DCI analysis v1.21 matched
Dihua Xu
December 15, 2015
Original data
n=450 DCI identified calciphylaxis patients with available onset date and correct death date.
Baseline information
## Variable Treatment Control P.Value
## 1 sex 85 / 251 (33.9 %) 63 / 199 (31.7 %) 0.694
## 2 race 158 / 245 (64.5 %) 117 / 198 (59.1 %) 0.286
## 3 ethnicity 26 / 242 (10.7 %) 7 / 198 (3.5 %) 0.007
## 4 age 58.5 + 12.8 (251) 54.8 + 12.9 (199) 0.002
## 5 diabetes 157 / 251 (62.5 %) 111 / 199 (55.8 %) 0.175
## 6 dm 58 / 251 (23.1 %) 68 / 199 (34.2 %) 0.013
## 7 bmi 32.2 + 8.7 (251) 32.0 + 8.8 (199) 0.826
## 8 calcium 8.8 + 0.8 (235) 8.9 + 1.0 (176) 0.491
## 9 phosphate 6.1 + 1.9 (234) 6.6 + 2.1 (176) 0.022
## 10 pth 517.9 + 484.1 (223) 553.2 + 609.8 (162) 0.528
## 11 alkphos 174.0 + 134.3 (235) 211.9 + 223.6 (175) 0.034
## 12 albumin 3.4 + 0.6 (235) 3.4 + 0.6 (175) 0.701
## 13 warfarin 16 / 251 (6.4 %) 5 / 199 (2.5 %) 0.088
## 14 cinacalcet 110 / 251 (43.8 %) 60 / 199 (30.2 %) 0.004
## 15 vd 135 / 251 (53.8 %) 92 / 199 (46.2 %) 0.134Mathed sample
n=406 Matched AGE + ETHNICITY + DM (PD/HD)
matchit(trt~age+ethnicity+dm, data=na.exclude(dat[,c(1,9,11,4,15)]), method="nearest") -> m.out
m.data1 <- match.data(m.out)
dat.m <- subset(dat, patient_id %in% m.data1$patient_id | is.na(dat$ethnicity ))Baseline information
## Variable Treatment Control P.Value
## 1 sex 72 / 207 (34.8 %) 63 / 199 (31.7 %) 0.574
## 2 race 123 / 202 (60.9 %) 117 / 198 (59.1 %) 0.791
## 3 ethnicity 3 / 198 (1.5 %) 7 / 198 (3.5 %) 0.337
## 4 age 56.7 + 11.6 (207) 54.8 + 12.9 (199) 0.120
## 5 diabetes 129 / 207 (62.3 %) 111 / 199 (55.8 %) 0.215
## 6 dm 55 / 207 (26.6 %) 68 / 199 (34.2 %) 0.119
## 7 bmi 32.8 + 9.0 (207) 32.0 + 8.8 (199) 0.377
## 8 calcium 8.8 + 0.8 (193) 8.9 + 1.0 (176) 0.482
## 9 phosphate 6.2 + 2.0 (192) 6.6 + 2.1 (176) 0.070
## 10 pth 514.2 + 448.9 (182) 553.2 + 609.8 (162) 0.497
## 11 alkphos 174.0 + 132.2 (193) 211.9 + 223.6 (175) 0.046
## 12 albumin 3.3 + 0.6 (193) 3.4 + 0.6 (175) 0.191
## 13 warfarin 14 / 207 (6.8 %) 5 / 199 (2.5 %) 0.073
## 14 cinacalcet 89 / 207 (43.0 %) 60 / 199 (30.2 %) 0.010
## 15 vd 109 / 207 (52.7 %) 92 / 199 (46.2 %) 0.232Kaplan Meier Curve
km1 <- survfit(Surv(len_fu2, death)~trt, data=dat.m)
plot(km1, col=c("blue", "red"))
legend("topright", c("STS", "non-STS"), lty=1, col=c("red", "blue"))
km1## Call: survfit(formula = Surv(len_fu2, death) ~ trt, data = dat.m)
##
## n events median 0.95LCL 0.95UCL
## trt=1 207 112 424 331 824
## trt=0 199 131 684 430 1011Kaplan Meier Curve with 1 yr follow up
dat.m$death.1y <- (dat.m$death==1 & dat.m$len_fu2 < 365.25)
dat.m$len_fu.1y <- as.numeric(ifelse(dat.m$len_fu2 > 365.25, 365.25, dat.m$len_fu2))
km1y <- survfit(Surv(len_fu.1y, death.1y)~trt, data=dat.m)
plot(km1y, col=c("blue", "red"))
legend("topright", c("STS", "non-STS"), lty=1, col=c("red", "blue"))
km1y## Call: survfit(formula = Surv(len_fu.1y, death.1y) ~ trt, data = dat.m)
##
## n events median 0.95LCL 0.95UCL
## trt=1 207 82 NA 331 NA
## trt=0 199 74 NA NA NAKaplan Meier Curve with 2 yrs follow up
dat.m$death.2y <- (dat.m$death==1 & dat.m$len_fu2 < 365.25*2)
dat.m$len_fu.2y <- as.numeric(ifelse(dat.m$len_fu2 > 365.25*2, 365.25*2, dat.m$len_fu2))
km2y <- survfit(Surv(len_fu.2y, death.2y)~trt, data=dat.m)
plot(km2y, col=c("blue", "red"))
legend("topright", c("STS", "non-STS"), lty=1, col=c("red", "blue"))
km2y## Call: survfit(formula = Surv(len_fu.2y, death.2y) ~ trt, data = dat.m)
##
## n events median 0.95LCL 0.95UCL
## trt=1 207 98 424 331 NA
## trt=0 199 93 684 430 NAPatients with Caliphylaxis diagnostic date only
n=324 DCI mathed sample with available onset diagnostic date.
dat.m.calci <- subset(dat.m, CalciDiag==1)Baseline information
## Variable Treatment Control P.Value
## 1 sex 46 / 125 (36.8 %) 63 / 199 (31.7 %) 0.405
## 2 race 77 / 122 (63.1 %) 117 / 198 (59.1 %) 0.550
## 3 ethnicity 2 / 119 (1.7 %) 7 / 198 (3.5 %) 0.540
## 4 age 57.5 + 11.3 (125) 54.8 + 12.9 (199) 0.059
## 5 diabetes 84 / 125 (67.2 %) 111 / 199 (55.8 %) 0.054
## 6 dm 32 / 125 (25.6 %) 68 / 199 (34.2 %) 0.133
## 7 bmi 32.7 + 8.7 (125) 32.0 + 8.8 (199) 0.487
## 8 calcium 8.8 + 0.9 (112) 8.9 + 1.0 (176) 0.520
## 9 phosphate 6.5 + 1.9 (111) 6.6 + 2.1 (176) 0.637
## 10 pth 613.4 + 517.2 (107) 553.2 + 609.8 (162) 0.401
## 11 alkphos 172.3 + 137.3 (112) 211.9 + 223.6 (175) 0.093
## 12 albumin 3.2 + 0.6 (112) 3.4 + 0.6 (175) 0.067
## 13 warfarin 9 / 125 (7.2 %) 5 / 199 (2.5 %) 0.082
## 14 cinacalcet 56 / 125 (44.8 %) 60 / 199 (30.2 %) 0.011
## 15 vd 66 / 125 (52.8 %) 92 / 199 (46.2 %) 0.300Kaplan Meier Curve for Caliphylaxis diagnostic patients
km.calci <- survfit(Surv(len_fu2, death)~trt, data=dat.m.calci)
plot(km.calci, col=c("blue", "red"))
legend("topright", c("STS", "non-STS"), lty=1, col=c("red", "blue"))
Kaplan Meier Curve with 1 yr follow up for Caliphylaxis diagnostic patients
km1y <- survfit(Surv(len_fu.1y, death.1y)~trt, data=dat.m.calci)
plot(km1y, col=c("blue", "red"))
legend("topright", c("STS", "non-STS"), lty=1, col=c("red", "blue"))
km1y## Call: survfit(formula = Surv(len_fu.1y, death.1y) ~ trt, data = dat.m.calci)
##
## n events median 0.95LCL 0.95UCL
## trt=1 125 51 NA 327 NA
## trt=0 199 74 NA NA NAKaplan Meier Curve with 2 yrs follow up for Caliphylaxis diagnostic patients
km2y <- survfit(Surv(len_fu.2y, death.2y)~trt, data=dat.m.calci)
plot(km2y, col=c("blue", "red"))
legend("topright", c("STS", "non-STS"), lty=1, col=c("red", "blue"))
km2y## Call: survfit(formula = Surv(len_fu.2y, death.2y) ~ trt, data = dat.m.calci)
##
## n events median 0.95LCL 0.95UCL
## trt=1 125 61 417 327 NA
## trt=0 199 93 684 430 NAdelayed treatment analysis (STS patients only)
n=134 DCI mathed sample with available STS treatment date. Delete 7 out of 141 patients who have STS treatment date before calciphylaxis diagnostic date. Final n=134. Choose group cutoff as 14 days, i.e. patients who have there treatment within 14 days of diagnostics as a group.
# delay treatment
dat.m.sts <- subset(dat.m.calci, trt==1)
dat.m.sts$delay <- as.numeric(difftime(as.Date(dat.m.sts$STSStartDate, "%m/%d/%Y"), as.Date(dat.m.sts$CalciDiagDate, "%m/%d/%Y"), units="days"))
dat.m.sts2 <- subset(dat.m.sts, delay >= 0)
dat.m.sts2$group <- dat.m.sts2$delay <= 14Baseline information
## Variable Treatment Control P.Value
## 1 sex 18 / 47 (38.3 %) 27 / 72 (37.5 %) >.999
## 2 race 30 / 47 (63.8 %) 46 / 69 (66.7 %) 0.907
## 3 ethnicity 0 / 45 (0.0 %) 1 / 68 (1.5 %) >.999
## 4 age 56.7 + 10.5 (47) 58.4 + 10.9 (72) 0.4
## 5 diabetes 35 / 47 (74.5 %) 47 / 72 (65.3 %) 0.392
## 6 dm 11 / 47 (23.4 %) 18 / 72 (25.0 %) >.999
## 7 bmi 34.1 + 9.3 (47) 32.5 + 8.1 (72) 0.325
## 8 calcium 8.8 + 0.8 (39) 8.8 + 0.9 (67) 0.971
## 9 phosphate 6.7 + 2.1 (39) 6.2 + 1.7 (66) 0.173
## 10 pth 675.0 + 657.7 (37) 580.8 + 435.9 (64) 0.389
## 11 alkphos 188.1 + 190.5 (39) 159.4 + 87.6 (67) 0.293
## 12 albumin 3.3 + 0.5 (39) 3.2 + 0.7 (67) 0.365
## 13 warfarin 2 / 47 (4.3 %) 7 / 72 (9.7 %) 0.454
## 14 cinacalcet 22 / 47 (46.8 %) 32 / 72 (44.4 %) 0.948
## 15 vd 28 / 47 (59.6 %) 36 / 72 (50.0 %) 0.403Kaplan Meier Curve for STS patients
km.delay <- survfit(Surv(len_fu2, death)~group, data=dat.m.sts2)
plot(km.delay, col=c("blue", "red"))
legend("topright", c("> 14 days", "<= 14 days"), lty=1, col=c("red", "blue"))
Kaplan Meier Curve with 1 yr follow up for Caliphylaxis diagnostic patients
km.delay.1y <- survfit(Surv(len_fu.1y, death.1y)~group, data=dat.m.sts2)
plot(km.delay.1y, col=c("blue", "red"))
legend("topright", c("> 14 days", "<= 14 days"), lty=1, col=c("red", "blue"))
Kaplan Meier Curve with 2 yrs follow up for Caliphylaxis diagnostic patients
km.delay.2y <- survfit(Surv(len_fu.2y, death.2y)~group, data=dat.m.sts2)
plot(km.delay.2y, col=c("blue", "red"))
legend("topright", c("> 14 days", "<= 14 days"), lty=1, col=c("red", "blue"))