Survival times, in days, of 23 leukaemia patients who received a bone marrow transplant, with donor and recipient characteristics and an indicator of platelet recovery (Collett, 2023, Example 8.2).
Format
A data frame with 23 rows and 8 variables:
- patient
Patient number (1-23).
- time
Survival time in days from transplantation.
- status
Event indicator: 1 = death, 0 = alive at the end of follow-up.
- group
Disease group: 1 = acute lymphoblastic leukaemia (ALL), 2 = low-risk acute myeloid leukaemia (AML), 3 = high-risk AML.
- page
Age of the patient in years.
- dage
Age of the bone marrow donor in years.
- precovery
Platelet recovery: 0 = no, 1 = platelet count returned to a normal level of 40 x 10^9 per litre.
- ptime
Time in days until the platelet count returned to normal (
NAwhenprecoveryis 0).
Source
Collett, D. (2023) Modelling Survival Data in Medical Research, 4th ed. Chapman & Hall/CRC, Example 8.2 (Table 8.2). Data from Klein and Moeschberger (2005).
Details
This is the subset of the study from St Vincent's Hospital, Sydney, used by
Klein and Moeschberger (2005). There are 12 deaths. Patients 7 and 21 died
before their platelet count returned to normal, and so ptime is missing for
them; these missing values are stored as NA in the package.
The data illustrate the use of a time-dependent covariate: the indicator of
platelet recovery changes from 0 to 1 at ptime, so the survival data are
expanded into the counting process format (Example 8.2, and Example 8.1
introduces the format itself).
Aliases
The data set is available under the alias d8.2; dX.Y is the alias for
the data set used in Example X.Y of Collett (2023). See
dataset_by_exercise for the complete index.
Examples
# Collett (2023), Chapter 17 style -----------------------------------
# Example 8.2: platelet recovery as a time-dependent covariate
base <- bmt_leukaemia[, c("patient", "time", "status", "group", "page", "dage")]
recovery <- subset(bmt_leukaemia, precovery == 1)[, c("patient", "ptime")]
cp <- survival::tmerge(base, base, id = patient, endpt = event(time, status))
cp <- survival::tmerge(cp, recovery, id = patient, preg = tdc(ptime))
survival::coxph(
survival::Surv(tstart, tstop, endpt) ~ preg + factor(group) + page + dage,
data = cp
)
#> Call:
#> survival::coxph(formula = survival::Surv(tstart, tstop, endpt) ~
#> preg + factor(group) + page + dage, data = cp)
#>
#> coef exp(coef) se(coef) z p
#> preg -2.449482 0.086338 1.273217 -1.924 0.0544
#> factor(group)2 -2.611157 0.073449 1.366726 -1.911 0.0561
#> factor(group)3 -0.594195 0.552007 1.433726 -0.414 0.6786
#> page 0.043504 1.044464 0.062179 0.700 0.4841
#> dage -0.005916 0.994101 0.039446 -0.150 0.8808
#>
#> Likelihood ratio test=11.98 on 5 df, p=0.03513
#> n= 44, number of events= 12