g_step: Create a STEP graph

In tern: Create Common TLGs Used in Clinical Trials

Create a STEP graph

Description

Based on the STEP results, creates a ggplot graph showing the estimated HR or OR along the continuous biomarker value subgroups.

Usage

g_step(
  df,
  use_percentile = "Percentile Center" %in% names(df),
  est = list(col = "blue", lty = 1),
  ci_ribbon = list(fill = getOption("ggplot2.discrete.colour")[1], alpha = 0.5),
  col = getOption("ggplot2.discrete.colour")
)

Arguments

df	(tibble) result of tidy.step().
use_percentile	(flag) whether to use percentiles for the x axis or actual biomarker values.
est	(named list) col and lty settings for estimate line.
ci_ribbon	(named list or NULL) fill and alpha settings for the confidence interval ribbon area, or NULL to not plot a CI ribbon.
col	(character) color(s).

Value

A ggplot STEP graph.

See Also

Custom tidy method tidy.step().

Examples

library(nestcolor)
library(survival)
lung$sex <- factor(lung$sex)

# Survival example.
vars <- list(
  time = "time",
  event = "status",
  arm = "sex",
  biomarker = "age"
)

step_matrix <- fit_survival_step(
  variables = vars,
  data = lung,
  control = c(control_coxph(), control_step(num_points = 10, degree = 2))
)
step_data <- broom::tidy(step_matrix)

# Default plot.
g_step(step_data)

# Add the reference 1 horizontal line.
library(ggplot2)
g_step(step_data) +
  ggplot2::geom_hline(ggplot2::aes(yintercept = 1), linetype = 2)

# Use actual values instead of percentiles, different color for estimate and no CI,
# use log scale for y axis.
g_step(
  step_data,
  use_percentile = FALSE,
  est = list(col = "blue", lty = 1),
  ci_ribbon = NULL
) + scale_y_log10()

# Adding another curve based on additional column.
step_data$extra <- exp(step_data$`Percentile Center`)
g_step(step_data) +
  ggplot2::geom_line(ggplot2::aes(y = extra), linetype = 2, color = "green")

# Response example.
vars <- list(
  response = "status",
  arm = "sex",
  biomarker = "age"
)

step_matrix <- fit_rsp_step(
  variables = vars,
  data = lung,
  control = c(
    control_logistic(response_definition = "I(response == 2)"),
    control_step()
  )
)
step_data <- broom::tidy(step_matrix)
g_step(step_data)

tern documentation built on Sept. 24, 2024, 9:06 a.m.