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Fitting functions for Cox proportional hazards regression

Source: R/cox_regression.R
fit_coxreg.Rd

[Stable]

Fitting functions for univariate and multivariate Cox regression models.

Usage 

fit_coxreg_univar(variables, data, at = list(), control = control_coxreg())

fit_coxreg_multivar(variables, data, control = control_coxreg())

Arguments

variables

(named list)
the names of the variables found in data, passed as a named list and corresponding to the time, event, arm, strata, and covariates terms. If arm is missing from variables, then only Cox model(s) including the covariates will be fitted and the corresponding effect estimates will be tabulated later.

data

(data.frame)
the dataset containing the variables to fit the models.

at

(list of numeric)
when the candidate covariate is a numeric, use at to specify the value of the covariate at which the effect should be estimated.

control

(list)
a list of parameters as returned by the helper function control_coxreg().

Value

  • fit_coxreg_univar() returns a coxreg.univar class object which is a named list with 5 elements:

    • mod: Cox regression models fitted by survival::coxph().

    • data: The original data frame input.

    • control: The original control input.

    • vars: The variables used in the model.

    • at: Value of the covariate at which the effect should be estimated.

  • fit_coxreg_multivar() returns a coxreg.multivar class object which is a named list with 4 elements:

    • mod: Cox regression model fitted by survival::coxph().

    • data: The original data frame input.

    • control: The original control input.

    • vars: The variables used in the model.

Functions

  • fit_coxreg_univar(): Fit a series of univariate Cox regression models given the inputs.

  • fit_coxreg_multivar(): Fit a multivariate Cox regression model.

Note

When using fit_coxreg_univar there should be two study arms.

See also

h_cox_regression for relevant helper functions, cox_regression.

Examples 

library(survival)

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

# Testing dataset [survival::bladder].
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)

plot(
  survfit(Surv(time, status) ~ armcd + covar1, data = dta_bladder),
  lty = 2:4,
  xlab = "Months",
  col = c("blue1", "blue2", "blue3", "blue4", "red1", "red2", "red3", "red4")
)


# fit_coxreg_univar

## 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)
)

## Cox regression: arm + 1 covariate, stratified analysis.
mod3 <- fit_coxreg_univar(
  variables = list(
    time = "time", event = "status", arm = "armcd", strata = "covar2",
    covariates = c("covar1")
  ),
  data = dta_bladder,
  control = control_coxreg(conf_level = 0.91)
)

## Cox regression: no arm, only covariates.
mod4 <- fit_coxreg_univar(
  variables = list(
    time = "time", event = "status",
    covariates = c("covar1", "covar2")
  ),
  data = dta_bladder
)

# fit_coxreg_multivar

## Cox regression: multivariate Cox regression.
multivar_model <- fit_coxreg_multivar(
  variables = list(
    time = "time", event = "status", arm = "armcd",
    covariates = c("covar1", "covar2")
  ),
  data = dta_bladder
)

# Example without treatment arm.
multivar_covs_model <- fit_coxreg_multivar(
  variables = list(
    time = "time", event = "status",
    covariates = c("covar1", "covar2")
  ),
  data = dta_bladder
)