Survival times, in days, of 12 patients in an artificial study of the treatment of cirrhosis of the liver, with baseline age and bilirubin level (Collett, 2023, Example 8.4).
Format
A data frame with 12 rows and 6 variables:
- patient
Patient number (1-12).
- time
Survival time in days.
- status
Event indicator: 1 = death, 0 = right-censored.
- age
Age of the patient in years.
- treat
Treatment: 0 = placebo, 1 = Liverol.
- lbr
Logarithm of the baseline bilirubin level (in micromol/l).
Source
Collett, D. (2023) Modelling Survival Data in Medical Research, 4th ed. Chapman & Hall/CRC, Example 8.4 (Table 8.4). The hypothetical study is loosely based on the primary biliary cirrhosis data of Therneau and Grambsch (2000).
Details
The data are artificial but are used to mimic a real liver study. Six
patients are allocated to Liverol and six to placebo. The baseline
log(bilirubin) value is analysed first, and the treatment effect is
estimated after adjustment for age and lbr (Example 8.4).
The same patients appear in three files in this package. cirrhosis holds
the baseline values (one row per patient), cirrhosis_lbr holds the
repeated log(bilirubin) measurements in long format (one row per visit) and
cirrhosis_cp holds the survival data together with the time-dependent
log(bilirubin) value in counting process format (one row per interval).
See cirrhosis_lbr and cirrhosis_cp.
Aliases
The data set is available under the alias d8.4; 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 8.4: the baseline model
survival::coxph(
survival::Surv(time, status) ~ age + lbr + treat,
data = cirrhosis
)
#> Call:
#> survival::coxph(formula = survival::Surv(time, status) ~ age +
#> lbr + treat, data = cirrhosis)
#>
#> coef exp(coef) se(coef) z p
#> age -0.08503 0.91848 0.07728 -1.100 0.2712
#> lbr 2.56080 12.94622 1.27211 2.013 0.0441
#> treat -3.05192 0.04727 1.56150 -1.954 0.0506
#>
#> Likelihood ratio test=11.83 on 3 df, p=0.007994
#> n= 12, number of events= 8
# Example 8.4: log(bilirubin) as a time-dependent covariate
# (two patients have a zero-length interval, hence the filter)
cp <- subset(cirrhosis_cp, stop > start)
survival::coxph(survival::Surv(start, stop, status) ~ lbrt + age, data = cp)
#> Call:
#> survival::coxph(formula = survival::Surv(start, stop, status) ~
#> lbrt + age, data = cp)
#>
#> coef exp(coef) se(coef) z p
#> lbrt 3.49468 32.93982 2.02534 1.725 0.0844
#> age -0.06233 0.93957 0.06969 -0.894 0.3711
#>
#> Likelihood ratio test=13.98 on 2 df, p=0.0009227
#> n= 52, number of events= 8