Skip to contents

Clinical Research Demographics with Treatment Groups

Source: R/data_toolssummary_docs.R
toolssummary_clinical_demographics.Rd

Simulated clinical research study demographics dataset with multiple treatment groups, comprehensive clinical variables, and realistic missing data patterns. Designed to test demographic table generation, grouped summaries, cross-tabulations, and publication-ready formatting typical in clinical research using summarytools enhanced features.

Usage

toolssummary_clinical_demographics

Format

A data frame with 300 observations and 16 variables:

patient_id

Character. Unique patient identifier (PT_0001 to PT_0300)

age

Integer. Patient age at enrollment (18-85 years)

sex

Factor. Patient sex ("Male", "Female")

treatment_group

Factor. Treatment assignment ("Control", "Treatment A", "Treatment B")

study_site

Factor. Study enrollment site ("Site_A" to "Site_F")

bmi

Numeric. Body mass index (16-45 kg/m²) with ~5% missing values

systolic_bp

Integer. Systolic blood pressure (90-200 mmHg)

diastolic_bp

Integer. Diastolic blood pressure (60-120 mmHg)

diabetes

Factor. Diabetes status ("No", "Type 1", "Type 2")

smoking_status

Factor. Smoking history ("Never", "Former", "Current")

education

Ordered Factor. Education level ("Less than HS" < "High School" < "Some College" < "Bachelor's" < "Graduate")

hemoglobin

Numeric. Hemoglobin level (8-18 g/dL) with ~3% missing values

glucose

Integer. Fasting glucose (70-400 mg/dL)

cholesterol

Integer. Total cholesterol (120-350 mg/dL) with ~8% missing values

enrollment_date

Date. Study enrollment date (2023-01-01 to 2024-12-31)

followup_months

Integer. Follow-up duration (1-24 months)

Source

Simulated data generated using create_toolssummary_test_data.R

Details

This dataset simulates a comprehensive clinical research study baseline characteristics table with realistic distributions and appropriate missing data patterns. It's specifically designed to showcase summarytools capabilities including dfSummary comprehensive overviews, freq tables for categorical variables, descr for numeric summaries, and ctable for cross-tabulations by treatment groups.

Key Features:

  • Realistic clinical variable distributions with physiological ranges

  • Balanced treatment groups for meaningful comparisons

  • Multiple categorical variables for frequency analysis

  • Ordered factors for proper categorical handling

  • Missing data patterns reflecting real clinical studies

  • Date variables for temporal analysis

summarytools Integration Testing:

  • dfSummary: Complete data frame overview with variable distributions

  • freq: Categorical variable frequency tables (sex, treatment_group, diabetes)

  • descr: Comprehensive descriptive statistics for numeric variables

  • ctable: Cross-tabulations by treatment group or study site

Recommended Usage Scenarios:

  • Grouped summaries by treatment_group or study_site

  • Cross-tabulation analysis for categorical associations

  • Missing data pattern assessment

  • Publication-ready demographic tables

See also

toolssummary, toolssummary_datasets

Examples

if (FALSE) { # \dontrun{
# Load the dataset
data(toolssummary_clinical_demographics)

# Basic enhanced summary with summarytools
result <- toolssummary(
  data = toolssummary_clinical_demographics,
  vars = c("age", "sex", "bmi", "diabetes", "treatment_group"),
  useSummarytools = TRUE,
  showDfSummary = TRUE,
  showDescr = TRUE,
  showFreq = TRUE
)

# Grouped analysis by treatment
result_grouped <- toolssummary(
  data = toolssummary_clinical_demographics,
  vars = c("age", "bmi", "systolic_bp", "diabetes"),
  groupVar = "treatment_group",
  useSummarytools = TRUE,
  showCrosstabs = TRUE
)
} # }