Time to the first serious infection for 128 patients with chronic granulomatous disease (CGD) in a multicentre trial (Collett, 2023, Appendix B.5).
Format
A data frame with 128 rows and 12 variables:
- patient
Patient number (1-128).
- time
Time to the first serious infection, in days.
- status
Event indicator: 1 = infection, 0 = censored.
- centre
Trial centre (1-14; centre 8 does not occur in these data).
- treat
Treatment: 0 = placebo, 1 = interferon.
- age
Age in years.
- sex
Sex: 1 = male, 2 = female.
- height
Height in cm.
- weight
Weight in kg.
- pattern
Pattern of inheritance: 1 = X-linked, 2 = autosomal recessive.
- cort
Use of corticosteroids at trial entry: 1 = used, 2 = not used.
- anti
Use of antibiotics at trial entry: 1 = used, 2 = not used.
Source
Collett, D. (2023) Modelling Survival Data in Medical Research, 4th ed. Chapman & Hall/CRC, Appendix B.5. Data described by Therneau and Grambsch (2000); the trial was carried out by Genentech, Inc.
Details
CGD is a group of inherited disorders that leave patients vulnerable to recurrent infections. The trial compared the antiviral drug interferon with placebo, and the endpoint is the time to the first serious infection after randomisation; there are 44 infections. The data were described by Therneau and Grambsch (2000); this is the 128-patient version used in the book.
The appendix suggests a wide range of analyses: model selection and model
checking, consistency of the treatment effect across centres, a
constructed interval-censored version of the data, shared frailty models
with centre as a random effect, risk-adjusted centre-specific infection
rates, and an inverse probability of censoring weighting analysis for
dependent censoring. The infection data are also available, in a larger
form, as cgd in the survival package.
Aliases
This data set is not used in a numbered example, so it has no dX.Y alias.
See dataset_by_exercise for the complete index.
Examples
# Collett (2023), Appendix B.5 (no numbered example) ------------------
# Chapter 17 style: Cox model for the first serious infection
survival::coxph(
survival::Surv(time, status) ~ factor(treat) + age + sex + height + weight,
data = cgd
)
#> Call:
#> survival::coxph(formula = survival::Surv(time, status) ~ factor(treat) +
#> age + sex + height + weight, data = cgd)
#>
#> coef exp(coef) se(coef) z p
#> factor(treat)1 -1.166933 0.311320 0.341088 -3.421 0.000623
#> age -0.070315 0.932101 0.039244 -1.792 0.073173
#> sex 0.079491 1.082735 0.426429 0.186 0.852124
#> height -0.005703 0.994313 0.011425 -0.499 0.617647
#> weight 0.026476 1.026829 0.019186 1.380 0.167609
#>
#> Likelihood ratio test=16.79 on 5 df, p=0.004923
#> n= 128, number of events= 44
# A shared frailty for trial centre
survival::coxph(
survival::Surv(time, status) ~ factor(treat) + age + frailty(centre),
data = cgd
)
#> Call:
#> survival::coxph(formula = survival::Surv(time, status) ~ factor(treat) +
#> age + frailty(centre), data = cgd)
#>
#> coef se(coef) se2 Chisq DF p
#> factor(treat)1 -1.16e+00 3.37e-01 3.37e-01 1.18e+01 1 0.0006
#> age -2.83e-02 1.71e-02 1.71e-02 2.73e+00 1 0.0985
#> frailty(centre) 1.42e-05 0 0.9430
#>
#> Iterations: 6 outer, 23 Newton-Raphson
#> Variance of random effect= 5e-07 I-likelihood = -186.8
#> Degrees of freedom for terms= 1 1 0
#> Likelihood ratio test=14.7 on 2 df, p=6e-04
#> n= 128, number of events= 44
# Modern tools --------------------------------------------------------
if (requireNamespace("coxme", quietly = TRUE)) {
coxme::coxme(
survival::Surv(time, status) ~ factor(treat) + age + (1 | centre),
data = cgd
)
}
#> Cox mixed-effects model fit by maximum likelihood
#> Data: cgd
#> events, n = 44, 128
#> Iterations= 2 12
#> NULL Integrated Fitted
#> Log-likelihood -194.1074 -186.7525 -186.7393
#>
#> Chisq df p AIC BIC
#> Integrated loglik 14.71 3.00 0.0020822 8.71 3.36
#> Penalized loglik 14.74 2.01 0.0006437 10.71 7.11
#>
#> Model: survival::Surv(time, status) ~ factor(treat) + age + (1 | centre)
#> Fixed coefficients
#> coef exp(coef) se(coef) z p
#> factor(treat)1 -1.15734667 0.3143191 0.33741057 -3.43 0.0006
#> age -0.02831933 0.9720779 0.01714062 -1.65 0.0980
#>
#> Random effects
#> Group Variable Std Dev Variance
#> centre Intercept 0.0199810065 0.0003992406