survfit_tidiers {broom} | R Documentation

## tidy survival curve fits

### Description

Construct tidied data frames showing survival curves over time.

### Usage

    ## S3 method for class 'survfit'
    tidy(x, ...)

    ## S3 method for class 'survfit'
    glance(x, ...)

### Arguments

`x` | "survfit" object
`...` | extra arguments, not used

### Details

`glance` does not work on multi-state survival curves, since the values `glance` outputs would be calculated for each state. `tidy` does work for multi-state survival objects, and includes a `state` column to distinguish between them.

### Value

All tidying methods return a data.frame without rownames, whose structure depends on the method chosen.

`tidy` returns a row for each time point, with columns

`time` | timepoint
`n.risk` | number of subjects at risk at time t0
`n.event` | number of events at time t
`n.censor` | number of censored events
`estimate` | estimate of survival or cumulative incidence rate when multistate
`std.error` | standard error of estimate
`conf.high` | upper end of confidence interval
`conf.low` | lower end of confidence interval
`state` | state if multistate survfit object inputted
`strata` | strata if stratified survfit object inputted

`glance` returns one-row data.frame with the columns displayed by `print.survfit`

`records` | number of observations
`n.max` | n.max
`n.start` | n.start
`events` | number of events
`rmean` | Restricted mean (see print.survfit)
`rmean.std.error` | Restricted mean standard error
`median` | median survival
`conf.low` | lower end of confidence interval on median
`conf.high` | upper end of confidence interval on median

### Examples

    if (require("survival", quietly = TRUE)) {
        cfit <- coxph(Surv(time, status) ~ age + sex, lung)
        sfit <- survfit(cfit)

        head(tidy(sfit))
        glance(sfit)

        library(ggplot2)
        ggplot(tidy(sfit), aes(time, estimate)) + geom_line() +
            geom_ribbon(aes(ymin=conf.low, ymax=conf.high), alpha=.25)

        # multi-state
        fitCI <- survfit(Surv(stop, status * as.numeric(event), type = "mstate") ~ 1,
                      data = mgus1, subset = (start == 0))
        td_multi <- tidy(fitCI)
        head(td_multi)
        tail(td_multi)
        ggplot(td_multi, aes(time, estimate, group = state)) +
            geom_line(aes(color = state)) +
            geom_ribbon(aes(ymin = conf.low, ymax = conf.high), alpha = .25)

        # perform simple bootstrapping
        library(dplyr)
        bootstraps <- lung %>% bootstrap(100) %>%
            do(tidy(survfit(coxph(Surv(time, status) ~ age + sex, .))))

        ggplot(bootstraps, aes(time, estimate, group = replicate)) +
            geom_line(alpha = .25)

        bootstraps_bytime <- bootstraps %>% group_by(time) %>%
            summarize(median = median(estimate),
                      low = quantile(estimate, .025),
                      high = quantile(estimate, .975))

        ggplot(bootstraps_bytime, aes(x = time, y = median)) + geom_line() +
            geom_ribbon(aes(ymin = low, ymax = high), alpha = .25)

        # bootstrap for median survival
        glances <- lung %>%
            bootstrap(100) %>%
            do(glance(survfit(coxph(Surv(time, status) ~ age + sex, .))))

        glances

        qplot(glances$median, binwidth = 15)
        quantile(glances$median, c(.025, .975))
    }

[Package broom version 0.4.2 Index]