Learn R Programming

dynpred (version 0.1.2)

EBMT data: Data from the European Society for Blood and Marrow Transplantation (EBMT)

Description

Data from the European Society for Blood and Marrow Transplantation (EBMT)

Arguments

Format

A data frame of 2279 patients transplanted at the EBMT between 1985 and 1998. These data were used in Fiocco, Putter & van Houwelingen (2008) and van Houwelingen & Putter (2008). The included variables are
id
Patient identification number
rec
Time in days from transplantation to recovery or last follow-up
rec.s
Recovery status; 1 = recovery, 0 = censored
ae
Time in days from transplantation to adverse event (AE) or last follow-up
ae.s
Adverse event status; 1 = adverse event, 0 = censored
recae
Time in days from transplantation to both recovery and AE or last follow-up
plag.s
Recovery and AE status; 1 = both recovery and AE, 0 = no recovery or no AE or censored
rel
Time in days from transplantation to relapse or last follow-up
rel.s
Relapse status; 1 = relapse, 0 = censored
srv
Time in days from transplantation to death or last follow-up
srv.s
Relapse status; 1 = dead, 0 = censored
year
Year of transplantation; factor with levels "1985-1989", "1990-1994", "1995-1998"
agecl
Patient age at transplant; factor with levels "<=20", "20-40",="" "="">40"
proph
Prophylaxis; factor with levels "no", "yes"
match
Donor-recipient gender match; factor with levels "no gender mismatch", "gender mismatch"

Source

We gratefully 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

Fiocco M, Putter H, van Houwelingen HC (2008). Reduced-rank proportional hazards regression and simulation-based prediction for multi-state models. Statistics in Medicine 27, 4340--4358.

van Houwelingen HC, Putter H (2008). Dynamic predicting by landmarking as an alternative for multi-state modeling: an application to acute lymphoid leukemia data. Lifetime Data Anal 14, 447--463.