| groupkm {Design} | R Documentation |
Function to divide x (e.g. age, or predicted survival at time u created by
survest) into g quantile groups, get Kaplan-Meier estimates at time u
(a scaler), and to return a matrix with columns x=mean x in
quantile, n=number of subjects, events=no. events, and
KM=K-M survival at time u,
std.err = s.e. of log-log K-M. Confidence intervals are based on
log-log S(t).
Instead of supplying g, the user can supply the minimum number of subjects
to have
in the quantile group (m, default=50).
If cuts is given (e.g. cuts=c(0,.1,.2,...,.9,.1)), it overrides m and g.
Calls Therneau's survfit.km to get Kaplan-Meiers estimates and standard
errors.
groupkm(x, Srv, m=50, g, cuts, u,
pl=FALSE, loglog=FALSE, conf.int=.95, xlab, ylab,
lty=1, add=FALSE, cex.subtitle=.7, ...)
x |
variable to stratify |
Srv |
a "Surv" object - n x 2 matrix containing survival time and event/censoring 1/0 indicator. Units of measurement come from the "units" attribute of the survival time variable. "Day" is the default. |
m |
desired minimum number of observations in a group |
g |
number of quantile groups |
cuts |
actual cuts in x, e.g. c(0,1,2) to use [0,1), [1,2].
|
u |
time for which to estimate survival |
pl |
TRUE to plot results |
loglog |
set to TRUE to plot log(-log(survival)) instead of survival
|
conf.int |
defaults to .95 for 0.95 confidence bars. Set to FALSE to suppress bars.
|
xlab |
if pl=TRUE, is x-axis label. Default is label(x) or name of calling argument
|
ylab |
if pl=TRUE, is y-axis label. Default is constructed from u and time units
attribute.
|
lty |
line time for primary line connecting estimates |
add |
set to TRUE if adding to an existing plot
|
cex.subtitle |
character size for subtitle. Default is .7. Use FALSE to suppress subtitle.
|
... |
plotting parameters to pass to the plot and errbar functions |
matrix with columns named x (mean predictor value in interval), n (sample size
in interval), events (number of events in interval), KM (Kaplan-Meier
estimate), std.err (standard error of log-log KM)
survfit.km, errbar, cut2, Surv, units
n <- 1000 set.seed(731) age <- 50 + 12*rnorm(n) cens <- 15*runif(n) h <- .02*exp(.04*(age-50)) d.time <- -log(runif(n))/h label(d.time) <- 'Follow-up Time' e <- ifelse(d.time <= cens,1,0) d.time <- pmin(d.time, cens) units(d.time) <- "Year" groupkm(age, Surv(d.time, e), g=10, u=5, pl=TRUE) #Plot 5-year K-M survival estimates and 0.95 confidence bars by #decile of age. If omit g=10, will have >= 50 obs./group.