Transplant survival times of 1439 kidney recipients in eight UK transplant centres (Collett, 2023, Example 11.4).
Format
A data frame with 1439 rows and 9 variables:
- patient
Patient identifier.
- centre
Transplant centre (1-8).
- tsurv
Transplant survival time in days.
- tcens
Event indicator: 1 = transplant failure, 0 = right-censored.
- dage
Age of the donor in years.
- dtype
Donor type: 0 = donation after brain death (DBD), 1 = donation after circulatory death (DCD).
- rage
Age of the recipient in years.
- diab
Diabetic status of the recipient: 0 = absent, 1 = present.
- cit
Cold ischaemic time, in hours.
Source
Collett, D. (2023) Modelling Survival Data in Medical Research, 4th ed. Chapman & Hall/CRC, Example 11.4 (Table 11.4). Data from the UK Transplant Registry.
Details
The data cover transplants with organs from deceased donors performed
between January 2009 and December 2011. Transplant survival is the earlier
of graft failure and patient death, and there are 206 failures. One
recipient (patient 7) had a kidney that never functioned, so tsurv is 0.
The data are used to compare centre-specific one-year failure rates, both unadjusted and risk-adjusted through a Cox model, and to estimate centre-specific rates with fixed and random effects (Examples 11.4-11.8).
Aliases
The data set is available under the aliases d11.4, d11.5, d11.6,
d11.7, d11.8; 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 11.4-11.8: comparison of transplant centres
survival::coxph(
survival::Surv(tsurv, tcens) ~ strata(centre) + dage + dtype + rage + diab + cit,
data = kidney_centres
)
#> Call:
#> survival::coxph(formula = survival::Surv(tsurv, tcens) ~ strata(centre) +
#> dage + dtype + rage + diab + cit, data = kidney_centres)
#>
#> coef exp(coef) se(coef) z p
#> dage 0.026047 1.026389 0.005690 4.578 4.69e-06
#> dtype 0.294704 1.342729 0.151170 1.949 0.0512
#> rage 0.001641 1.001642 0.006336 0.259 0.7957
#> diab -0.086793 0.916867 0.272139 -0.319 0.7498
#> cit 0.009317 1.009360 0.015779 0.590 0.5549
#>
#> Likelihood ratio test=34.03 on 5 df, p=2.347e-06
#> n= 1439, number of events= 206
# A random (frailty) effect for centre instead of stratification
survival::coxph(
survival::Surv(tsurv, tcens) ~ dage + dtype + rage + diab + cit +
frailty(centre, distribution = "gamma"),
data = kidney_centres
)
#> Call:
#> survival::coxph(formula = survival::Surv(tsurv, tcens) ~ dage +
#> dtype + rage + diab + cit + frailty(centre, distribution = "gamma"),
#> data = kidney_centres)
#>
#> coef se(coef) se2 Chisq DF p
#> dage 0.02423 0.00552 0.00549 19.28562 1.00 1.1e-05
#> dtype 0.22625 0.14608 0.14424 2.39877 1.00 0.12
#> rage 0.00188 0.00626 0.00625 0.09011 1.00 0.76
#> diab -0.12275 0.26974 0.26924 0.20708 1.00 0.65
#> cit 0.01370 0.01514 0.01502 0.81846 1.00 0.37
#> frailty(centre, distribut 3.56307 2.18 0.19
#>
#> Iterations: 8 outer, 33 Newton-Raphson
#> Variance of random effect= 0.0176 I-likelihood = -1432.9
#> Degrees of freedom for terms= 1.0 1.0 1.0 1.0 1.0 2.2
#> Likelihood ratio test=36.2 on 7.12 df, p=7e-06
#> n= 1439, number of events= 206
# Modern tools --------------------------------------------------------
if (requireNamespace("coxme", quietly = TRUE)) {
coxme::coxme(
survival::Surv(tsurv, tcens) ~ dage + dtype + rage + diab + cit +
(1 | centre),
data = kidney_centres
)
}
#> Cox mixed-effects model fit by maximum likelihood
#> Data: kidney_centres
#> events, n = 206, 1439
#> Iterations= 12 63
#> NULL Integrated Fitted
#> Log-likelihood -1448.278 -1432.681 -1429.593
#>
#> Chisq df p AIC BIC
#> Integrated loglik 31.19 6.00 2.3271e-05 19.19 -0.77
#> Penalized loglik 37.37 7.64 7.1883e-06 22.08 -3.35
#>
#> Model: survival::Surv(tsurv, tcens) ~ dage + dtype + rage + diab + cit + (1 | centre)
#> Fixed coefficients
#> coef exp(coef) se(coef) z p
#> dage 0.024404709 1.0247049 0.005532123 4.41 0.00001
#> dtype 0.234509388 1.2642883 0.146436494 1.60 0.11000
#> rage 0.001891275 1.0018931 0.006265974 0.30 0.76000
#> diab -0.118259648 0.8884653 0.269980456 -0.44 0.66000
#> cit 0.013156210 1.0132431 0.015199260 0.87 0.39000
#>
#> Random effects
#> Group Variable Std Dev Variance
#> centre Intercept 0.15895389 0.02526634