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Get started with pharmhand

Source: vignettes/pharmhand.Rmd
pharmhand.Rmd

pharmhand creates clinical tables and reports from ADaM datasets using S7 classes.

Installation 

# install.packages("pak")
pak::pak("sims1253/pharmhand")

Quick Start (Fastest Way)

Generate complete reports in a single call:

library(pharmhand)

# Load example ADaM datasets
library(pharmaverseadam)
adsl <- pharmaverseadam::adsl
adae <- pharmaverseadam::adae

# One-line demographics report
quick_demographics_report(adsl, "demographics.docx")

# One-line safety report with multiple tables
quick_safety_report(adae, adsl, "safety.docx",
                   include_overview = TRUE,
                   include_soc = TRUE,
                   include_sae = TRUE)

That’s it! The functions automatically: - Accept data.frames directly (no S7 wrapping needed) - Use sensible defaults (configurable via options()) - Create formatted Word documents with multiple tables

Core Workflow (Detailed Control)

For more control, build tables and reports step-by-step:

Note: Scalar inputs must be non-missing. NA values should be removed or checked using assert_no_na() before passing to validation functions.

Simple: Pass data.frames directly 

library(pharmhand)

# Most functions accept data.frames directly
demo_table <- create_demographics_table(
  adsl,
  title = "Table 1.1: Demographics and Baseline Characteristics"
)

demo_table@flextable    # Formatted flextable
demo_table@metadata     # Analysis details

Advanced: Wrap ADaM data for population filtering 

# For population filtering, wrap in ADaMData
adam_data <- ADaMData(
  data = adsl,
  domain = "ADSL",
  population = "SAF",
  trt_var = "TRT01A"
)

adam_data@filtered_data  # Filtered to SAFFL == "Y"
adam_data@trt_n          # Treatment group N's

# Pass to table functions
demo_table <- create_demographics_table(adam_data)

Create plots 

# Example data for Kaplan-Meier plots
adtte <- pharmaverseadam::adtte

km_plot <- create_km_plot(
  data = adtte,
  time_var = "AVAL",
  event_var = "CNSR",
  trt_var = "TRT01P",
  title = "Figure 1: Kaplan-Meier Plot of Overall Survival",
  risk_table = TRUE,
  show_median = TRUE
)

km_plot@plot            # ggplot2/ggsurvplot object

Build reports 

report <- ClinicalReport(
  study_id = "STUDY-001",
  study_title = "Phase III Clinical Trial"
)

efficacy_section <- ReportSection(
  title = "Efficacy Results",
  section_type = "efficacy"
)

efficacy_section <- add_content(efficacy_section, demo_table)
efficacy_section <- add_content(efficacy_section, km_plot)
report <- add_section(report, efficacy_section)

generate_word(report, "clinical_study_report.docx")

G-BA Module 4 Compliance 

# Create a Module 4 template table
module4_table <- create_hta_module4_table()

# Apply G-BA formatting and validate
module4_table <- to_gba_template(module4_table)
check_gba_compliance(module4_table, strict = FALSE)

Configuration

Customize package defaults for your study:

# Set once at the start of your script
options(
  pharmhand.trt_var = "ARM",         # Treatment variable
  pharmhand.autofit = TRUE,          # Auto-fit columns
  pharmhand.conf_level = 0.95,       # Confidence level
  pharmhand.n_top = 20               # Top N for common AEs
)

# All functions now use your preferences
create_demographics_table(adsl)        # Uses ARM
create_ae_summary_table(adae, type = "common")  # Shows top 20
meta_analysis(yi, sei)                 # Uses 95% CI

Available options: - pharmhand.trt_var - Treatment variable (default: “TRT01P”) - pharmhand.subject_var - Subject ID variable (default: “USUBJID”) - pharmhand.autofit - Auto-fit table columns (default: TRUE) - pharmhand.conf_level - Confidence level (default: 0.95) - pharmhand.n_top - Top N for common tables (default: 15) - pharmhand.na_string - NA display string (default: “–”)

More examples