Time from registration to death on the waiting list for 281 adults with primary biliary cirrhosis (Collett, 2023, Example 14.1).
Format
A data frame with 281 rows and 7 variables:
- patient
Patient identifier.
- time
Time from registration to death on the waiting list, in days.
- status
Event indicator: 1 = died while waiting, 0 = censored (usually at transplantation).
- age
Age of the patient in years.
- gender
Gender: 0 = female, 1 = male.
- bmi
Body mass index, in kg/m squared.
- ukeld
UK end-stage liver disease score; higher values indicate more severe disease.
Source
Collett, D. (2023) Modelling Survival Data in Medical Research, 4th ed. Chapman & Hall/CRC, Example 14.1 (Table 14.1). Data from the UK Transplant Registry.
Details
The patients were first registered for a liver transplant in the five years from 1 January 2006, and there are 64 deaths. The time of a patient who receives a transplant is censored at transplantation. Livers are allocated on the basis of need, so the time to transplantation (censoring) is associated with the time to death; the data are used to illustrate sensitivity analysis for dependent censoring and inverse probability of censoring weighting (Examples 14.1 and 14.3). The rows are ordered by survival time.
See liver for the post-transplant graft survival data from the same registry.
Aliases
The data set is available under the aliases d14.1, d14.3; 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 -----------------------------------
# Examples 14.1 and 14.3: dependent censoring (transplant) modelled with
# inverse probability of censoring weights
liver_wait$cens <- 1 - liver_wait$status
cens_fit <- survival::survreg(
survival::Surv(time, cens) ~ age + gender + bmi + ukeld,
data = liver_wait
)
lp <- cens_fit$linear.predictors
sigma <- cens_fit$scale
# Probability of still being uncensored at each observed time
survival_uncensored <- exp(-exp((log(liver_wait$time) - lp) / sigma))
weights <- 1 / survival_uncensored
summary(weights)
#> Min. 1st Qu. Median Mean 3rd Qu. Max.
#> 1.005 1.251 1.658 4.439 2.823 231.618
survival::coxph(
survival::Surv(time, status) ~ age + gender + bmi + ukeld,
data = liver_wait, weights = weights
)
#> Call:
#> survival::coxph(formula = survival::Surv(time, status) ~ age +
#> gender + bmi + ukeld, data = liver_wait, weights = weights)
#>
#> coef exp(coef) se(coef) robust se z p
#> age -0.017620 0.982534 0.008467 0.041785 -0.422 0.6732
#> gender -0.313481 0.730898 0.372888 0.956187 -0.328 0.7430
#> bmi 0.016672 1.016812 0.015175 0.094157 0.177 0.8595
#> ukeld 0.221538 1.247994 0.020850 0.080131 2.765 0.0057
#>
#> Likelihood ratio test=156.2 on 4 df, p=< 2.2e-16
#> n= 281, number of events= 64