DCI analysis v1.3
Dihua Xu
December 18, 2015
PD Patients only
n=126 DCI identified calciphylaxis patients with available onset date, correct death date, and were on PD at the time of onset date.
dat.pd <- subset(dat, dm == "PD")Baseline information
## Variable Treatment Control P.Value
## 1 sex 15 / 38 (39.5 %) 13 / 49 (26.5 %) 0.294
## 2 race 25 / 37 (67.6 %) 33 / 49 (67.3 %) >.999
## 3 ethnicity 5 / 36 (13.9 %) 3 / 49 (6.1 %) 0.403
## 4 age 54.5 + 14.6 (38) 55.1 + 13.7 (49) 0.855
## 5 diabetes 18 / 38 (47.4 %) 26 / 49 (53.1 %) 0.756
## 6 bmi 29.9 + 7.9 (38) 32.5 + 9.2 (49) 0.178
## 7 calcium 8.5 + 0.9 (38) 8.8 + 0.9 (46) 0.264
## 8 phosphate 6.0 + 1.6 (38) 6.7 + 2.2 (46) 0.087
## 9 pth 481.4 + 327.5 (35) 431.7 + 267.7 (42) 0.466
## 10 alkphos 140.9 + 55.4 (38) 175.7 + 114.1 (46) 0.09
## 11 albumin 2.8 + 0.6 (38) 3.0 + 0.6 (46) 0.145
## 12 warfarin 5 / 38 (13.2 %) 1 / 49 (2.0 %) 0.109
## 13 cinacalcet 13 / 38 (34.2 %) 23 / 49 (46.9 %) 0.329
## 14 vd 18 / 38 (47.4 %) 14 / 49 (28.6 %) 0.114Kaplan Meier Curve: all time + PD
km.pd <- survfit(Surv(len_fu2, death)~trt, data=dat.pd)
plot(km.pd, col=c("blue", "red"))
legend("topright", c("STS", "non-STS"), lty=1, col=c("red", "blue"))
km.pd## Call: survfit(formula = Surv(len_fu2, death) ~ trt, data = dat.pd)
##
## n events median 0.95LCL 0.95UCL
## trt=1 38 21 277 180 NA
## trt=0 49 36 271 69 993Kaplan Meier Curve: 1 yr follow up + PD
km.pd.1y <- survfit(Surv(len_fu.1y, death.1y)~trt, data=dat.pd)
plot(km.pd.1y, col=c("blue", "red"))
legend("topright", c("STS", "non-STS"), lty=1, col=c("red", "blue"))
km.pd.1y## Call: survfit(formula = Surv(len_fu.1y, death.1y) ~ trt, data = dat.pd)
##
## n events median 0.95LCL 0.95UCL
## trt=1 38 18 277 180 NA
## trt=0 49 24 271 69 NAKaplan Meier Curve: 2 yrs follow up + PD
km.pd.2y <- survfit(Surv(len_fu.2y, death.2y)~trt, data=dat.pd)
plot(km.pd.2y, col=c("blue", "red"))
legend("topright", c("STS", "non-STS"), lty=1, col=c("red", "blue"))
km.pd.2y## Call: survfit(formula = Surv(len_fu.2y, death.2y) ~ trt, data = dat.pd)
##
## n events median 0.95LCL 0.95UCL
## trt=1 38 20 277 180 NA
## trt=0 49 28 271 69 NAHD patient only
n=324 DCI identified calciphylaxis patients with available onset date, correct death date, and were on PD at the time of onset date.
dat.hd <- subset(dat, dm == "HD")Baseline information
## Variable Treatment Control P.Value
## 1 sex 70 / 213 (32.9 %) 50 / 150 (33.3 %) >.999
## 2 race 133 / 208 (63.9 %) 84 / 149 (56.4 %) 0.182
## 3 ethnicity 21 / 206 (10.2 %) 4 / 149 (2.7 %) 0.012
## 4 age 59.3 + 12.4 (213) 54.7 + 12.7 (150) <.001
## 5 diabetes 139 / 213 (65.3 %) 85 / 150 (56.7 %) 0.121
## 6 bmi 32.6 + 8.8 (213) 31.8 + 8.8 (150) 0.428
## 7 calcium 8.9 + 0.8 (197) 8.9 + 1.1 (130) 0.625
## 8 phosphate 6.1 + 2.0 (196) 6.5 + 2.1 (130) 0.094
## 9 pth 524.7 + 508.4 (188) 595.7 + 686.6 (120) 0.299
## 10 alkphos 180.4 + 143.9 (197) 224.9 + 250.5 (129) 0.043
## 11 albumin 3.5 + 0.6 (197) 3.5 + 0.5 (129) 0.443
## 12 warfarin 11 / 213 (5.2 %) 4 / 150 (2.7 %) 0.363
## 13 cinacalcet 97 / 213 (45.5 %) 37 / 150 (24.7 %) <.001
## 14 vd 117 / 213 (54.9 %) 78 / 150 (52.0 %) 0.657Kaplan Meier Curve: all time + HD
km.hd <- survfit(Surv(len_fu2, death)~trt, data=dat.hd)
plot(km.hd, col=c("blue", "red"))
legend("topright", c("STS", "non-STS"), lty=1, col=c("red", "blue"))
km.hd## Call: survfit(formula = Surv(len_fu2, death) ~ trt, data = dat.hd)
##
## n events median 0.95LCL 0.95UCL
## trt=1 213 116 523 385 879
## trt=0 150 95 852 584 1268Kaplan Meier Curve: 1 yr follow up + HD
km.hd.1y <- survfit(Surv(len_fu.1y, death.1y)~trt, data=dat.hd)
plot(km.hd.1y, col=c("blue", "red"))
legend("topright", c("STS", "non-STS"), lty=1, col=c("red", "blue"))
km.hd.1y## Call: survfit(formula = Surv(len_fu.1y, death.1y) ~ trt, data = dat.hd)
##
## n events median 0.95LCL 0.95UCL
## trt=1 213 78 NA NA NA
## trt=0 150 50 NA NA NAKaplan Meier Curve: 2 yrs follow up + HD
km.hd.2y <- survfit(Surv(len_fu.2y, death.2y)~trt, data=dat.hd)
plot(km.hd.2y, col=c("blue", "red"))
legend("topright", c("STS", "non-STS"), lty=1, col=c("red", "blue"))
km.hd.2y## Call: survfit(formula = Surv(len_fu.2y, death.2y) ~ trt, data = dat.hd)
##
## n events median 0.95LCL 0.95UCL
## trt=1 213 97 523 385 NA
## trt=0 150 65 NA 584 NAHD patient only: matched sample
n=268 DCI identified calciphylaxis patients with available onset date, correct death date, and were on PD at the time of onset date; matched by age and ethnicity.
matchit(trt~age+ethnicity, data=na.exclude(dat.hd[,c(1,9,11, 4)]), method="nearest") -> m.out
m.data1 <- match.data(m.out)
dat.hd.m <- subset(dat.hd, patient_id %in% m.data1$patient_id | is.na(ethnicity))Baseline information
## Variable Treatment Control P.Value
## 1 sex 54 / 156 (34.6 %) 50 / 150 (33.3 %) 0.908
## 2 race 91 / 153 (59.5 %) 84 / 149 (56.4 %) 0.668
## 3 ethnicity 0 / 149 (0.0 %) 4 / 149 (2.7 %) 0.131
## 4 age 55.6 + 10.0 (156) 54.7 + 12.7 (150) 0.486
## 5 diabetes 99 / 156 (63.5 %) 85 / 150 (56.7 %) 0.273
## 6 bmi 33.4 + 9.2 (156) 31.8 + 8.8 (150) 0.118
## 7 calcium 8.9 + 0.9 (144) 8.9 + 1.1 (130) 0.557
## 8 phosphate 6.3 + 2.1 (143) 6.5 + 2.1 (130) 0.344
## 9 pth 509.3 + 470.0 (136) 595.7 + 686.6 (120) 0.236
## 10 alkphos 183.4 + 147.5 (144) 224.9 + 250.5 (129) 0.092
## 11 albumin 3.4 + 0.6 (144) 3.5 + 0.5 (129) 0.099
## 12 warfarin 8 / 156 (5.1 %) 4 / 150 (2.7 %) 0.415
## 13 cinacalcet 68 / 156 (43.6 %) 37 / 150 (24.7 %) <.001
## 14 vd 80 / 156 (51.3 %) 78 / 150 (52.0 %) 0.991Kaplan Meier Curve: all time + HD + matched sample
km.hd.m <- survfit(Surv(len_fu2, death)~trt, data=dat.hd.m)
plot(km.hd.m, col=c("blue", "red"))
legend("topright", c("STS", "non-STS"), lty=1, col=c("red", "blue"))
km.hd.m## Call: survfit(formula = Surv(len_fu2, death) ~ trt, data = dat.hd.m)
##
## n events median 0.95LCL 0.95UCL
## trt=1 156 83 552 369 1226
## trt=0 150 95 852 584 1268Kaplan Meier Curve: 1 yr follow up + HD + matched sample
km.hd.1y.m <- survfit(Surv(len_fu.1y, death.1y)~trt, data=dat.hd.m)
plot(km.hd.1y.m, col=c("blue", "red"))
legend("topright", c("STS", "non-STS"), lty=1, col=c("red", "blue"))
km.hd.1y.m## Call: survfit(formula = Surv(len_fu.1y, death.1y) ~ trt, data = dat.hd.m)
##
## n events median 0.95LCL 0.95UCL
## trt=1 156 58 NA NA NA
## trt=0 150 50 NA NA NAKaplan Meier Curve: 2 yrs follow up + HD + matched sample
km.hd.2y.m <- survfit(Surv(len_fu.2y, death.2y)~trt, data=dat.hd.m)
plot(km.hd.2y.m, col=c("blue", "red"))
legend("topright", c("STS", "non-STS"), lty=1, col=c("red", "blue"))
km.hd.2y.m## Call: survfit(formula = Surv(len_fu.2y, death.2y) ~ trt, data = dat.hd.m)
##
## n events median 0.95LCL 0.95UCL
## trt=1 156 69 552 369 NA
## trt=0 150 65 NA 584 NAPD Patients with Caliphylaxis diagnostic date only
n=101 DCI identified calciphylaxis patients with available onset diagnostic date and were on PD at the time of onset date.
dat.calci.pd <- subset(dat, CalciDiag==1 & dm == "PD")Baseline information
## Variable Treatment Control P.Value
## 1 sex 7 / 24 (29.2 %) 13 / 49 (26.5 %) >.999
## 2 race 15 / 23 (65.2 %) 33 / 49 (67.3 %) >.999
## 3 ethnicity 3 / 22 (13.6 %) 3 / 49 (6.1 %) 0.554
## 4 age 55.0 + 15.9 (24) 55.1 + 13.7 (49) 0.833
## 5 diabetes 15 / 24 (62.5 %) 26 / 49 (53.1 %) 0.608
## 6 bmi 31.2 + 8.6 (24) 32.5 + 9.2 (49) 0.655
## 7 calcium 8.4 + 0.8 (24) 8.8 + 0.9 (46) 0.045
## 8 phosphate 6.2 + 1.5 (24) 6.7 + 2.2 (46) 0.344
## 9 pth 479.7 + 275.3 (23) 431.7 + 267.7 (42) 0.493
## 10 alkphos 147.2 + 59.7 (24) 175.7 + 114.1 (46) 0.621
## 11 albumin 2.8 + 0.5 (24) 3.0 + 0.6 (46) 0.094
## 12 warfarin 3 / 24 (12.5 %) 1 / 49 (2.0 %) 0.195
## 13 cinacalcet 7 / 24 (29.2 %) 23 / 49 (46.9 %) 0.231
## 14 vd 11 / 24 (45.8 %) 14 / 49 (28.6 %) 0.231Kaplan Meier Curve: all time + PD + Caliph diag only
km.calci.pd <- survfit(Surv(len_fu2, death)~trt, data=dat.calci.pd)
plot(km.calci.pd, col=c("blue", "red"))
legend("topright", c("STS", "non-STS"), lty=1, col=c("red", "blue"))
Kaplan Meier Curve: 1 yr follow up + PD + Caliph diag only
km.calci.pd.1y <- survfit(Surv(len_fu.1y, death.1y)~trt, dat=dat.calci.pd)
plot(km.calci.pd.1y, col=c("blue", "red"))
legend("topright", c("STS", "non-STS"), lty=1, col=c("red", "blue"))
km.calci.pd.1y## Call: survfit(formula = Surv(len_fu.1y, death.1y) ~ trt, data = dat.calci.pd)
##
## n events median 0.95LCL 0.95UCL
## trt=1 24 13 264 114 NA
## trt=0 49 24 271 69 NAKaplan Meier Curve: 2 yrs follow up + PD + Caliph diag only
km.calci.pd.2y <- survfit(Surv(len_fu.2y, death.2y)~trt, data=dat.calci.pd)
plot(km.calci.pd.2y, col=c("blue", "red"))
legend("topright", c("STS", "non-STS"), lty=1, col=c("red", "blue"))
km.calci.pd.2y## Call: survfit(formula = Surv(len_fu.2y, death.2y) ~ trt, data = dat.calci.pd)
##
## n events median 0.95LCL 0.95UCL
## trt=1 24 14 264 114 NA
## trt=0 49 28 271 69 NAHD Patients with Caliphylaxis diagnostic date only
n=239 DCI identified calciphylaxis patients with available onset diagnostic date and never on PD at the time of onset date.
dat.calci.hd <- subset(dat, CalciDiag==1 & dm == "HD")Baseline information
## Variable Treatment Control P.Value
## 1 sex 43 / 117 (36.8 %) 50 / 150 (33.3 %) 0.651
## 2 race 76 / 115 (66.1 %) 84 / 149 (56.4 %) 0.14
## 3 ethnicity 4 / 113 (3.5 %) 4 / 149 (2.7 %) 0.971
## 4 age 60.1 + 11.2 (117) 54.7 + 12.7 (150) <.001
## 5 diabetes 82 / 117 (70.1 %) 85 / 150 (56.7 %) 0.034
## 6 bmi 32.8 + 8.4 (117) 31.8 + 8.8 (150) 0.377
## 7 calcium 8.9 + 0.8 (102) 8.9 + 1.1 (130) 0.953
## 8 phosphate 6.5 + 2.0 (101) 6.5 + 2.1 (130) 0.906
## 9 pth 634.7 + 537.0 (97) 595.7 + 686.6 (120) 0.648
## 10 alkphos 172.5 + 143.3 (102) 224.9 + 250.5 (129) 0.061
## 11 albumin 3.4 + 0.6 (102) 3.5 + 0.5 (129) 0.159
## 12 warfarin 8 / 117 (6.8 %) 4 / 150 (2.7 %) 0.182
## 13 cinacalcet 58 / 117 (49.6 %) 37 / 150 (24.7 %) <.001
## 14 vd 63 / 117 (53.8 %) 78 / 150 (52.0 %) 0.86Kaplan Meier Curve: all time + HD + Caliph diag only
km.calci.hd <- survfit(Surv(len_fu2, death)~trt, data=dat.calci.hd)
plot(km.calci.hd, col=c("blue", "red"))
legend("topright", c("STS", "non-STS"), lty=1, col=c("red", "blue"))
km.calci.hd## Call: survfit(formula = Surv(len_fu2, death) ~ trt, data = dat.calci.hd)
##
## n events median 0.95LCL 0.95UCL
## trt=1 117 65 434 332 1027
## trt=0 150 95 852 584 1268Kaplan Meier Curve: 1 yr follow up + HD + Caliph diag only
km.calci.hd.1y <- survfit(Surv(len_fu.1y, death.1y)~trt, dat=dat.calci.hd)
plot(km.calci.hd.1y, col=c("blue", "red"))
legend("topright", c("STS", "non-STS"), lty=1, col=c("red", "blue"))
km.calci.hd.1y## Call: survfit(formula = Surv(len_fu.1y, death.1y) ~ trt, data = dat.calci.hd)
##
## n events median 0.95LCL 0.95UCL
## trt=1 117 45 NA 332 NA
## trt=0 150 50 NA NA NAKaplan Meier Curve: 2 yr follow up + HD + Caliph diag only
km.calci.hd.2y <- survfit(Surv(len_fu.2y, death.2y)~trt, data=dat.calci.hd)
plot(km.calci.hd.2y, col=c("blue", "red"))
legend("topright", c("STS", "non-STS"), lty=1, col=c("red", "blue"))
km.calci.hd.2y## Call: survfit(formula = Surv(len_fu.2y, death.2y) ~ trt, data = dat.calci.hd)
##
## n events median 0.95LCL 0.95UCL
## trt=1 117 57 434 332 NA
## trt=0 150 65 NA 584 NAHD Patients with Caliphylaxis diagnostic date only : matched sample
n=205 DCI identified calciphylaxis patients with available onset diagnostic date and never on PD at the time of onset date.
dat.calci.hd.m <- subset(dat.hd.m, CalciDiag==1)Baseline information
## Variable Treatment Control P.Value
## 1 sex 37 / 92 (40.2 %) 50 / 150 (33.3 %) 0.344
## 2 race 58 / 91 (63.7 %) 84 / 149 (56.4 %) 0.322
## 3 ethnicity 0 / 88 (0.0 %) 4 / 149 (2.7 %) 0.304
## 4 age 56.4 + 9.1 (92) 54.7 + 12.7 (150) 0.258
## 5 diabetes 62 / 92 (67.4 %) 85 / 150 (56.7 %) 0.128
## 6 bmi 33.0 + 9.0 (92) 31.8 + 8.8 (150) 0.303
## 7 calcium 8.9 + 0.9 (80) 8.9 + 1.1 (130) 0.724
## 8 phosphate 6.6 + 2.1 (79) 6.5 + 2.1 (130) 0.756
## 9 pth 638.4 + 561.3 (76) 595.7 + 686.6 (120) 0.65
## 10 alkphos 182.0 + 157.8 (80) 224.9 + 250.5 (129) 0.172
## 11 albumin 3.4 + 0.6 (80) 3.5 + 0.5 (129) 0.058
## 12 warfarin 5 / 92 (5.4 %) 4 / 150 (2.7 %) 0.45
## 13 cinacalcet 44 / 92 (47.8 %) 37 / 150 (24.7 %) <.001
## 14 vd 48 / 92 (52.2 %) 78 / 150 (52.0 %) >.999Kaplan Meier Curve: all time + HD + Caliph diag only + matched sample
km.calci.hd.m <- survfit(Surv(len_fu2, death)~trt, data=dat.calci.hd.m)
plot(km.calci.hd.m, col=c("blue", "red"))
legend("topright", c("STS", "non-STS"), lty=1, col=c("red", "blue"))
km.calci.hd.m## Call: survfit(formula = Surv(len_fu2, death) ~ trt, data = dat.calci.hd.m)
##
## n events median 0.95LCL 0.95UCL
## trt=1 92 49 552 369 1518
## trt=0 150 95 852 584 1268Kaplan Meier Curve: 1 yr follow up + HD + Caliph diag only + matched sample
km.calci.hd.1y.m <- survfit(Surv(len_fu.1y, death.1y)~trt, dat=dat.calci.hd.m)
plot(km.calci.hd.1y.m, col=c("blue", "red"))
legend("topright", c("STS", "non-STS"), lty=1, col=c("red", "blue"))
km.calci.hd.1y.m## Call: survfit(formula = Surv(len_fu.1y, death.1y) ~ trt, data = dat.calci.hd.m)
##
## n events median 0.95LCL 0.95UCL
## trt=1 92 34 NA NA NA
## trt=0 150 50 NA NA NAKaplan Meier Curve: 2 yr follow up + HD + Caliph diag only + matched sample
km.calci.hd.2y.m <- survfit(Surv(len_fu.2y, death.2y)~trt, data=dat.calci.hd.m)
plot(km.calci.hd.2y.m, col=c("blue", "red"))
legend("topright", c("STS", "non-STS"), lty=1, col=c("red", "blue"))
km.calci.hd.2y.m## Call: survfit(formula = Surv(len_fu.2y, death.2y) ~ trt, data = dat.calci.hd.m)
##
## n events median 0.95LCL 0.95UCL
## trt=1 92 41 552 369 NA
## trt=0 150 65 NA 584 NAPD Patients with STS date only (group by delayed treatment or not)
n=30 DCI identified patients treated with STS and were on PD at the time of onset date. Choose group cutoff as 14 days, i.e. patients who have there treatment within 14 days of diagnostics as a group.
# delay treatment
dat.sts <- subset(dat, trt==1 & CalciDiag==1)
dat.sts$delay <- as.numeric(difftime(as.Date(dat.sts$STSStartDate, "%m/%d/%Y"), as.Date(dat.sts$CalciDiagDate, "%m/%d/%Y"), units="days"))
dat.sts2 <- subset(dat.sts, delay >= 0)
dat.sts2$group <- dat.sts2$delay <= 14
dat.sts2.pd <- subset(dat.sts2, dm=="PD")Baseline information
## Variable Delayed Within2wks P.Value
## 1 sex 2 / 10 (20.0 %) 5 / 11 (45.5 %) 0.44
## 2 race 7 / 10 (70.0 %) 8 / 10 (80.0 %) >.999
## 3 ethnicity 1 / 9 (11.1 %) 1 / 10 (10.0 %) >.999
## 4 age 53.2 + 11.9 (10) 58.5 + 15.2 (11) 0.291
## 5 diabetes 6 / 10 (60.0 %) 8 / 11 (72.7 %) 0.877
## 6 bmi 32.7 + 9.9 (10) 32.0 + 7.2 (11) 0.833
## 7 calcium 8.2 + 0.7 (10) 8.3 + 0.7 (11) 0.548
## 8 phosphate 6.4 + 1.9 (10) 6.0 + 1.2 (11) 0.725
## 9 pth 512.0 + 306.7 (10) 449.5 + 282.6 (10) 0.65
## 10 alkphos 160.4 + 75.1 (10) 146.4 + 43.3 (11) 0.698
## 11 albumin 2.8 + 0.6 (10) 2.8 + 0.5 (11) 0.944
## 12 warfarin 0 / 10 (0.0 %) 3 / 11 (27.3 %) 0.246
## 13 cinacalcet 5 / 10 (50.0 %) 1 / 11 (9.1 %) 0.112
## 14 vd 6 / 10 (60.0 %) 5 / 11 (45.5 %) 0.819Kaplan Meier Curve: all time + PD + STS patients
km.delay.pd <- survfit(Surv(len_fu2, death)~group, dat=dat.sts2.pd)
plot(km.delay.pd, col=c("blue", "red"))
legend("topright", c("> 14 days", "<= 14 days"), lty=1, col=c("red", "blue"))
km.delay.pd## Call: survfit(formula = Surv(len_fu2, death) ~ group, data = dat.sts2.pd)
##
## n events median 0.95LCL 0.95UCL
## group=FALSE 10 5 350 113 NA
## group=TRUE 11 8 202 62 NAKaplan Meier Curve: 1 yr follow up + PD + STS patients
km.delay.pd.1y <- survfit(Surv(len_fu.1y, death.1y)~group, dat=dat.sts2.pd)
plot(km.delay.pd.1y, col=c("blue", "red"))
legend("topright", c("> 14 days", "<= 14 days"), lty=1, col=c("red", "blue"))
km.delay.pd.1y## Call: survfit(formula = Surv(len_fu.1y, death.1y) ~ group, data = dat.sts2.pd)
##
## n events median 0.95LCL 0.95UCL
## group=FALSE 10 4 277 113 NA
## group=TRUE 11 8 202 62 NAKaplan Meier Curve: 2 yrs follow up + PD + STS patients
km.delay.pd.2y <- survfit(Surv(len_fu.2y, death.2y)~group, data=dat.sts2.pd)
plot(km.delay.pd.2y, col=c("blue", "red"))
legend("topright", c("> 14 days", "<= 14 days"), lty=1, col=c("red", "blue"))
km.delay.pd.2y## Call: survfit(formula = Surv(len_fu.2y, death.2y) ~ group, data = dat.sts2.pd)
##
## n events median 0.95LCL 0.95UCL
## group=FALSE 10 5 350 113 NA
## group=TRUE 11 8 202 62 NAHD Patients with STS date only (group by delayed treatment or not)
n=104 DCI identified patients treated with STS and were on PD at the time of onset date. Choose group cutoff as 14 days, i.e. patients who have there treatment within 14 days of diagnostics as a group.
dat.sts2.hd <- subset(dat.sts2, dm=="HD")Baseline information
## Variable Delayed Within2wks P.Value
## 1 sex 16 / 40 (40.0 %) 25 / 73 (34.2 %) 0.686
## 2 race 25 / 40 (62.5 %) 49 / 71 (69.0 %) 0.625
## 3 ethnicity 1 / 39 (2.6 %) 3 / 70 (4.3 %) >.999
## 4 age 57.7 + 10.4 (40) 61.4 + 11.6 (73) 0.096
## 5 diabetes 32 / 40 (80.0 %) 49 / 73 (67.1 %) 0.217
## 6 bmi 34.4 + 9.0 (40) 32.3 + 7.9 (73) 0.209
## 7 calcium 8.9 + 0.8 (31) 8.9 + 0.8 (67) 0.802
## 8 phosphate 6.8 + 2.1 (31) 6.3 + 1.8 (66) 0.212
## 9 pth 711.7 + 722.3 (29) 599.8 + 442.6 (64) 0.66
## 10 alkphos 195.5 + 209.8 (31) 156.9 + 90.5 (67) 0.205
## 11 albumin 3.5 + 0.4 (31) 3.4 + 0.7 (67) 0.379
## 12 warfarin 2 / 40 (5.0 %) 6 / 73 (8.2 %) 0.799
## 13 cinacalcet 18 / 40 (45.0 %) 38 / 73 (52.1 %) 0.603
## 14 vd 23 / 40 (57.5 %) 38 / 73 (52.1 %) 0.72Kaplan Meier Curve: all time + HD + STS patients
km.delay.hd <- survfit(Surv(len_fu2, death)~group, dat=dat.sts2.hd)
plot(km.delay.hd, col=c("blue", "red"))
legend("topright", c("> 14 days", "<= 14 days"), lty=1, col=c("red", "blue"))
km.delay.hd## Call: survfit(formula = Surv(len_fu2, death) ~ group, data = dat.sts2.hd)
##
## n events median 0.95LCL 0.95UCL
## group=FALSE 40 23 406 321 NA
## group=TRUE 73 40 445 311 NAKaplan Meier Curve: 1 yr follow up + HD + STS patients
km.delay.hd.1y <- survfit(Surv(len_fu.1y, death.1y)~group, dat=dat.sts2.hd)
plot(km.delay.hd.1y, col=c("blue", "red"))
legend("topright", c("> 14 days", "<= 14 days"), lty=1, col=c("red", "blue"))
km.delay.hd.1y## Call: survfit(formula = Surv(len_fu.1y, death.1y) ~ group, data = dat.sts2.hd)
##
## n events median 0.95LCL 0.95UCL
## group=FALSE 40 16 NA 321 NA
## group=TRUE 73 28 NA 311 NAKaplan Meier Curve: 2 yr follow up + HD + STS patients
km.delay.hd.2y <- survfit(Surv(len_fu.2y, death.2y)~group, data=dat.sts2.hd)
plot(km.delay.hd.2y, col=c("blue", "red"))
legend("topright", c("> 14 days", "<= 14 days"), lty=1, col=c("red", "blue"))
km.delay.hd.2y## Call: survfit(formula = Surv(len_fu.2y, death.2y) ~ group, data = dat.sts2.hd)
##
## n events median 0.95LCL 0.95UCL
## group=FALSE 40 19 406 321 NA
## group=TRUE 73 37 445 311 NA