Person-day data for the 137 leukemia patients who received bone marrow transplants under a radiation-free regimen at four medical centers, as used in the parametric g-formula illustration of Keil et al. (2014). Each subject contributes one row per day of follow-up (day 1 up to death, loss to follow-up, or day 1825), giving 108,714 rows. The dataset reproduces the person-day file created in Appendix 1 of that paper: 80 deaths and 39 losses to follow-up.
Format
A data frame with 108,714 rows and 21 variables:
- id
unique subject identifier (1-137)
- age
baseline age in years
- male
male indicator (1 = male, 0 = female)
- cmv
baseline cytomegalovirus immune status (1 = positive, 0 = negative)
- all
leukemia type (1 = acute lymphocytic, 0 = acute myeloid)
- wait
waiting time from leukemia diagnosis to transplantation, in months (waiting days / 30.5)
- day
days since bone marrow transplant (1-1825)
- d
indicator of death at the end of day
day(1 = yes, 0 = no)- gvhd
indicator of graft-versus-host disease having occurred by day
day(1 = yes, 0 = no); the exposure- daysgvhd
cumulative number of days with GvHD
- daysnogvhd
cumulative number of days without GvHD
- gvhdm1
one-day lag of
gvhd- relapse
indicator of leukemia relapse having occurred by day
day(1 = yes, 0 = no)- relapsem1
one-day lag of
relapse- daysrelapse
cumulative number of days since relapse
- daysnorelapse
cumulative number of days relapse-free
- platnorm
indicator of platelet recovery having occurred by day
day(1 = platelets returned to normal levels, 0 = not yet)- platnormm1
one-day lag of
platnorm- daysplatnorm
cumulative number of days with normal platelet levels
- daysnoplatnorm
cumulative number of days without normal platelet levels
- censlost
indicator of censoring due to loss to follow-up at day
day(1 = yes, 0 = no)
Details
GvHD (gvhd) is the exposure, death (d) is the outcome, and
relapse (relapse) and platelet recovery (platnorm) are
time-varying confounders. The three time-varying states are absorbing
(once 1, they remain 1); their daysX/daysnoX counters and
one-day lags (Xm1) summarize the history entering each model.
A worked total-effect analysis on this dataset (counterfactual mortality risk
had GvHD never occurred versus the natural course), mirroring the model
specifications in Appendix 2 of Keil et al. (2014), is provided in
references/paper_example.R in the package source repository.
References
Keil, A. P., Edwards, J. K., Richardson, D. B., Naimi, A. I., & Cole, S. R. (2014). The parametric g-formula for time-to-event data: intuition and a worked example. Epidemiology, 25(6), 889-897. doi:10.1097/EDE.0000000000000160