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Enhanced Tables with gt Package - Professional Clinical Research

Publication-ready tables with advanced formatting and clinical research optimization

ClinicoPath

2025-07-13

Source: vignettes/clinicopath-descriptives-14-enhancedtables.Rmd
clinicopath-descriptives-14-enhancedtables.Rmd

Introduction to Enhanced Tables with gt

The enhancedtables function in ClinicoPath leverages the powerful gt package to create professional, publication-ready tables with advanced formatting capabilities. This module transforms standard data summaries into sophisticated tables suitable for medical journals, regulatory submissions, and clinical presentations. The integration provides comprehensive styling options, automated statistical summaries, and clinical research-specific optimizations.

What Makes Enhanced Tables Special?

Advanced Table Creation

  • gt Package Integration: Full integration with the grammar of tables for professional formatting
  • Multiple Table Types: Summary, grouped, clinical characteristics, Table One, and custom formats
  • Six Professional Themes: Clinical, publication, modern, traditional, minimal, and journal styles
  • Statistical Integration: Automated statistical summaries and group comparisons
  • Export Flexibility: HTML, Word, LaTeX, and RTF export options

Clinical Research Focus

  • Publication Standards: Tables formatted for medical journal requirements
  • Regulatory Compliance: Output suitable for FDA, EMA, and other regulatory submissions
  • Statistical Rigor: Appropriate statistical summaries and test selection
  • Professional Appearance: High-quality formatting for presentations and reports

When to Use Enhanced Tables

Perfect for: - Clinical trial baseline characteristic tables (Table 1) - Summary statistics for manuscript preparation - Regulatory submission documentation - Conference presentation materials - Multi-center study reports - Biostatistical analysis summaries

Key Advantages: - Superior formatting compared to basic data frames - Automated statistical summary generation - Professional themes designed for medical research - Comprehensive export options for various publication formats - Clinical interpretation and usage guidelines

Getting Started

Installation Requirements

The enhanced tables functionality requires the gt package and supporting packages:

# Install core gt package
install.packages("gt")

# For enhanced export functionality
install.packages(c("officer", "webshot2"))

# Supporting packages (automatically handled)
install.packages(c("dplyr", "tidyr", "janitor", "stringr"))

Basic Syntax and Options

Enhanced Tables vs Standard Tables 

# Standard R table
summary(histopathology[c("Age", "Sex", "Grade_Level")])

# Enhanced tables with gt integration
enhancedtables(
  data = histopathology,
  vars = c("Age", "Sex", "Grade_Level"),
  table_type = "summary",
  table_theme = "clinical",
  export_format = "html"
)

Key Differences

  • Professional Formatting: Publication-ready appearance with customizable themes
  • Statistical Summaries: Comprehensive statistics for both continuous and categorical variables
  • Clinical Optimization: Designed specifically for medical research applications
  • Export Flexibility: Multiple output formats for different publication needs

Clinical Research Applications

Example 1: Baseline Characteristics Table (Table 1)

This example demonstrates creating a comprehensive Table 1 for clinical trial baseline characteristics:

library(ClinicoPath)

# Load the histopathology dataset
data("histopathology")

# Create Table 1 with enhanced formatting
enhancedtables(
  data = histopathology,
  vars = c("Age", "Sex", "Race", "Grade_Level", "LVI", "PNI", "OverallTime"),
  table_type = "tableone",
  table_theme = "clinical",
  stats_continuous = "mean_sd",
  stats_categorical = "n_percent",
  include_total = TRUE,
  table_title = "Table 1. Baseline Characteristics",
  table_subtitle = "Clinical and pathological features of study participants",
  show_interpretation = TRUE
)

Clinical Features: - Automatic Table 1 formatting with “Overall (N = X)” column - Professional clinical theme with appropriate fonts and spacing - Mean ± SD for continuous variables, N (%) for categorical variables - Comprehensive sample size reporting - Clinical interpretation guidelines included

Example 2: Grouped Analysis by Treatment

Analyzing characteristics across treatment groups with statistical comparisons:

# Grouped analysis with statistical testing
enhancedtables(
  data = histopathology,
  vars = c("Age", "Sex", "Grade_Level", "LVI", "PNI", "OverallTime"),
  group_var = "Group",
  table_type = "grouped",
  table_theme = "publication",
  include_pvalues = TRUE,
  test_type = "auto",
  highlight_significant = TRUE,
  confidence_level = 0.95,
  table_title = "Baseline Characteristics by Treatment Group",
  export_format = "word"
)

Advanced Features: - Automatic statistical test selection (t-test, chi-square, etc.) - P-value calculation and highlighting of significant results - Publication theme optimized for journal submission - Word-compatible export for manuscript preparation - Confidence interval reporting

Example 3: Clinical Characteristics Summary

Creating a comprehensive clinical summary table:

# Clinical characteristics with comprehensive statistics
enhancedtables(
  data = histopathology,
  vars = c("Age", "Sex", "Grade_Level", "TStage", "LVI", "PNI", "Mortality5yr"),
  table_type = "clinical",
  table_theme = "journal",
  stats_continuous = "both",
  stats_categorical = "n_percent_missing",
  missing_handling = "show",
  show_footnotes = TRUE,
  decimal_places = 1,
  source_note = "Data from institutional pathology database"
)

Clinical Optimization: - Both mean ± SD and median [IQR] for continuous variables - Missing value transparency with counts - Journal-style formatting for publication - Comprehensive footnotes explaining statistical methods - Source documentation for data provenance

Advanced Features and Customization

Table Types and Formatting

The enhanced tables module offers six distinct table types optimized for different clinical applications:

Table Type Options

  • summary: General summary statistics for selected variables
  • grouped: Grouped analysis with statistical comparisons
  • descriptive: Comprehensive descriptive overview
  • clinical: Clinical characteristics format
  • tableone: Standard Table 1 format for baseline characteristics
  • custom: User-defined formatting options

Professional Applications 

# Summary table for exploratory analysis
enhancedtables(
  data = histopathology,
  vars = c("Age", "OverallTime", "Grade_Level"),
  table_type = "summary",
  table_theme = "minimal"
)

# Clinical characteristics for case series
enhancedtables(
  data = histopathology,
  vars = c("Sex", "Age", "TStage", "Grade_Level", "Outcome"),
  table_type = "clinical",
  table_theme = "clinical",
  stats_continuous = "median_iqr"
)

Theme System and Visual Customization

The enhanced tables module provides six professional themes designed for different publication contexts:

Available Themes

  • clinical: Healthcare-focused with clean, readable formatting
  • publication: Optimized for academic journal submission
  • modern: Contemporary design with accent colors
  • traditional: Conservative formatting for formal reports
  • minimal: Clean, uncluttered appearance
  • journal: Specific formatting for medical journals

Theme Applications 

# Clinical research presentation
enhancedtables(
  data = histopathology,
  vars = c("Age", "Sex", "Grade_Level"),
  table_theme = "clinical",
  font_size = "large",
  stripe_rows = TRUE,
  group_colors = TRUE
)

# Journal manuscript submission
enhancedtables(
  data = histopathology,
  vars = c("Age", "Sex", "Grade_Level"),
  table_theme = "publication",
  font_size = "normal",
  stripe_rows = FALSE,
  table_width = "full"
)

Statistical Summary Options

Comprehensive statistical summary options for different variable types:

Continuous Variables

  • mean_sd: Mean ± Standard Deviation
  • median_iqr: Median [Interquartile Range]
  • both: Mean ± SD, Median [IQR]
  • mean_sd_range: Mean ± SD with Min-Max range
  • all: Comprehensive statistics including all measures

Categorical Variables

  • n_percent: Count (Percentage)
  • n_only: Count only
  • percent_only: Percentage only
  • n_percent_missing: Count (Percentage) with missing value reporting

Clinical Applications 

# Comprehensive continuous variable summaries
enhancedtables(
  data = histopathology,
  vars = c("Age", "OverallTime"),
  stats_continuous = "all",
  decimal_places = 2
)

# Categorical variables with missing value transparency
enhancedtables(
  data = histopathology,
  vars = c("Sex", "Race", "Grade_Level"),
  stats_categorical = "n_percent_missing",
  missing_handling = "show"
)

Statistical Testing and P-values

Automated statistical testing with appropriate test selection:

Test Types

  • auto: Automatic test selection based on data characteristics
  • parametric: Parametric tests (t-test, ANOVA)
  • nonparametric: Non-parametric tests (Wilcoxon, Kruskal-Wallis)
  • categorical: Chi-square and Fisher’s exact tests

Statistical Features 

# Automatic test selection with p-values
enhancedtables(
  data = histopathology,
  vars = c("Age", "Sex", "Grade_Level", "LVI"),
  group_var = "Mortality5yr",
  include_pvalues = TRUE,
  test_type = "auto",
  confidence_level = 0.95,
  highlight_significant = TRUE
)

# Non-parametric analysis for skewed data
enhancedtables(
  data = histopathology,
  vars = c("OverallTime", "Age"),
  group_var = "Group",
  include_pvalues = TRUE,
  test_type = "nonparametric",
  stats_continuous = "median_iqr"
)

Export and Reporting Options

Publication-Ready Outputs

HTML Tables for Web Reports 

# Interactive HTML table for web reports
enhancedtables(
  data = histopathology,
  vars = c("Sex", "Age", "Grade_Level", "LVI", "PNI"),
  group_var = "Group",
  export_format = "html",
  table_theme = "modern",
  stripe_rows = TRUE,
  highlight_significant = TRUE
)

Word Document Integration 

# Word-compatible formatting for manuscripts
enhancedtables(
  data = histopathology,
  vars = c("Age", "Sex", "TStage", "Grade_Level", "Outcome"),
  group_var = "Group",
  export_format = "word",
  table_theme = "publication",
  font_size = "small",
  table_width = "full"
)

LaTeX for Academic Publishing 

# LaTeX output for academic journals
enhancedtables(
  data = histopathology,
  vars = c("Age", "Sex", "Grade_Level", "LVI"),
  table_type = "tableone",
  export_format = "latex",
  table_theme = "journal",
  decimal_places = 1
)

Missing Value Handling

Comprehensive missing value handling strategies:

Missing Value Options

  • show: Display missing values transparently
  • hide: Hide missing values from display
  • exclude: Exclude cases with missing values
  • category: Treat missing as a separate category

Clinical Applications 

# Transparent missing value reporting
enhancedtables(
  data = histopathology,
  vars = c("Race", "Smoker", "BMI"),
  missing_handling = "show",
  stats_categorical = "n_percent_missing",
  show_footnotes = TRUE
)

# Complete case analysis
enhancedtables(
  data = histopathology,
  vars = c("Age", "Sex", "Grade_Level"),
  group_var = "Group",
  missing_handling = "exclude",
  include_pvalues = TRUE
)

Best Practices for Clinical Research

Design Recommendations

Variable Selection

  • Primary Variables: Focus on clinically meaningful characteristics
  • Grouping Variables: Select appropriate stratification variables
  • Sample Size: Ensure adequate representation in each group

Statistical Considerations

  • Appropriate Statistics: Use median [IQR] for skewed distributions
  • Multiple Testing: Consider Bonferroni correction for multiple comparisons
  • Effect Sizes: Report alongside p-values for clinical significance
  • Confidence Intervals: Provide precision estimates for key measures

Formatting Standards

  • Journal Requirements: Follow specific journal table formatting guidelines
  • Regulatory Standards: Adhere to FDA/EMA table formatting requirements
  • Accessibility: Ensure tables are accessible to screen readers

Quality Assurance

Data Validation 

# Pre-analysis data quality check
summary(histopathology[c("Group", "Age", "Sex", "Grade_Level")])

# Enhanced table with validation
enhancedtables(
  data = histopathology,
  vars = c("Age", "Sex", "Grade_Level", "LVI"),
  group_var = "Group",
  missing_handling = "show",
  show_interpretation = TRUE,
  source_note = "Data validated: [Date] by [Analyst]"
)

Reproducibility Standards 

# Document analysis parameters
analysis_parameters <- list(
  dataset = "histopathology v1.0",
  variables = c("Age", "Sex", "Grade_Level", "LVI", "PNI"),
  grouping = "Group",
  statistics = "mean_sd for continuous, n_percent for categorical",
  missing_handling = "show",
  significance_level = 0.05,
  analysis_date = Sys.Date()
)

# Include parameters in table
enhancedtables(
  data = histopathology,
  vars = analysis_parameters$variables,
  group_var = analysis_parameters$grouping,
  stats_continuous = "mean_sd",
  stats_categorical = "n_percent",
  missing_handling = "show",
  confidence_level = 0.95,
  source_note = paste("Analysis parameters documented:", 
                     analysis_parameters$analysis_date)
)

Advanced Features and Customization

Conditional Formatting and Highlighting

Significance Highlighting 

# Highlight statistically significant results
enhancedtables(
  data = histopathology,
  vars = c("Age", "OverallTime", "Grade_Level", "LVI"),
  group_var = "Mortality5yr",
  include_pvalues = TRUE,
  highlight_significant = TRUE,
  confidence_level = 0.95,
  table_theme = "clinical"
)

Custom Color Schemes 

# Custom color scheme for group headers
enhancedtables(
  data = histopathology,
  vars = c("Age", "Sex", "Grade_Level"),
  group_var = "Group",
  table_theme = "modern",
  group_colors = TRUE,
  stripe_rows = TRUE,
  font_size = "normal"
)

Multi-level Grouping and Stratification

Stratified Analysis 

# Stratified analysis by multiple variables
enhancedtables(
  data = histopathology,
  vars = c("Age", "Grade_Level", "LVI", "PNI"),
  group_var = "Group",
  strata_var = "Sex",
  table_type = "grouped",
  include_pvalues = TRUE,
  table_theme = "publication"
)

Complex Table Structures 

# Complex multi-level table
enhancedtables(
  data = histopathology,
  vars = c("Age", "OverallTime", "TStage", "Grade_Level", "LVI", "PNI"),
  group_var = "Group",
  table_type = "clinical",
  stats_continuous = "both",
  stats_categorical = "n_percent",
  table_theme = "journal",
  decimal_places = 2,
  show_footnotes = TRUE
)

Integration with Clinical Workflows

Electronic Medical Record (EMR) Data

EMR Data Processing 

# Example with electronic medical record data structure
emr_data <- data.frame(
  patient_id = 1:150,
  age = rnorm(150, 62, 15),
  sex = factor(sample(c("Male", "Female"), 150, replace = TRUE)),
  diagnosis = factor(sample(c("Type 1", "Type 2", "Type 3"), 150, replace = TRUE)),
  stage = factor(sample(c("Early", "Advanced"), 150, replace = TRUE)),
  treatment = factor(sample(c("Surgery", "Chemotherapy", "Radiation", "Combined"), 150, replace = TRUE)),
  response = factor(sample(c("Complete", "Partial", "No Response"), 150, replace = TRUE))
)

# Clinical research table
enhancedtables(
  data = emr_data,
  vars = c("age", "sex", "diagnosis", "stage"),
  group_var = "treatment",
  table_type = "clinical",
  include_pvalues = TRUE,
  table_theme = "clinical",
  export_format = "word"
)

Multi-Center Studies

Standardized Reporting 

# Multi-center study analysis function
multi_center_table <- function(data, center_var) {
  centers <- unique(data[[center_var]])
  
  for (center in centers) {
    cat("\n=== Analysis for", center, "===\n")
    center_data <- data[data[[center_var]] == center, ]
    
    result <- enhancedtables(
      data = center_data,
      vars = c("age", "sex", "diagnosis", "stage"),
      table_type = "summary",
      table_theme = "clinical",
      table_title = paste("Center:", center),
      show_interpretation = FALSE
    )
    
    print(result)
  }
}

Troubleshooting and Support

Common Issues and Solutions

Package Dependencies 

# Check required packages
required_packages <- c("gt", "dplyr", "tidyr", "officer")

for (pkg in required_packages) {
  if (!requireNamespace(pkg, quietly = TRUE)) {
    message(paste("Installing missing package:", pkg))
    install.packages(pkg)
  }
}

Data Format Issues 

# Ensure proper factor levels
histopathology$Group <- factor(histopathology$Group)
histopathology$Sex <- factor(histopathology$Sex)

# Check for adequate sample sizes
table(histopathology$Group, histopathology$Sex)

Export Problems 

# Fallback to HTML if specialized export fails
tryCatch({
  enhancedtables(
    data = histopathology,
    vars = c("Age", "Sex"),
    export_format = "word"
  )
}, error = function(e) {
  enhancedtables(
    data = histopathology,
    vars = c("Age", "Sex"),
    export_format = "html"  # Reliable fallback
  )
})

Performance Optimization

Large Dataset Handling 

# Optimize for large datasets
large_data_table <- function(data, max_vars = 10) {
  # Limit variables for performance
  selected_vars <- names(data)[1:min(max_vars, ncol(data))]
  
  enhancedtables(
    data = data,
    vars = selected_vars,
    table_type = "summary",
    table_theme = "minimal",
    show_interpretation = FALSE
  )
}

Memory Management 

# Memory-efficient processing
enhancedtables(
  data = histopathology,
  vars = c("Age", "Sex", "Grade_Level"),
  table_theme = "minimal",
  stripe_rows = FALSE,
  show_footnotes = FALSE,
  show_interpretation = FALSE
)

Getting Help

Documentation Resources

  • Function Help: ?enhancedtables for parameter details
  • gt Package Documentation: help(package = "gt") for advanced formatting
  • Clinical Examples: Additional vignettes for specific research contexts

Community Support

  • GitHub Issues: Report bugs and request features
  • Clinical Research Forums: Discuss table design with peers
  • Statistical Consulting: Professional guidance for complex studies

Future Enhancements

Planned Features

Advanced Analytics Integration

  • Survival Tables: Integration with survival analysis results
  • Regression Tables: Automated model summary tables
  • ROC Analysis: Diagnostic test performance tables
  • Meta-analysis: Forest plot data integration

Enhanced Export Options

  • PowerPoint: Direct export to presentation slides
  • Excel: Advanced Excel formatting with formulas
  • PDF: Direct PDF generation with custom styling
  • Interactive Tables: Web-based interactive table widgets

Research Applications

Specialized Clinical Areas

  • Oncology: Tumor response evaluation tables
  • Cardiology: Hemodynamic parameter summaries
  • Neurology: Neurological assessment tables
  • Pathology: Histopathological feature summaries

Regulatory Submissions

  • FDA Templates: Pre-configured FDA submission formats
  • EMA Standards: European regulatory compliance
  • ICH Guidelines: International harmonization standards
  • GCP Compliance: Good clinical practice documentation

Conclusion

The enhanced tables functionality represents a significant advancement in clinical research reporting capabilities. By integrating the sophisticated gt package with ClinicoPath’s clinical focus, researchers gain access to:

  • Professional Quality: Publication-ready tables that meet journal and regulatory standards
  • Statistical Integration: Automated statistical summaries and hypothesis testing
  • Clinical Optimization: Features designed specifically for medical research applications
  • Export Flexibility: Multiple output formats for various publication needs
  • Reproducible Research: Comprehensive documentation and parameter tracking

Key Benefits

  1. Enhanced Productivity: Automated table generation reduces manual formatting work
  2. Improved Quality: Professional appearance and statistical rigor
  3. Clinical Focus: Designed for medical research applications and standards
  4. Flexibility: Comprehensive customization options for different contexts
  5. Integration: Seamless workflow with ClinicoPath analysis ecosystem

Recommendations

  • Start Simple: Begin with basic summary tables before exploring advanced features
  • Validate Output: Compare with manual calculations during initial implementation
  • Document Methods: Maintain detailed table generation logs for reproducibility
  • Follow Standards: Adhere to journal and regulatory formatting requirements
  • Stay Current: Monitor package updates for new features and improvements

Clinical Research Impact

Enhanced tables transform the way clinical researchers present their findings, providing: - Consistency: Standardized formatting across studies and publications - Efficiency: Reduced time from analysis to publication - Quality: Professional appearance that enhances credibility - Compliance: Adherence to regulatory and journal standards - Accessibility: Tables designed for broad accessibility and understanding

For optimal results in clinical research, combine the enhanced tables functionality with appropriate study design, comprehensive data validation, and careful interpretation of results within the clinical context. This approach ensures that tables not only look professional but also accurately communicate important clinical findings to the medical community.