npsurv {rms} | R Documentation

## Nonparametric Survival Estimates for Censored Data

### Description

Computes an estimate of a survival curve for censored data using either the Kaplan-Meier or the Fleming-Harrington method or computes the predicted survivor function. For competing risks data it computes the cumulative incidence curve. This calls the `survival` package's `survfit.formula` function. Attributes of the event time variable are saved (label and units of measurement).

For competing risks the second argument for `Surv` should be the event state variable, and it should be a factor variable with the first factor level denoting right-censored observations.

### Usage

    npsurv(formula, data, subset, na.action, ...)

### Arguments

`formula` | a formula object, which must have a `Surv` object as the response on the left of the `~` operator and, if desired, terms separated by + operators on the right. One of the terms may be a `strata` object. For a single survival curve the right hand side should be `~ 1`.
`data,subset,na.action` | see `survfit.formula`
`...` | see `survfit.formula`

### Details

see `survfit.formula` for details

### Value

an object of class `"npsurv"` and `"survfit"`. See `survfit.object` for details. Methods defined for `survfit` objects are `print`, `summary`, `plot`,`lines`, and `points`.

### Author(s)

Thomas Lumley tlumley@u.washington.edu and Terry Therneau

### See Also

`survfit.cph` for survival curves from Cox models. `print`, `plot`, `lines`, `coxph`, `strata`, `survplot`

### Examples

    require(survival)
    # fit a Kaplan-Meier and plot it
    fit <- npsurv(Surv(time, status) ~ x, data = aml)
    plot(fit, lty = 2:3)
    legend(100, .8, c("Maintained", "Nonmaintained"), lty = 2:3)

    # Here is the data set from Turnbull
    #  There are no interval censored subjects, only left-censored (status=3),
    #  right-censored (status 0) and observed events (status 1)
    #
    #                             Time
    #                         1    2   3   4
    # Type of observation
    #           death        12    6   2   3
    #          losses         3    2   0   3
    #      late entry         2    4   2   5
    #
    tdata <- data.frame(time   = c(1,1,1,2,2,2,3,3,3,4,4,4),
                        status = rep(c(1,0,2),4),
                        n      = c(12,3,2,6,2,4,2,0,2,3,3,5))
    fit  <- npsurv(Surv(time, time, status, type='interval') ~ 1,
                   data=tdata, weights=n)

    #
    # Time to progression/death for patients with monoclonal gammopathy
    # Competing risk curves (cumulative incidence)
    # status variable must be a factor with first level denoting right censoring
    m <- upData(mgus1, stop = stop / 365.25, units=c(stop='years'),
                labels=c(stop='Follow-up Time'), subset=start == 0)
    f <- npsurv(Surv(stop, event) ~ 1, data=m)

    # CI curves are always plotted from 0 upwards, rather than 1 down
    plot(f, fun='event', xmax=20, mark.time=FALSE,
         col=2:3, xlab="Years post diagnosis of MGUS")
    text(10, .4, "Competing Risk: death", col=3)
    text(16, .15,"Competing Risk: progression", col=2)

    # Use survplot for enhanced displays of cumulative incidence curves for
    # competing risks

    survplot(f, state='pcm', n.risk=TRUE, xlim=c(0, 20), ylim=c(0, .5), col=2)
    survplot(f, state='death', add=TRUE, col=3)

    f <- npsurv(Surv(stop, event) ~ sex, data=m)
    survplot(f, state='death', n.risk=TRUE, conf='diffbands')

[Package rms version 5.1-2 Index]