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valueJudgementCE

The goal of valueJudgementCE is to generate benefit-risk visualizations for the publication “How to visually integrate value judgment with clinical evidence”.

Table of Contents

Installation

You can install the development version of valueJudgementCE from GitHub using the following methods:

# Install using pak (recommended)
install.packages("pak")
pak::pak("BR-Visualization/value-judgement-clinical-evidence")

Alternative Installation 

# Install using remotes
install.packages("remotes")
remotes::install_github("BR-Visualization/value-judgement-clinical-evidence")

Figure - Dot-Forest Plot

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Getting Help

Click to view sample code 
# Load the package and create the plot
library(valueJudgementCE)

# Prepare the data and create the visualization
result_plot <- create_forest_dot_plot(
  prepare_forest_dot_data(effects_table)
)

# Display the plot
result_plot

Figure - Trade-off Plot

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Getting Help

Click to view sample code 
library(valueJudgementCE)

effects_table_filtered <- effects_table |>
  dplyr::filter(Outcome %in% c("Risk 1", "Benefit 1"))

generate_tradeoff_plot(
  data = effects_table_filtered,
  filter = "None",
  category = "All",
  benefit = "Benefit 1",
  risk = "Risk 1",
  type_risk = "Crude proportions",
  type_graph = "Absolute risk",
  ci = "Yes",
  ci_method = "Calculated",
  cl = 0.95,
  mab = 0.05,
  mar = 0.45,
  threshold = "Segmented line",
  ratio = 4,
  b1 = 0.05, b2 = 0.1, b3 = 0.15, b4 = 0.2, b5 = 0.25,
  b6 = 0.3, b7 = 0.35, b8 = 0.4, b9 = 0.45, b10 = 0.5,
  r1 = 0.09, r2 = 0.17, r3 = 0.24, r4 = 0.3, r5 = 0.35,
  r6 = 0.39, r7 = 0.42, r8 = 0.44, r9 = 0.45, r10 = 0.45,
  testdrug = "Yes",
  type_scale = "Free",
  lower_x = 0, upper_x = 0.5,
  lower_y = 0, upper_y = 0.5,
  chartcolors = colfun()$fig7_colors
)

Figure - Correlogram

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Getting Help

Click to view sample code

Figure - Scatter Plot

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Getting Help

Click to view sample code 
library(valueJudgementCE)

outcome <- c("Benefit", "Risk")
scatter_plot(scatterplot, outcome, mab = 0.2, mar = 0.6)

Figure - Divergent Stacked Bar Chart

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Getting Help

Click to view sample code 
library(valueJudgementCE)
library(cowplot)
library(gtable)

# Create both plots
stacked_bar_fig <- stacked_barchart(
  data = comp_outcome,
  chartcolors = colfun()$fig12_colors,
  ylabel = "Study Week"
)

divergent_stacked_bar_fig <- divergent_stacked_barchart(
  data = comp_outcome,
  chartcolors = colfun()$fig12_colors,
  favcat = c(
    "Benefit larger than threshold, with AE",
    "Benefit larger than threshold, w/o AE"
  ),
  unfavcat = c(
    "Withdrew",
    "Benefit less than threshold, w/o AE",
    "Benefit less than threshold, with AE"
  ),
  ylabel = "Study Week"
)

# Extract and combine with shared legend
stacked_bar_with_legend <- stacked_bar_fig + 
  labs(fill = "Outcome") +
  theme(legend.position = "top",
        legend.justification = "center",
        legend.box.just = "center",
        legend.title.align = 0.5) +
  guides(fill = guide_legend(nrow = 2, title.position = "top", title.hjust = 0.5))

g <- ggplotGrob(stacked_bar_with_legend)
legend <- g$grobs[[which(g$layout$name == "guide-box-top")]]

stacked_bar_no_legend <- stacked_bar_fig +
  theme(
    legend.position = "none",
    plot.background = element_rect(color = "black", fill = NA, linewidth = 0.5)
  )
divergent_stacked_bar_no_legend <- divergent_stacked_bar_fig +
  theme(
    legend.position = "none",
    plot.background = element_rect(color = "black", fill = NA, linewidth = 0.5)
  )

combined_plots <- plot_grid(stacked_bar_no_legend, divergent_stacked_bar_no_legend, ncol = 2)
plot_grid(legend, combined_plots, ncol = 1, rel_heights = c(0.2, 1))

Figure - Cumulative Excess Plot

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Getting Help

Click to view sample code 
library(valueJudgementCE)

gensurv_combined(
  df_plot = cumexcess, subjects_pt = 100, visits_pt = 6,
  df_table = cumexcess, fig_colors_pt = colfun()$fig13_colors,
  rel_heights_table = c(1, 0.5),
  legend_position_p = c(.1, 1.56),
  titlename =
    "Cumulative Excess # of Subjects w/ Events (per 100 Subjects)",
  mar = 32,
  mab = 15,
  mcd = 22
)

Figure - Value Function Types

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Getting Help

Click to view sample code 
library(valueJudgementCE)

compare_value_function_types(
  benefit_name = "Efficacy",
  benefit_min = 0,
  benefit_max = 100,
  benefit_label = "Response Rate (%)",
  risk_name = "Safety",
  risk_min = 0,
  risk_max = 100,
  risk_label = "Adverse Event Rate (%)",
  power = 2,
  show_titles = FALSE,
  show_legend = TRUE
)

Figure - MCDA Comparison Plot

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Getting Help

Click to view sample code 
library(valueJudgementCE)

create_mcda_barplot_comparison(
  data = mcda_data,
  study = "Study 1",
  benefit_criteria = c("Benefit 1", "Benefit 2", "Benefit 3"),
  risk_criteria = c("Risk 1", "Risk 2"),
  comparison_drug = "Drug A",
  clinical_scales = clinical_scales,
  weights = weights
)

Figure - MCDA Waterfall Plot

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Getting Help

Click to view sample code 
library(valueJudgementCE)

create_mcda_waterfall(
  data = mcda_data,
  comparator_name = "Placebo",
  benefit_criteria = c("Benefit 1", "Benefit 2", "Benefit 3"),
  risk_criteria = c("Risk 1", "Risk 2"),
  weights = weights,
  clinical_scales = clinical_scales
)

Figure - MCDA Benefit-Risk Map

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Getting Help

Click to view sample code 
library(valueJudgementCE)

create_mcda_brmap(
  data = mcda_data,
  comparator_name = "Placebo",
  benefit_criteria = c("Benefit 1", "Benefit 2", "Benefit 3"),
  risk_criteria = c("Risk 1", "Risk 2"),
  weights = weights,
  clinical_scales = clinical_scales,
  show_frontier = TRUE,
  show_labels = TRUE
)

Figure - MCDA Tornado Plot

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Getting Help

Click to view sample code 
library(valueJudgementCE)

mcda_tornado(
  data = mcda_data |> dplyr::filter(Study == "Study 1") |> dplyr::select(-Study),
  comparator_name = "Placebo",
  comparison_drug = "Drug A",
  weights = weights,
  clinical_scales = clinical_scales
)

Citation

If you use this package in your research, please cite:

citation("valueJudgementCE")

License

This package is licensed under the MIT License. See the LICENSE file for details.

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