Kaplan-Meier Estimates vs. a Continuous Variable

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 K-M. Confidence intervals are based on -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 survfitKM in the survival package to get Kaplan-Meiers estimates and standard errors.

Usage

groupkm(x, Srv, m=50, g, cuts, u, 
        pl=FALSE, loglog=FALSE, conf.int=.95, xlab, ylab,
        lty=1, add=FALSE, cex.subtitle=.7, ...)

Arguments

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

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 KM)

See also

survfit, errbar, cut2, Surv, units

Examples

require(survival)
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)

#>            x   n events      KM  std.err
#>  [1,] 27.917 100      4 0.94651 0.027727
#>  [2,] 36.560 100      6 0.95640 0.022420
#>  [3,] 40.794 100     13 0.89594 0.036899
#>  [4,] 44.157 100     11 0.91388 0.034117
#>  [5,] 47.342 100     18 0.86132 0.045553
#>  [6,] 50.884 100     10 0.92282 0.030492
#>  [7,] 53.931 100     18 0.87666 0.041909
#>  [8,] 57.650 100     18 0.86837 0.045158
#>  [9,] 62.185 100     12 0.93819 0.028810
#> [10,] 70.899 100     29 0.72407 0.070133
#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.