utile.tables (version 0.3.0)

build_model.coxph: Build Cox PH models

Description

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.

Usage

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

Value

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

Arguments

.object

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.

.mv

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

.test

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

.col.test

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

.level

A double. The confidence level required.

.stat.pct.sign

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

.digits

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

.p.digits

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.

See Also

build_model

Examples

library(survival)
library(dplyr)

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)