European Group for Blood and Marrow Transplantation data (one row per individual)
ebmtcal.RdA data frame of 2,279 individuals with blood cancer who have undergone a transplant.
This data is identical to the ebmt4 data, except two extra variables have
been derived, time until censoring and a censoring indicator, which are required
to assess calibration using some of the methods in calibmsm. Code for the derivation
of this dataset is provided in the source code for the package.
Format
'ebmtcal'
A data frame with 2,279 rows and 17 columns:
- id
Patient indentifier
- rec, rec.s
Time until and event indicator for recovery variable
- ae, ae.s
Time until and event indicator for adverse event variable
- recae, recae.s
Time until and event indicator for recovery + adverse event variable
- rel, rel.s
Time until and event indicator for relapse variable
- srv, srv.s
Time until and event indicator for death variable
- year
Year of transplant
- agecl
Age at transplant
- proph
Prophylaxis
- match
Donor-recipient match
- dtcens
Time of censoring
- dtcens_s
Event indicator, 1:censoring occured, 0: absorbing state entered before censoring occured
Source
This dataset was derived from data made available within the mstate package, see ebmt4.
The data was originally provided by the European Group for Blood and Marrow Transplantation (https://www.ebmt.org/).
We reiterate the source statement given by the developers of mstate:
"We acknowledge the European Society for Blood and Marrow Transplantation (EBMT)
for making available these data. Disclaimer: these data were simplified for the
purpose of illustration of the analysis of competing risks and multi-state models
and do not reflect any real life situation. No clinical conclusions should be
drawn from these data."
References
EBMT (2023). “Data from the European Society for Blood and Marrow Transplantation.” URL https://search.r-project.org/CRAN/refmans/mstate/html/EBMT-data.html.
de Wreede LC, Fiocco M, Putter H (2011). “mstate: An R Package for the Analysis of Competing Risks and Multi-State Models.” Journal of Statistical Software, 38(7).