| poissonize {surrosurv} | R Documentation |
Transform survival data for fitting a Poisson model
Description
This function transform survival data into a format compatible with
the glm() function for fitting an auxiliary Poisson model,
providing the parameter estimates of the associated proportional hazard model.
Usage
poissonize(data,
all.breaks = NULL, interval.width = NULL, nInts = 8,
factors = NULL, compress = TRUE)
plotsson(x, type = c('survival', 'hazard'),
add = FALSE, xscale = 1, by, col, ...)
Arguments
data |
a data frame with columns:
|
all.breaks |
the breakpoints between time intervals |
interval.width |
the width of the time intervals on which the risks will be assumed constant,
in case of intervals of the same length.
This parameter is ignored if |
nInts |
the number of intervals containing the same expected number of events
(used only if |
factors |
a vector of characters, containing the names of the factors to be kept in the transformed data set |
compress |
a logical, indicating whether the record with the same factor profile should be summarized into one record, i.e. whether the data should be expressed in a short form |
x |
The fitted Poisson model on the poissonized data |
type |
the type of plot, either 'haz' for the hazard function or 'Surv', for the survival curve |
add |
should the plot added to the active device? |
xscale |
scaling factor for the time (x) axis |
by |
covariate for which a different curve per level has to be plotted |
col, ... |
other graphical parameters |
Details
If interval.width is not null, the study period is divided into
equal-length intervals of length interval.width.
Otherwise, nInts intervals are used, and the location of their bounds
is computed based on the empirical quantiles of the survival function.
Note
This code is hugely inspired by original code made publicly available by Stephanie Kovalchik.
Author(s)
NA
References
Whitehead, J. Fitting Cox's regression model to survival data using GLIM. J Roy Stat Soc C Appl Stat 1980; 29(3):268-275. https://www.jstor.org/stable/2346901.
Crowther MJ, Riley RD, Staessen JA, Wang J, Gueyffier F, Lambert PC. Individual patient data meta-analysis of survival data using Poisson regression models. BMC Medical Research Methodology 2012; 12:34. doi: 10.1186/1471-2288-12-34.
Examples
################################################################################
# Example 1 - KIDNEY data #
################################################################################
library(survival)
data(kidney)
kidney <- kidney[1:(nrow(kidney)/2)*2,]
head(kidney)
par(mfrow=c(1, 3))
for (int in c(50, 20, 10)) {
head(wdata1 <- poissonize(kidney, interval.width = int,
factors = c('disease'), compress = FALSE))
head(wdata2 <- poissonize(kidney, interval.width = int,
factors = c('disease'), compress = TRUE))
fitcox <- (coxph(Surv(time, status) ~ disease, data = kidney))
fitpoi1 <- glm(event ~ -1 + interval + disease + offset(log(time)),
data = wdata1, family = 'poisson')
fitpoi2 <- glm(m ~ -1 + interval + offset(log(Rt)) + disease,
data = wdata2, family = 'poisson')
cox.base <- basehaz(fitcox, centered = FALSE)
plot(stepfun(cox.base$time[-nrow(cox.base)], exp(-cox.base$hazard)),
ylim = 0:1, xlim = c(0, max(cox.base$time)),
do.points = FALSE, verticals = FALSE, xaxs = 'i',
main = paste0('KIDNEY data set\nInterval width = ', int),
xlab = 'Time', ylab = 'Survival probability')
plotsson(fitpoi1, 'Surv', add = TRUE, col = 2, lty = 2)
plotsson(fitpoi2, 'Surv', add = TRUE, col = 3, lty = 3)
legend('topright', col = 1:3, lty = 1:3,
legend = c('Breslow (Cox)', 'Poisson',
'Poisson (compressed dataset)'))
}
print(cbind(Cox = coef(fitcox),
Poisson = rev(rev(coef(fitpoi1))[1:3]),
Poisson_Compressed = rev(rev(coef(fitpoi2))[1:3])), digits = 2)
################################################################################
# Example 2 - COLON data #
################################################################################
library(survival)
data(colon)
head(wdata1 <- poissonize(subset(colon, etype == 1), interval.width = 365.25,
factors=c('surg', 'sex', 'age'), compress = FALSE))
head(wdata2 <- poissonize(subset(colon, etype == 1), interval.width = 365.25,
factors=c('surg', 'sex', 'age'), compress = TRUE))
fitcox <- coxph(Surv(time, status) ~ surg + sex + age,
data = subset(colon, etype == 1))
system.time({
fitpoi1 <- glm(event ~ -1 + interval + surg + sex + age + offset(log(time)),
data = wdata1, fam = 'poisson')
})
system.time({
fitpoi2 <- glm(m ~ -1 + interval + offset(log(Rt)) + surg + sex + age,
data = wdata2, family = 'poisson')
})
{
cox.base <- basehaz(fitcox, centered = FALSE)
par(mfrow = c(1, 1))
plot(stepfun(cox.base$time[-nrow(cox.base)], exp(-cox.base$hazard)),
ylim = 0:1, xlim = c(0, max(cox.base$time)),
do.points = FALSE, verticals = FALSE, xaxs = 'i',
main = 'COLON data set', xlab = 'Time', ylab = 'Survival probability')
plotsson(fitpoi1, 'Surv', add = TRUE, col = 2, lty = 2)
plotsson(fitpoi2, 'Surv', add = TRUE, col = 3, lty = 3)
legend('topright', col = 1:3, lty = 1:3,
legend = c('Cox', 'Poisson', 'Poisson (compressed dataset)'))
}
print(cbind(Cox = coef(fitcox),
Poisson = rev(rev(coef(fitpoi1))[1:3]),
Poisson_Compressed = rev(rev(coef(fitpoi2))[1:3])), digits = 2)
################################################################################
# Example 3 - LUNG data #
################################################################################
library(survival)
data(lung)
lung$status <- lung$status - 1
lung$id <- 1:nrow(lung)
head(wdata1 <- poissonize(lung, interval.width = 365.25/12,
factors = c('pat.karno', 'sex', 'age'),
compress = FALSE))
head(wdata2 <- poissonize(lung, interval.width = 365.25/12,
factors = c('pat.karno', 'sex', 'age'),
compress = TRUE))
fitcox <- coxph(Surv(time, status) ~ pat.karno + sex + age, data = lung)
system.time({
fitpoi1 <- glm(event ~ -1 + interval + pat.karno + sex + age +
offset(log(time)),
data = wdata1, family = 'poisson')
})
system.time({
fitpoi2 <- glm(m ~ -1 + interval + pat.karno + sex + age + offset(log(Rt)),
data = wdata2, family = 'poisson')
})
{
cox.base <- basehaz(fitcox, centered = FALSE)
plot(stepfun(cox.base$time[-nrow(cox.base)], exp(-cox.base$hazard)),
ylim = 0:1, xlim = c(0, max(cox.base$time)),
do.points = FALSE, verticals = FALSE, xaxs = 'i',
main = 'LUNG data set', xlab = 'Time', ylab = 'Survival probability')
plotsson(fitpoi1, 'Surv', add = TRUE, col = 2, lty = 2)
plotsson(fitpoi2, 'Surv', add = TRUE, col = 3, lty = 3)
legend('topright', col = 1:3, lty = 1:3,
legend = c('Cox', 'Poisson', 'Poisson (compressed dataset)'))
}
print(cbind(Cox = coef(fitcox),
Poisson = rev(rev(coef(fitpoi1))[1:3]),
Poisson_Compressed = rev(rev(coef(fitpoi2))[1:3])), digits = 2)