This is a dataset on 407 patients suffering from chronic kidney disease who underwent a primary renal transplantation with a graft from a deceased or living donor in the University Hospital of the Catholic University of Leuven (Belgium) between 21 January 1983 and 16 August 2000. Chronic kidney (renal) disease is a progressive loss of renal function over a period of months or years through five stages. Each stage is a progression through an abnormally low and progressively worse glomerular filtration rate (GFR). The dataset records 3 repeated measures (2 continuous and 1 binary), and an event time.
This is a list with 4 data frames:
prot: repeated measurement data for proteinuria (binary) that
measures whether the kidneys succeed in sustaining the proteins in the
blood and not discard them in the urine.
haem: repeated measurement data for blood haematocrit level
(continuous) that measures whether the kidneys produce adequate amounts of
the hormone erythropoietin that regulates the red blood cell production.
gfr: repeated measurement data for GFR (continuous) that
measures the filtration rate of the kidneys.
surv: time-to-event data for renal graft failure.
All datasets have the common data columns, which are in long format for the 3 longitudinal data data frames, and 1-per-subject for the time-to-event data frame:
number for patient identification.
age of patient at day of surgery (years).
preoperative weight of patient (kg).
gender of patient.
maximum follow up time, with transplant date as the time origin (years).
censoring indicator (
1=graft failure and
The longitudinal datasets only contain 2 further columns:
observed time point, with surgery date as the time origin (years).
a recorded measurement of the biomarker taken at
time. The 3 biomarkers (one per data frame) are:
proteinuria: recorded as binary indicator: present or
not-present. Present in the
haematocrit: recorded as percentage (%) of the ratio of the
volume of red blood cells to the total volume of blood. Present in the
gfr: measured as ml/min/1.73m^2. Present in the
Dr Dimitris Rizopoulos (email@example.com).
Rizopoulos D, Ghosh, P. A Bayesian semiparametric multivariate joint model for multiple longitudinal outcomes and a time-to-event. Stat Med. 2011; 30(12): 1366-80.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.