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Multivariable Fine-Gray Subdistribution Hazard Regression

Source: R/tbl_fine_gray_multi.R
tbl_fine_gray_multi.Rd

Fits one Fine-Gray (1999) proportional subdistribution hazards model of a cause with several predictors at once, using `cmprsk::crr()`. It is the adjusted counterpart of [tbl_fine_gray_single()], as [cox_multi()] is for [cox_single()]. The estimates are subdistribution hazard ratios (see [tbl_fine_gray_single()] for how to read them).

Usage

tbl_fine_gray_multi(
  data,
  time_col,
  event_col,
  formula,
  failcode = 1,
  cencode = 0,
  conf_level = 0.95,
  exponentiate = TRUE,
  ...
)

Arguments

data

A data frame, one row per subject.

time_col

Follow-up time.

event_col

Event type, as in [tbl_cumulative_incidence()]: a censoring code, then one code per cause.

formula

One-sided formula of the predictors, for example `~ age + arm + sex`.

failcode

Code of the cause of interest (default 1).

cencode

Code for censoring (default 0).

conf_level

Confidence level.

exponentiate

Report the subdistribution hazard ratio (default); `FALSE` gives the coefficient on the log scale.

...

Passed to `cmprsk::crr()` (for example `maxiter`).

Value

A tibble with `term`, `estimate`, `std.error` (of the log coefficient), `statistic`, `p.value`, `conf.low`, `conf.high`, `n` and `n_event`.

Rows used

Rows with a missing time, event code or any variable in `formula` are dropped (complete cases). The design matrix comes from `stats::model.matrix()`, so factors, logicals, interactions and transforms such as `log(x)` or `I(x^2)` work, and terms are named as `broom::tidy()` names them.

See also

[tbl_fine_gray_single()], [tbl_fine_gray_screen()], [tbl_cumulative_incidence()]

Examples

if (requireNamespace("cmprsk", quietly = TRUE)) {
  set.seed(1)
  d <- data.frame(time = rexp(150, 0.1), type = sample(0:2, 150, TRUE, prob = c(0.3, 0.4, 0.3)),
                  age = rnorm(150, 60, 8), arm = rep(c("A", "B"), 75))
  tbl_fine_gray_multi(d, "time", "type", ~ age + arm)
}
#> # A tibble: 2 × 9
#>   term  estimate std.error statistic p.value conf.low conf.high     n n_event
#>   <chr>    <dbl>     <dbl>     <dbl>   <dbl>    <dbl>     <dbl> <int>   <int>
#> 1 age      1.00     0.0141     0.321   0.748    0.977      1.03   150      69
#> 2 armB     0.829    0.236     -0.796   0.426    0.522      1.32   150      69