clinpubr (version 1.1.1)

subgroup_forest: Create subgroup forest plot.

Description

Create subgroup forest plot with glm or coxph models. The interaction p-values are calculated using likelihood ratio tests.

Usage

subgroup_forest(
  data,
  subgroup_vars,
  x,
  y,
  time = NULL,
  time2 = NULL,
  standardize_x = FALSE,
  covars = NULL,
  cluster = NULL,
  est_nsmall = 2,
  p_nsmall = 3,
  group_cut_quantiles = 0.5,
  save_plot = FALSE,
  filename = NULL,
  ...
)

Value

A gtable object.

Arguments

data

A data frame.

subgroup_vars

A character vector of variable names to be used as subgroups. It's recommended that the variables are categorical. If the variables are continuous, they will be cut into groups.

x

A character string of the predictor variable.

y

A character string of the outcome variable.

time

A character string of the time variable. If NULL, logistic regression is used. Otherwise, Cox proportional hazards regression is used.

time2

A character string of the ending time of the interval for interval censored or counting process data only.

standardize_x

A logical value. If TRUE, the predictor variable will be standardized.

covars

A character vector of covariate names.

cluster

A character string of the cluster variable. If set, correct for heteroscedasticity and for correlated responses from cluster samples using rms::robcov().

est_nsmall

An integer specifying the precision for the estimates in the plot.

p_nsmall

An integer specifying the number of decimal places for the p-values.

group_cut_quantiles

A vector of numerical values between 0 and 1, specifying the quantile to use for cutting continuous subgroup variables.

save_plot

A logical value indicating whether to save the plot.

filename

A character string specifying the filename for the plot. If NULL, a default filename is used.

...

Additional arguments passed to the forestploter::forest function.

Examples

data(cancer, package = "survival")
# coxph model with time assigned
subgroup_forest(cancer,
  subgroup_vars = c("age", "sex", "wt.loss"), x = "ph.ecog", y = "status",
  time = "time", covars = "ph.karno", ticks_at = c(1, 2), save_plot = FALSE
)

# logistic model with time not assigned
cancer$dead <- cancer$status == 2
subgroup_forest(cancer,
  subgroup_vars = c("age", "sex", "wt.loss"), x = "ph.ecog", y = "dead",
  covars = "ph.karno", ticks_at = c(1, 2), save_plot = FALSE
)

cancer$ph.ecog_cat <- factor(cancer$ph.ecog, levels = c(0:3), labels = c("0", "1", ">=2", ">=2"))
subgroup_forest(cancer,
  subgroup_vars = c("sex", "wt.loss"), x = "ph.ecog_cat", y = "dead",
  covars = "ph.karno", ticks_at = c(1, 2), save_plot = FALSE
)