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.
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
VALUE:
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)
SEE ALSO:
,
,
,
,
EXAMPLES:
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.