poissonize: Transform survival data for fitting a Poisson model
In surrosurv: Evaluation of Failure Time Surrogate Endpoints in Individual Patient Data Meta-Analyses
poissonize 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:
id : the patient identifier
time : the event/censoring time
status : the event(1) or censoring(0) indicator
... : other factors such like the covariables needed
in the regression model
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 all.breaks is specified
nInts
the number of intervals containing the same expected number of events
(used only if is.null(interval.width), see Details).
This parameter is ignored if either all.breaks
or interval.width is specified
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)
Federico Rotolo [aut] (<https://orcid.org/0000-0003-4837-6501>),
Xavier Paoletti [ctb],
Marc Buyse [ctb],
Tomasz Burzykowski [ctb],
Stefan Michiels [ctb] (<https://orcid.org/0000-0002-6963-2968>),
Dan Chaltiel [cre] (<https://orcid.org/0000-0003-3488-779X>)
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.
\Sexpr[results=rd]{tools:::Rd_expr_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)
surrosurv documentation built on April 14, 2023, 9:09 a.m.
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| poissonize | 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)
Federico Rotolo [aut] (<https://orcid.org/0000-0003-4837-6501>), Xavier Paoletti [ctb], Marc Buyse [ctb], Tomasz Burzykowski [ctb], Stefan Michiels [ctb] (<https://orcid.org/0000-0002-6963-2968>), Dan Chaltiel [cre] (<https://orcid.org/0000-0003-3488-779X>)
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. \Sexpr[results=rd]{tools:::Rd_expr_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)
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