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A data frame with 5943 French kidney transplant recipients from the DIVAT cohort. The patient evolution can be described according to a 4-state structure: X=2 represents the acute rejection episode, X=3 the definitive return to dialysis and X=4 the death. These times can be right-censored. A vector of covariates is also collected at the transplantation, i.e. the baseline of the cohort.
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A data frame with 5943 observations (rows) with the 7 following variables (columns):
trajectory | A numeric vector with the sequences of observed states. |
time1 | A numeric vector with the times (in days) between the transplantation |
and the first clinical event (acute rejection episode, return to dialysis, | |
or death with a functioning graft), or the times to censoring if trajectory=1 |
|
time2 | A numeric vector with the time between the transplantation and the |
second clinical event (return to dialysis or death with a functioning graft), | |
or the the time to censoring if trajectory=12 |
|
ageR | A numeric vector with the recipient age (in years) at the transplantation. |
sexR | A character vector with the recipient gender. |
year.tx | A numeric vector with the calendar year of the transplantation. |
z | A numeric vector represents the explicative variable under interest, i.e. the delayed |
graft function (1=yes, 0=no). | |
The immunology and nephrology department of the Nantes University hospital constituted a data bank with the monitoring of medical records for kidney and/or pancreas transplant recipients. Here is a sample of 5943 patients from this DIVAT cohort. A vector of covariates, all measured at the transplantation, is collected for each patient.
URL: http://www.divat.fr/
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | data(dataDIVAT)
### a description of transitions
table(dataDIVAT$trajectory)
### patient-graft survival (first event between the return to dialysis and the patient
### death with a functioning graft)
dataDIVAT$failure<-1*(dataDIVAT$trajectory!=1 & dataDIVAT$trajectory!=12)
dataDIVAT$time<-NA
dataDIVAT$time<-ifelse(dataDIVAT$trajectory %in% c(1,12,13,14),
dataDIVAT$time1,dataDIVAT$time1+dataDIVAT$time2)
plot(survfit(Surv(time/365.24, failure) ~ 1 , data=dataDIVAT), mark.time=FALSE,
xlim=c(0,12), ylim=c(0,1), cex=1.5, col=1, lwd=2, lty=1,
xlab="Times after the transplantation (years)",
ylab="Patient-graft survival")
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