knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.width = 8, fig.height = 6 ) # Check if gt is available gt_available <- requireNamespace("gt", quietly = TRUE)
library(boinet) library(dplyr) # Only load gt if available if (requireNamespace("gt", quietly = TRUE)) { library(gt) }
This vignette demonstrates how to create publication-ready tables from BOIN-ET simulation results using the gt package integration. The enhanced boinet package provides seamless integration with gt for creating beautiful, professionally formatted tables suitable for clinical reports, regulatory submissions, and academic publications.
if (!gt_available) { cat("**Note:** The gt package is not installed. To use the table formatting features, install it with:\n\n") cat("```r\n") cat("install.packages('gt')\n") cat("```\n\n") cat("The code examples below will not run without gt installed.\n\n") }
# Create realistic mock data for demonstration create_mock_result <- function(design_type = "tite.boinet") { result <- list( toxprob = c("50mg" = 0.02, "100mg" = 0.08, "200mg" = 0.15, "400mg" = 0.25, "800mg" = 0.40), effprob = c("50mg" = 0.10, "100mg" = 0.20, "200mg" = 0.35, "400mg" = 0.50, "800mg" = 0.65), n.patient = c("50mg" = 8.2, "100mg" = 12.5, "200mg" = 15.8, "400mg" = 10.3, "800mg" = 7.2), prop.select = c("50mg" = 5.2, "100mg" = 18.7, "200mg" = 42.1, "400mg" = 28.3, "800mg" = 5.7), phi = 0.30, delta = 0.60, lambda1 = 0.03, lambda2 = 0.42, eta1 = 0.36, duration = 156.3, prop.stop = 3.2, n.sim = 1000 ) if (design_type %in% c("tite.boinet", "tite.gboinet")) { result$tau.T <- 28 result$tau.E <- 42 result$accrual <- 7 } class(result) <- design_type return(result) } # Create sample results tite_result <- create_mock_result("tite.boinet") gboinet_result <- create_mock_result("gboinet")
# Helper function to create basic operating characteristics table create_basic_oc_table <- function(result) { if (!gt_available) { cat("gt package not available. Install with: install.packages('gt')\n") return(NULL) } # Extract data from result object dose_levels <- names(result$n.patient) # Create data frame oc_data <- data.frame( `Dose Level` = dose_levels, `True Toxicity Probability` = round(as.numeric(result$toxprob), 3), `True Efficacy Probability` = round(as.numeric(result$effprob), 3), `Average N Treated` = round(as.numeric(result$n.patient), 1), `Selection Probability (%)` = round(as.numeric(result$prop.select), 1), check.names = FALSE ) # Create gt table gt_table <- oc_data %>% gt() %>% tab_header( title = "Operating Characteristics", subtitle = "BOIN-ET Design Simulation Results" ) %>% fmt_number( columns = c("True Toxicity Probability", "True Efficacy Probability"), decimals = 3 ) %>% fmt_number( columns = "Average N Treated", decimals = 1 ) %>% fmt_number( columns = "Selection Probability (%)", decimals = 1 ) %>% cols_align( align = "center", columns = everything() ) %>% cols_align( align = "left", columns = "Dose Level" ) return(gt_table) } # Helper function to create design parameters table create_design_parameters_table <- function(result) { if (!gt_available) { cat("gt package not available. Install with: install.packages('gt')\n") return(NULL) } # Extract design parameters design_params <- data.frame( Parameter = c("Target Toxicity Rate (φ)", "Target Efficacy Rate (δ)", "Lower Toxicity Boundary (λ₁)", "Upper Toxicity Boundary (λ₂)", "Efficacy Boundary (η₁)", "Early Stop Rate (%)", "Average Duration (days)"), Value = c(result$phi, result$delta, result$lambda1, result$lambda2, result$eta1, result$prop.stop, result$duration), stringsAsFactors = FALSE ) # Add time-specific parameters if available if (!is.null(result$tau.T)) { time_params <- data.frame( Parameter = c("Toxicity Assessment Window (days)", "Efficacy Assessment Window (days)", "Accrual Rate (days)"), Value = c(result$tau.T, result$tau.E, result$accrual), stringsAsFactors = FALSE ) design_params <- rbind(design_params, time_params) } # Create gt table gt_table <- design_params %>% gt() %>% tab_header( title = "Design Parameters", subtitle = paste("Based on", result$n.sim, "simulated trials") ) %>% fmt_number( columns = "Value", decimals = 3 ) %>% cols_align( align = "left", columns = "Parameter" ) %>% cols_align( align = "center", columns = "Value" ) return(gt_table) }
# Create operating characteristics table oc_table <- create_basic_oc_table(tite_result) if (!is.null(oc_table)) { oc_table }
# Create design parameters table design_table <- create_design_parameters_table(tite_result) if (!is.null(design_table)) { design_table }
# Create table with custom styling if (gt_available) { custom_oc_table <- create_basic_oc_table(tite_result) %>% # Update header with subtitle using tab_header tab_header( title = "Table 1: TITE-BOIN-ET Operating Characteristics for Drug XYZ-123", subtitle = "Phase I Dose-Finding Study" ) %>% # Add footnotes tab_footnote( footnote = "Based on 1,000 simulated trials", locations = cells_title(groups = "title") ) %>% tab_footnote( footnote = "Highlighted row indicates dose with highest selection probability", locations = cells_column_labels(columns = "Selection Probability (%)") ) %>% # Highlight optimal dose (row with highest selection probability) tab_style( style = cell_fill(color = "lightblue"), locations = cells_body( rows = `Selection Probability (%)` == max(`Selection Probability (%)`) ) ) %>% # Add source note tab_source_note( source_note = "Generated using boinet package" ) %>% # Professional styling tab_style( style = list( cell_text(weight = "bold"), cell_borders(sides = "bottom", weight = px(2)) ), locations = cells_column_labels() ) custom_oc_table }
# Create a table suitable for regulatory submission if (gt_available) { regulatory_table <- create_basic_oc_table(tite_result) %>% # Professional styling tab_header( title = "Operating Characteristics Summary", subtitle = "Regulatory Submission Table" ) %>% tab_style( style = list( cell_text(weight = "bold"), cell_borders(sides = "bottom", weight = px(2)) ), locations = cells_column_labels() ) %>% tab_style( style = cell_text(align = "center"), locations = cells_body(columns = everything()) ) %>% tab_style( style = cell_text(align = "left"), locations = cells_body(columns = "Dose Level") ) %>% # Add regulatory footnotes tab_footnote( footnote = "Target toxicity probability: 30%; Target efficacy probability: 60%", locations = cells_title() ) %>% # Regulatory-style source note tab_source_note( source_note = paste("Study Protocol ABC-123-001 |", "Statistical Analysis Plan v2.0 |", "Generated:", format(Sys.Date(), "%d-%b-%Y")) ) %>% # Adjust table options for print tab_options( table.font.size = 11, heading.title.font.size = 12, footnotes.font.size = 9, table.width = pct(100) ) regulatory_table }
if (gt_available) { # Create comparison data scenarios <- data.frame( `Dose Level` = names(tite_result$prop.select), `Conservative (φ=0.25)` = c(8.1, 32.4, 45.2, 12.1, 2.2), `Standard (φ=0.30)` = as.numeric(tite_result$prop.select), `Aggressive (φ=0.35)` = c(2.3, 12.8, 35.6, 38.9, 10.4), check.names = FALSE ) comparison_table <- scenarios %>% gt() %>% tab_header( title = "Scenario Comparison: Impact of Target Toxicity Rate", subtitle = "Selection Probabilities Across Different Design Parameters" ) %>% # Format numbers consistently fmt_number( columns = contains("φ="), decimals = 1 ) %>% # Add spanning header tab_spanner( label = "Selection Probability (%)", columns = contains("φ=") ) %>% # Color code the scenarios tab_style( style = cell_fill(color = "lightgreen"), locations = cells_body(columns = contains("Conservative")) ) %>% tab_style( style = cell_fill(color = "lightyellow"), locations = cells_body(columns = contains("Standard")) ) %>% tab_style( style = cell_fill(color = "lightcoral"), locations = cells_body(columns = contains("Aggressive")) ) %>% cols_align( align = "center", columns = everything() ) %>% cols_align( align = "left", columns = "Dose Level" ) comparison_table }
if (gt_available) { # Create enhanced design parameters table with categories design_data <- data.frame( category = c(rep("Design Criteria", 5), rep("Trial Logistics", 3)), parameter = c("Target Toxicity Rate (φ)", "Target Efficacy Rate (δ)", "Lower Toxicity Boundary (λ₁)", "Upper Toxicity Boundary (λ₂)", "Efficacy Boundary (η₁)", "Toxicity Assessment Window", "Efficacy Assessment Window", "Accrual Rate"), value = c(tite_result$phi, tite_result$delta, tite_result$lambda1, tite_result$lambda2, tite_result$eta1, paste(tite_result$tau.T, "days"), paste(tite_result$tau.E, "days"), paste(tite_result$accrual, "days")), stringsAsFactors = FALSE ) enhanced_design_table <- design_data %>% gt(groupname_col = "category") %>% tab_header( title = "TITE-BOIN-ET Design Specifications", subtitle = "Drug XYZ-123 Phase I Study" ) %>% cols_label( parameter = "Parameter", value = "Value" ) %>% # Style the groups tab_style( style = cell_text(weight = "bold"), locations = cells_row_groups() ) %>% tab_style( style = cell_fill(color = "gray95"), locations = cells_row_groups() ) %>% cols_align( align = "left", columns = "parameter" ) %>% cols_align( align = "center", columns = "value" ) enhanced_design_table }
# Save tables in different formats (examples - not run) if (gt_available && exists("oc_table") && !is.null(oc_table)) { # HTML (interactive) # oc_table %>% gtsave("operating_characteristics.html") # PNG (for presentations) # oc_table %>% gtsave("operating_characteristics.png") # Word document (for reports) # oc_table %>% gtsave("operating_characteristics.docx") # RTF (for regulatory submissions) # oc_table %>% gtsave("operating_characteristics.rtf") cat("Tables can be saved using gtsave() function\n") cat("Example: table %>% gtsave('filename.html')\n") }
Follow institutional or regulatory guidelines
Clear Communication
Use highlighting to draw attention to key findings
Professional Appearance
if (gt_available) { # Example of professional formatting professional_table <- create_basic_oc_table(tite_result) %>% # Consistent alignment tab_style( style = cell_text(align = "center"), locations = cells_body(columns = c("True Toxicity Probability", "True Efficacy Probability", "Selection Probability (%)")) ) %>% tab_style( style = cell_text(align = "right"), locations = cells_body(columns = "Average N Treated") ) %>% # Clear borders tab_style( style = cell_borders(sides = "top", weight = px(2)), locations = cells_body(rows = 1) ) %>% # Appropriate precision fmt_number( columns = "Average N Treated", decimals = 1 ) %>% tab_header( title = "Professional Table Example", subtitle = "Consistent formatting and alignment" ) professional_table }
# Issue: gt package not installed if (!requireNamespace("gt", quietly = TRUE)) { message("Installing gt package...") # install.packages("gt") } # Issue: Functions not found # Solution: Ensure boinet package is properly loaded library(boinet) # Issue: Tables not displaying in PDF # Solution: Use specific styling for PDF output and consider using gtsave() cat("Common troubleshooting tips:\n") cat("1. Ensure gt package is installed: install.packages('gt')\n") cat("2. Load boinet package: library(boinet)\n") cat("3. For PDF output, use gtsave() to export tables\n") cat("4. Check that result objects have expected structure\n")
The gt integration provides powerful tools for creating publication-ready tables from BOIN-ET simulation results. Key benefits include:
This vignette demonstrates the basic functionality. As the boinet package develops further, additional specialized functions for gt integration will become available.
For more information on the gt package, visit: https://gt.rstudio.com/
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