Skip to contents

Models specified terms in model data against an existing model and returns a clean, human readable table of summarizing the effects and statistics for the newly generated model. This functions greatly simplifies fitting a large number of variables against a set of time-to-event data.


# S3 method for coxph
  .mv = FALSE,
  .test = c("LRT", "Wald"),
  .col.test = FALSE,
  .level = 0.95,
  .stat.pct.sign = TRUE,
  .digits = 1,
  .p.digits = 4



An object of class coxph.


One or more unquoted expressions separated by commas representing columns in the model data.frame. May be specified using tidyselect helpers.


A logical. Fit all terms into a single multivariable model. If left FALSE, all terms are fit in their own univariate models.


A character. The name of a stats::drop1 test to use with the model.


A logical. Append a columns for the test and accompanying statistic used to derive the p-value.


A double. The confidence level required.


A logical. Paste a percent symbol after all reported frequencies.


An integer. The number of digits to round numbers to.


An integer. The number of p-value digits to report. Note that the p-value still rounded to the number of digits specified in .digits.


An object of class data.frame summarizing the provided object. If the tibble package has been installed, a tibble will be returned.

See also


#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>     filter, lag
#> The following objects are masked from 'package:base':
#>     intersect, setdiff, setequal, union

data_lung <- lung |>
  mutate_at(vars(inst, status, sex), as.factor) |>
  mutate(status = case_when(status == 1 ~ 0, status == 2 ~ 1))

fit <- coxph(Surv(time, status) ~ 1, data = data_lung)

# Create a univariate model for each variable
fit |> build_model(sex, age)
#> Error in eval(.object$call$data): object 'data_lung' not found