tern (version 0.9.8)

tidy_coxreg: Custom tidy methods for Cox regression

Description

Usage

# S3 method for summary.coxph
tidy(x, ...)
# S3 method for coxreg.univar
tidy(x, ...)
# S3 method for coxreg.multivar
tidy(x, ...)

Value

broom::tidy() returns:

• For summary.coxph objects, a data.frame with columns: Pr(>|z|), exp(coef), exp(-coef), lower .95, upper .95, level, and n.

• For coxreg.univar objects, a data.frame with columns: effect, term, term_label, level, n, hr, lcl, ucl, pval, and ci.

• For coxreg.multivar objects, a data.frame with columns: term, pval, term_label, hr, lcl, ucl, level, and ci.

Arguments

x

(list)
result of the Cox regression model fitted by fit_coxreg_univar() (for univariate models) or fit_coxreg_multivar() (for multivariate models).

...

additional arguments for the lower level functions.

Functions

• tidy(summary.coxph): Custom tidy method for survival::coxph() summary results.

  Tidy the survival::coxph() results into a data.frame to extract model results.

• tidy(coxreg.univar): Custom tidy method for a univariate Cox regression.

  Tidy up the result of a Cox regression model fitted by fit_coxreg_univar().

• tidy(coxreg.multivar): Custom tidy method for a multivariate Cox regression.

  Tidy up the result of a Cox regression model fitted by fit_coxreg_multivar().

See Also

cox_regression

Examples

library(survival)
library(broom)

set.seed(1, kind = "Mersenne-Twister")

dta_bladder <- with(
  data = bladder[bladder$enum < 5, ],
  data.frame(
    time = stop,
    status = event,
    armcd = as.factor(rx),
    covar1 = as.factor(enum),
    covar2 = factor(
      sample(as.factor(enum)),
      levels = 1:4, labels = c("F", "F", "M", "M")
    )
  )
)
labels <- c("armcd" = "ARM", "covar1" = "A Covariate Label", "covar2" = "Sex (F/M)")
formatters::var_labels(dta_bladder)[names(labels)] <- labels
dta_bladder$age <- sample(20:60, size = nrow(dta_bladder), replace = TRUE)

formula <- "survival::Surv(time, status) ~ armcd + covar1"
msum <- summary(coxph(stats::as.formula(formula), data = dta_bladder))
tidy(msum)

## Cox regression: arm + 1 covariate.
mod1 <- fit_coxreg_univar(
  variables = list(
    time = "time", event = "status", arm = "armcd",
    covariates = "covar1"
  ),
  data = dta_bladder,
  control = control_coxreg(conf_level = 0.91)
)

## Cox regression: arm + 1 covariate + interaction, 2 candidate covariates.
mod2 <- fit_coxreg_univar(
  variables = list(
    time = "time", event = "status", arm = "armcd",
    covariates = c("covar1", "covar2")
  ),
  data = dta_bladder,
  control = control_coxreg(conf_level = 0.91, interaction = TRUE)
)

tidy(mod1)
tidy(mod2)

multivar_model <- fit_coxreg_multivar(
  variables = list(
    time = "time", event = "status", arm = "armcd",
    covariates = c("covar1", "covar2")
  ),
  data = dta_bladder
)
broom::tidy(multivar_model)