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

Source: R/datasets.R

A data frame of 2279 patients transplanted at the EBMT between 1985 and 1998. These data were used in Fiocco, Putter & van Houwelingen (2008), van Houwelingen & Putter (2008, 2012) and de Wreede, Fiocco & Putter (2011). 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

recae.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"

Format

A data frame, see data.frame.

Source

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

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.

van Houwelingen HC, Putter H (2012). Dynamic Prediction in Clinical Survival Analaysis. Chapman & Hall/CRC Press, Boca Raton.

de Wreede LC, Fiocco M, and Putter H (2011). mstate: An R Package for the Analysis of Competing Risks and Multi-State Models. Journal of Statistical Software, Volume 38, Issue 7.