14 + ca14 + phos14 + alb14 + catheter + Ivvitd, data = dat)
##
## n= 1602, number of events= 224
## (691 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## group0 -0.2036363 0.8157590 0.1515646 -1.344 0.179
## Age 0.0429902 1.0439277 0.0054199 7.932 2.11e-15 ***
## Race2NW 0.0141941 1.0142954 0.1679337 0.085 0.933
## diabetes0 0.0131327 1.0132194 0.1371148 0.096 0.924
## pth14 0.0002818 1.0002819 0.0002788 1.011 0.312
## ca14 0.1450807 1.1561328 0.0938318 1.546 0.122
## phos14 0.0309412 1.0314249 0.0455985 0.679 0.497
## alb14 -0.8124774 0.4437573 0.1465972 -5.542 2.99e-08 ***
## catheter0 -0.2735063 0.7607075 0.1498499 -1.825 0.068 .
## Ivvitd0 -0.0230802 0.9771842 0.1606759 -0.144 0.886
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## group0 0.8158 1.2259 0.6061 1.0979
## Age 1.0439 0.9579 1.0329 1.0551
## Race2NW 1.0143 0.9859 0.7298 1.4096
## diabetes0 1.0132 0.9870 0.7744 1.3256
## pth14 1.0003 0.9997 0.9997 1.0008
## ca14 1.1561 0.8650 0.9619 1.3896
## phos14 1.0314 0.9695 0.9432 1.1278
## alb14 0.4438 2.2535 0.3329 0.5915
## catheter0 0.7607 1.3146 0.5671 1.0204
## Ivvitd0 0.9772 1.0233 0.7132 1.3389
##
## Concordance= 0.706 (se = 0.019 )
## Rsquare= 0.069 (max possible= 0.87 )
## Likelihood ratio test= 115.3 on 10 df, p=0
## Wald test = 108.4 on 10 df, p=0
## Score (logrank) test = 109.8 on 10 df, p=0logit.fit <- glm(Death~group+Age+Race2+diabetes+pth14+ca14+phos14+alb14+catheter+Ivvitd,
dat, family="binomial")
summary(logit.fit)##
## Call:
## glm(formula = Death ~ group + Age + Race2 + diabetes + pth14 +
## ca14 + phos14 + alb14 + catheter + Ivvitd, family = "binomial",
## data = dat)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.3787 -0.5908 -0.4408 -0.2913 2.7046
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.1907759 0.9330944 -3.420 0.000627 ***
## group0 -0.2543495 0.1698599 -1.497 0.134287
## Age 0.0477245 0.0060632 7.871 3.51e-15 ***
## Race2NW 0.0177572 0.1863729 0.095 0.924094
## diabetes0 0.0203106 0.1534822 0.132 0.894721
## pth14 0.0003216 0.0003264 0.986 0.324376
## ca14 0.1502854 0.1030468 1.458 0.144725
## phos14 0.0356131 0.0512432 0.695 0.487067
## alb14 -0.9109368 0.1679169 -5.425 5.80e-08 ***
## catheter0 -0.3023627 0.1646709 -1.836 0.066334 .
## Ivvitd0 -0.0162697 0.1803800 -0.090 0.928131
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1296.5 on 1601 degrees of freedom
## Residual deviance: 1182.2 on 1591 degrees of freedom
## (691 observations deleted due to missingness)
## AIC: 1204.2
##
## Number of Fisher Scoring iterations: 5Matched Sample
m.out <- matchit(group~Age, data=na.exclude(subset(dat, select=c("Age","group", "MRN"))), method="nearest")
m.data1 <- match.data(m.out)
dat.m <- subset(dat, MRN %in% m.data1$MRN)Baseline information: matched sample
## Variable DBP7.A DBP7.AG.G P.Value
## 1 Age 64.5 + 14.9 (1065) 63.8 + 14.5 (1066) 0.238
## 2 Sex 613 / 1065 (57.6 %) 549 / 1066 (51.5 %) 0.006
## 3 Race2 920 / 1065 (86.4 %) 400 / 1066 (37.5 %) <.001
## 4 Ethnic 151 / 1065 (14.2 %) 118 / 1066 (11.1 %) 0.036
## 5 diabetes 481 / 1058 (45.5 %) 473 / 1063 (44.5 %) 0.686
## 6 catheter 666 / 985 (67.6 %) 626 / 978 (64.0 %) 0.102
## 7 BMI 27.3 + 7.5 (1064) 27.9 + 8.0 (1066) 0.107
## 8 sbp14 141.1 + 22.4 (1052) 145.5 + 23.0 (1061) <.001
## 9 dbp14 71.6 + 13.1 (1052) 74.2 + 13.2 (1061) <.001
## 10 ca14 8.5 + 0.8 (1041) 8.4 + 0.9 (1054) 0.351
## 11 cr14 5.8 + 2.4 (1019) 6.5 + 2.8 (1038) <.001
## 12 alk14 98.5 + 73.6 (978) 97.6 + 63.0 (1000) 0.778
## 13 alb14 3.5 + 0.5 (1049) 3.5 + 0.5 (1058) 0.117
## 14 pth14 266.1 + 265.6 (822) 308.2 + 255.6 (830) 0.001
## 15 phos14 4.7 + 1.6 (1045) 4.7 + 1.5 (1050) 0.313
## 16 wbc14 8.8 + 3.2 (1018) 8.4 + 3.0 (1038) 0.005
## 17 ferr14 300.5 + 369.2 (972) 326.6 + 342.4 (998) 0.104
## 18 Ivvitd 684 / 1065 (64.2 %) 773 / 1066 (72.5 %) <.001Kaplan Meier Curve: matched sample
km.m <- survfit(Surv(fu, Death)~group, data=dat.m)
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.m)
summary(cox.match.fit)## Call:
## coxph(formula = Surv(fu, Death) ~ group + Age + Race2 + diabetes +
## pth14 + ca14 + phos14 + alb14 + catheter + Ivvitd, data = dat.m)
##
## n= 1496, number of events= 218
## (635 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## group0 -0.2097677 0.8107725 0.1540250 -1.362 0.1732
## Age 0.0428529 1.0437844 0.0057374 7.469 8.07e-14 ***
## Race2NW 0.0137935 1.0138890 0.1708248 0.081 0.9356
## diabetes0 0.0209467 1.0211676 0.1394983 0.150 0.8806
## pth14 0.0003182 1.0003182 0.0002795 1.138 0.2549
## ca14 0.1515415 1.1636266 0.0953977 1.589 0.1122
## phos14 0.0125871 1.0126667 0.0470591 0.267 0.7891
## alb14 -0.7873122 0.4550663 0.1491910 -5.277 1.31e-07 ***
## catheter0 -0.2641410 0.7678653 0.1515529 -1.743 0.0814 .
## Ivvitd0 -0.0343162 0.9662660 0.1627594 -0.211 0.8330
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## group0 0.8108 1.2334 0.5995 1.0965
## Age 1.0438 0.9581 1.0321 1.0556
## Race2NW 1.0139 0.9863 0.7254 1.4171
## diabetes0 1.0212 0.9793 0.7769 1.3423
## pth14 1.0003 0.9997 0.9998 1.0009
## ca14 1.1636 0.8594 0.9652 1.4029
## phos14 1.0127 0.9875 0.9234 1.1105
## alb14 0.4551 2.1975 0.3397 0.6096
## catheter0 0.7679 1.3023 0.5705 1.0334
## Ivvitd0 0.9663 1.0349 0.7024 1.3293
##
## Concordance= 0.698 (se = 0.02 )
## Rsquare= 0.066 (max possible= 0.879 )
## Likelihood ratio test= 102.8 on 10 df, p=0
## Wald test = 97.17 on 10 df, p=2.22e-16
## Score (logrank) test = 98.23 on 10 df, p=1.11e-16logit.match.fit <- glm(Death~group+Age+Race2+diabetes+pth14+ca14+phos14+alb14+catheter+Ivvitd,
dat.m, family="binomial")
summary(logit.match.fit)##
## Call:
## glm(formula = Death ~ group + Age + Race2 + diabetes + pth14 +
## ca14 + phos14 + alb14 + catheter + Ivvitd, family = "binomial",
## data = dat.m)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.3370 -0.6036 -0.4538 -0.3090 2.7234
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.2811849 0.9594454 -3.420 0.000626 ***
## group0 -0.2650137 0.1727966 -1.534 0.125110
## Age 0.0478088 0.0064154 7.452 9.18e-14 ***
## Race2NW 0.0205942 0.1897542 0.109 0.913575
## diabetes0 0.0293598 0.1562082 0.188 0.850914
## pth14 0.0003693 0.0003301 1.119 0.263308
## ca14 0.1579803 0.1049764 1.505 0.132346
## phos14 0.0131283 0.0528068 0.249 0.803662
## alb14 -0.8793534 0.1707552 -5.150 2.61e-07 ***
## catheter0 -0.2918558 0.1668021 -1.750 0.080168 .
## Ivvitd0 -0.0324433 0.1829553 -0.177 0.859250
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1242.3 on 1495 degrees of freedom
## Residual deviance: 1140.3 on 1485 degrees of freedom
## (635 observations deleted due to missingness)
## AIC: 1162.3
##
## Number of Fisher Scoring iterations: 5Limit Age under 80
dat.age <- subset(dat, Age <= 80 & !is.na(Age))Baseline information: Age <= 80
## Variable DBP7.A DBP7.AG.G P.Value
## 1 Age 64.5 + 14.9 (1065) 63.8 + 14.5 (1066) 0.238
## 2 Sex 613 / 1065 (57.6 %) 549 / 1066 (51.5 %) 0.006
## 3 Race2 920 / 1065 (86.4 %) 400 / 1066 (37.5 %) <.001
## 4 Ethnic 151 / 1065 (14.2 %) 118 / 1066 (11.1 %) 0.036
## 5 diabetes 481 / 1058 (45.5 %) 473 / 1063 (44.5 %) 0.686
## 6 catheter 666 / 985 (67.6 %) 626 / 978 (64.0 %) 0.102
## 7 BMI 27.3 + 7.5 (1064) 27.9 + 8.0 (1066) 0.107
## 8 sbp14 141.1 + 22.4 (1052) 145.5 + 23.0 (1061) <.001
## 9 dbp14 71.6 + 13.1 (1052) 74.2 + 13.2 (1061) <.001
## 10 ca14 8.5 + 0.8 (1041) 8.4 + 0.9 (1054) 0.351
## 11 cr14 5.8 + 2.4 (1019) 6.5 + 2.8 (1038) <.001
## 12 alk14 98.5 + 73.6 (978) 97.6 + 63.0 (1000) 0.778
## 13 alb14 3.5 + 0.5 (1049) 3.5 + 0.5 (1058) 0.117
## 14 pth14 266.1 + 265.6 (822) 308.2 + 255.6 (830) 0.001
## 15 phos14 4.7 + 1.6 (1045) 4.7 + 1.5 (1050) 0.313
## 16 wbc14 8.8 + 3.2 (1018) 8.4 + 3.0 (1038) 0.005
## 17 ferr14 300.5 + 369.2 (972) 326.6 + 342.4 (998) 0.104
## 18 Ivvitd 684 / 1065 (64.2 %) 773 / 1066 (72.5 %) <.001Kaplan Meier Curve: Age <= 80
km.age <- survfit(Surv(fu, Death)~group, data=dat.age)
ggkmTable(km.age, ystratalabs=c("A", "AG/G"), timeby=50, main="DBP 7: A vs. AG/G")
Cox model: Age <= 80
cox.age.fit <- coxph(Surv(fu, Death)~group+Age+Race2+diabetes+pth14+ca14+phos14+alb14+catheter+Ivvitd, dat.age)
summary(cox.age.fit)## Call:
## coxph(formula = Surv(fu, Death) ~ group + Age + Race2 + diabetes +
## pth14 + ca14 + phos14 + alb14 + catheter + Ivvitd, data = dat.age)
##
## n= 1387, number of events= 164
## (589 observations deleted due to missingness)
##
## coef exp(coef) se(coef) z Pr(>|z|)
## group0 -0.3696558 0.6909721 0.1834042 -2.016 0.0438 *
## Age 0.0450304 1.0460596 0.0071889 6.264 3.76e-10 ***
## Race2NW 0.0991971 1.1042840 0.1949591 0.509 0.6109
## diabetes0 0.1867077 1.2052750 0.1591426 1.173 0.2407
## pth14 0.0003911 1.0003912 0.0003118 1.254 0.2097
## ca14 0.2119637 1.2361030 0.1049105 2.020 0.0433 *
## phos14 0.0710947 1.0736829 0.0538328 1.321 0.1866
## alb14 -1.0444074 0.3519003 0.1708378 -6.113 9.75e-10 ***
## catheter0 -0.2738454 0.7604497 0.1736344 -1.577 0.1148
## Ivvitd0 0.0301015 1.0305591 0.1880014 0.160 0.8728
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## exp(coef) exp(-coef) lower .95 upper .95
## group0 0.6910 1.4472 0.4823 0.9899
## Age 1.0461 0.9560 1.0314 1.0609
## Race2NW 1.1043 0.9056 0.7536 1.6182
## diabetes0 1.2053 0.8297 0.8823 1.6465
## pth14 1.0004 0.9996 0.9998 1.0010
## ca14 1.2361 0.8090 1.0064 1.5183
## phos14 1.0737 0.9314 0.9662 1.1932
## alb14 0.3519 2.8417 0.2518 0.4919
## catheter0 0.7604 1.3150 0.5411 1.0687
## Ivvitd0 1.0306 0.9703 0.7129 1.4897
##
## Concordance= 0.7 (se = 0.023 )
## Rsquare= 0.06 (max possible= 0.817 )
## Likelihood ratio test= 85.23 on 10 df, p=4.696e-14
## Wald test = 79.1 on 10 df, p=7.526e-13
## Score (logrank) test = 80.27 on 10 df, p=4.448e-13logit.age.fit <- glm(Death~group+Age+Race2+diabetes+pth14+ca14+phos14+alb14+catheter+Ivvitd,
dat.age, family="binomial")
summary(logit.age.fit)##
## Call:
## glm(formula = Death ~ group + Age + Race2 + diabetes + pth14 +
## ca14 + phos14 + alb14 + catheter + Ivvitd, family = "binomial",
## data = dat.age)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.3192 -0.5503 -0.4152 -0.2763 2.7769
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.4069575 1.0543858 -3.231 0.00123 **
## group0 -0.4258677 0.2014205 -2.114 0.03449 *
## Age 0.0481956 0.0078024 6.177 6.53e-10 ***
## Race2NW 0.1105601 0.2133601 0.518 0.60433
## diabetes0 0.1758694 0.1752935 1.003 0.31572
## pth14 0.0004620 0.0003619 1.277 0.20178
## ca14 0.2207920 0.1152208 1.916 0.05533 .
## phos14 0.0684467 0.0577018 1.186 0.23554
## alb14 -1.1112122 0.1915276 -5.802 6.56e-09 ***
## catheter0 -0.2845288 0.1881925 -1.512 0.13056
## Ivvitd0 0.0461366 0.2067001 0.223 0.82338
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 1008.09 on 1386 degrees of freedom
## Residual deviance: 925.26 on 1376 degrees of freedom
## (589 observations deleted due to missingness)
## AIC: 947.26
##
## Number of Fisher Scoring iterations: 5