Broom methods for tidy cuminc objects

# S3 method for tidycuminc
tidy(x, times = NULL, conf.int = TRUE, conf.level = x$conf.level, ...)

# S3 method for tidycuminc
glance(x, ...)

Arguments

x: object of class 'tidycuminc'
times: Numeric vector of times to obtain risk estimates at
conf.int: Logical indicating whether or not to include a confidence interval in the tidied output. Defaults to FALSE.
conf.level: Level of the confidence interval. Default matches that in cuminc(conf.level=) (typically, 0.95)
...: not used

Value

a tibble

`tidy()` data frame

The returned tidy() data frame returns the following columns:

Column Name	Description
`outcome`	Competing Event Outcome
`time`	Numeric follow-up time
`estimate`	Risk estimate
`std.error`	Standard Error
`n.risk`	Number at risk at the specified time
`n.event`	If the `times=` argument is missing, then the number of events that occurred at time `t`. Otherwise, it is the cumulative number of events that have occurred since the last time listed.
`n.censor`	If the `times=` argument is missing, then the number of censored obs at time `t`. Otherwise, it is the cumulative number of censored obs that have occurred since the last time listed.
`cum.event`	Cumulative number of events at specified time
`cum.censor`	Cumulative number of censored observations at specified time

If tidy(time=) is specified, then n.event and n.censor are the cumulative number of events/censored in the interval. For example, if tidy(time = c(0, 12, 18)) is passed, n.event and n.censor at time = 18 are the cumulative number of events/censored in the interval (12, 18].

p-values

The p-values reported in cuminc(), glance.tidycuminc() and add_p.tbl_cuminc() are Gray's test as described in Gray RJ (1988) A class of K-sample tests for comparing the cumulative incidence of a competing risk, Annals of Statistics, 16:1141-1154.

Confidence intervals

The confidence intervals for cumulative incidence estimates use the recommended method in Competing Risks: A Practical Perspective by Melania Pintilie.

$$x^{exp(±z * se / (x * log(x)))}$$

where $x$ is the cumulative incidence estimate, $se$ is the standard error estimate, and $z$ is the z-score associated with the confidence level of the interval, e.g. $z = 1.96$ for a 95% CI.

Examples

cuminc <- cuminc(Surv(ttdeath, death_cr) ~ trt, trial)

tidy(cuminc)
#> # A tibble: 444 × 12
#>     time outcome     strata estimate std.error conf.low conf.high n.risk n.event
#>    <dbl> <chr>       <fct>     <dbl>     <dbl>    <dbl>     <dbl>  <int>   <int>
#>  1  0    death from… Drug A   0         0      NA         NA          98       0
#>  2  3.53 death from… Drug A   0         0      NA         NA          98       0
#>  3  5.33 death from… Drug A   0         0      NA         NA          97       0
#>  4  6.32 death from… Drug A   0         0      NA         NA          97       0
#>  5  7.27 death from… Drug A   0.0102    0.0102  8.84e-4    0.0503     97       1
#>  6  7.38 death from… Drug A   0.0204    0.0144  3.90e-3    0.0652     96       1
#>  7  8.37 death from… Drug A   0.0204    0.0144  3.90e-3    0.0652     95       0
#>  8  8.71 death from… Drug A   0.0204    0.0144  3.90e-3    0.0652     94       0
#>  9  9.24 death from… Drug A   0.0204    0.0144  3.90e-3    0.0652     94       0
#> 10  9.73 death from… Drug A   0.0204    0.0144  3.90e-3    0.0652     94       0
#> # ℹ 434 more rows
#> # ℹ 3 more variables: n.censor <int>, cum.event <int>, cum.censor <int>

glance(cuminc)
#> # A tibble: 1 × 8
#>   outcome_1    statistic_1  df_1 p.value_1 outcome_2 statistic_2  df_2 p.value_2
#>   <chr>              <dbl> <dbl>     <dbl> <chr>           <dbl> <dbl>     <dbl>
#> 1 death from …        1.99     1     0.159 death ot…      0.0886     1     0.766

# restructure glance to one line per outcome
glance(cuminc) %>%
  tidyr::pivot_longer(
    everything(),
    names_to = c(".value", "outcome_id"),
    names_pattern = "(.*)_(.*)"
  )
#> # A tibble: 2 × 5
#>   outcome_id outcome            statistic    df p.value
#>   <chr>      <chr>                  <dbl> <dbl>   <dbl>
#> 1 1          death from cancer     1.99       1   0.159
#> 2 2          death other causes    0.0886     1   0.766