groupkm {Design} | R Documentation

## Kaplan-Meier Estimates vs. a Continuous Variable

### Description

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.

### 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-log `KM`)

### See Also

`survfit.km`, `errbar`, `cut2`, `Surv`, `units`

### 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.

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