Acute myeloid leukemia

This simulated data set is based on a trial in acute myeloid leukemia.

Usage

myeloid

Format

A data frame with 646 observations on the following 9 variables.

id

subject identifier, 1-646

trt

treatment arm A or B

sex

f=female, m=male

flt3

mutations of the FLT3 gene, a factor with levels of A, B, C

futime

time to death or last follow-up

death

1 if futime is a death, 0 for censoring

txtime

time to hematropetic stem cell transplant

crtime

time to complete response

rltime

time to relapse of disease

Details

This data set is used to illustrate multi-state survival curves. It is based on the actual study in the reference below. A subset of subjects was de-identifed, reordered, and then all of the time values randomly perturbed.

Mutations in the FLT3 domain occur in about 1/3 of AML patients, the additional agent in treatment arm B was presumed to target this anomaly. All subjects had a FLT mutation, either internal tandem duplications (ITD) (divided into low vs high) +- mutations in the TKD domain, or TKD mutations only. This was a stratification factor for treatment assignment in the study. The levels of A, B, C correspond to increasing severity of the mutation burden.

Note

The usage above assumes that the survival package is attached via library(survival) or via another package that depends on survival, e.g., mstate. If not, then either survival::myeloid or data(cancer, package="survival") can be used.

References

Le-Rademacher JG, Peterson RA, Therneau TM, Sanford BL, Stone RM, Mandrekar SJ. Application of multi-state models in cancer clinical trials. Clin Trials. 2018 Oct; 15 (5):489-498

Examples

coxph(Surv(futime, death) ~ trt + flt3, data=myeloid)
#> Call:
#> coxph(formula = Surv(futime, death) ~ trt + flt3, data = myeloid)
#> 
#>          coef exp(coef) se(coef)      z        p
#> trtB  -0.3534    0.7023   0.1122 -3.149  0.00164
#> flt3B  0.4114    1.5089   0.1587  2.593  0.00952
#> flt3C  0.7878    2.1985   0.1656  4.758 1.96e-06
#> 
#> Likelihood ratio test=34.04  on 3 df, p=1.94e-07
#> n= 646, number of events= 320 
# See the mstate vignette for a more complete analysis