Plot to display risk difference — plot

Create a plot to display risk difference for each item.

Usage

plot_errorbar(
  tbl,
  ci_cols = c("diff_1", "lower_1", "upper_1"),
  y_var,
  errbar_width = 0.4,
  color = NULL,
  shape = NULL,
  label,
  x_breaks = NULL,
  grp_abbrev = "paired",
  favor_direction = "negative",
  vline = NULL,
  line_type = 1,
  title = "Risk Diff. + 95% CI \n (Percentage Points)",
  background_color = c("#69B8F7", "#FFFFFF"),
  background_alpha = 0.3,
  theme = theme_panel(show_text = TRUE, show_ticks = TRUE),
  legend_nrow = 1
)

Arguments

tbl: A data frame selected from input data set to display on this plot. y and x variables are required.
ci_cols: A character vector of columns for a risk difference to be used for a plot. Need 3 columns, risk difference, lower bound, and upper bound.
y_var: A character string that specifies a variable to be displayed on the y-axis.
errbar_width: A numeric value to define the error bar width. Default is 0.4. Value of this argument will be a half length of the error bar, for example, errorbar_width = 0.2 means half of the error bar width is 0.2 unit length. If y = 4, the error bar will range from y = 3.8 to y = 4.2.
color: Color for each treatment group.
shape: Shape for each treatment group. Default is circle and square. Input values could be either a character or numeric value, For details, see https://ggplot2.tidyverse.org/articles/ggplot2-specs.html.
label: A character vector of labels for each treatment group. The control group label should be specified as the last element of the vector.
x_breaks: A numeric vector for x-axis breaks. Default is NULL, which uses a default ggplot2 x-axis breaks presentation.
grp_abbrev: A character vector for displaying the treatment groups on a favor bar. If grp_abbrev = "paired", treatment label on the error bar will be the same as in the label argument. If grp_abbrev = "none", the error bar will not be shown. Also, for customized terms, users can provide an alternative vector of treatment labels. Default is "paired".
favor_direction: The position of a favor label under the condition "comparison is treatment – control". For AEs, favor_direction should be "negative"; for efficacy, favor_direction should be "positive".
vline: Vertical reference line position. Default is NULL. Users can define one or multiple numeric values in a vector as a reference line position.
line_type: Reference line type. Default is solid line. Users can define one or multiple line types in a vector (can be numeric such as 1, 2, 3 or character such as "solid", "dashed"). The values will be recycled and the order will be consistent with the argument vline.
title: Plot title. Default is "Risk Diff. + 95% CI \\n (Percentage Points)".
background_color: Plot background color. Default is c("#69B8F7", "#FFFFFF"), which are pastel blue and white. The value of this argument is used as input for the background_color argument in background_panel().
background_alpha: Opacity of the background. Default is 0.3. The value of this argument is the input for background_alpha argument in background_panel().
theme: Panel theme, including the y-axis text, ticks, and plot margin. Default is theme_panel(show_text = TRUE, show_ticks = TRUE). For more details, refer to theme_panel.
legend_nrow: Integer, the number of rows for a legend display. Must be smaller than or equal to the number of the treatment groups. To omit the legend, set this to NULL. Default is 1.

Value

A risk difference plot for each item.

Examples

forestly_adsl$TRTA <- factor(
  forestly_adsl$TRT01A,
  levels = c("Xanomeline Low Dose", "Placebo"),
  labels = c("Low Dose", "Placebo")
)
forestly_adae$TRTA <- factor(
  forestly_adae$TRTA,
  levels = c("Xanomeline Low Dose", "Placebo"),
  labels = c("Low Dose", "Placebo")
)

analysis_plan <- metalite::plan(
  analysis = "ae_forestly",
  population = "apat",
  observation = "wk12",
  parameter = "any"
)
meta <- metalite::meta_adam(
  population = forestly_adsl,
  observation = forestly_adae
) |>
  metalite::define_plan(plan = analysis_plan) |>
  metalite::define_population(
    name = "apat",
    var = c("USUBJID", "SAFFL", "TRTA", "SITEID", "SEX", "RACE", "AGE"),
    group = "TRTA",
    subset = SAFFL == "Y",
    label = "All Participants as Treated"
  ) |>
  metalite::define_observation(
    name = "wk12",
    var = c(
      "USUBJID", "SAFFL", "TRTA", "SITEID", "SEX", "RACE", "AGE",
      "ASTDY", "AEDECOD", "AEBODSYS", "AESER", "AEREL", "AEACN",
      "AEOUT", "ADURN", "ADURU"
    ),
    group = "TRTA",
    subset = SAFFL == "Y",
    label = "Weeks 0 to 12"
  ) |>
  metalite::define_parameter(
    name = "any",
    term1 = "",
    term2 = "",
    var = "AEDECOD",
    soc = "AEBODSYS",
    label = "All AEs"
  ) |>
  metalite::define_analysis(
    name = "ae_forestly",
    label = "Interactive forest plot"
  ) |>
  metalite::meta_build()

outdata <- meta |>
  prepare_ae_forestly(parameter = "any") |>
  format_ae_forestly()

outdata_any <- outdata$tbl[1:20, ] |> dplyr::filter(parameter == "any")
outdata_any |>
  dplyr::select(name, diff_1, lower_1, upper_1) |>
  plot_errorbar(
    y_var = "name",
    ci_cols = c("diff_1", "lower_1", "upper_1"),
    label = c("Treatment", "Placebo")
  )