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Clinical Research Demographics Test Dataset

Source: R/data_tinytable_docs.R
tinytable_clinical_demographics.Rd

Simulated clinical research study demographics dataset with treatment groups, clinical variables, and laboratory values. Designed to test demographic table generation, grouped summaries, and publication-ready formatting typical in clinical research publications.

Usage

tinytable_clinical_demographics

Format

A data frame with 250 observations and 14 variables:

patient_id

Character. Unique patient identifier (PT_001 to PT_250)

age

Integer. Patient age at enrollment (18-90 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 ~3% 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_level

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

hemoglobin

Numeric. Hemoglobin level (8-18 g/dL) with sex-based differences

glucose

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

cholesterol

Integer. Total cholesterol (120-350 mg/dL) with ~5% missing values

Source

Simulated data generated using create_tinytable_test_data.R

Details

This dataset simulates a typical clinical research study baseline characteristics table. It includes realistic distributions for demographic and clinical variables commonly reported in medical publications, with appropriate missing data patterns and clinical correlations.

Key Features:

  • Realistic clinical variable distributions

  • Multiple treatment groups for comparison tables

  • Sex-specific laboratory value ranges

  • Appropriate missing data patterns (3-5%)

  • Multiple study sites for subgroup analysis

Recommended TinyTable Usage:

  • Table Type: "Grouped Summary" or "Descriptive Statistics"

  • Grouping Variable: treatment_group or study_site

  • Variables: age, sex, bmi, systolic_bp, diabetes, smoking_status

  • Themes: "Clinical" or "Publication" for professional appearance

See also

tinytable, tinytable_datasets

Examples

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

# Basic demographic summary table
result <- tinytable(
  data = tinytable_clinical_demographics,
  vars = c("age", "sex", "bmi", "diabetes"),
  table_type = "summary",
  table_theme = "clinical"
)

# Grouped comparison by treatment
result_grouped <- tinytable(
  data = tinytable_clinical_demographics,
  vars = c("age", "bmi", "systolic_bp", "hemoglobin"),
  group_var = "treatment_group",
  table_type = "grouped",
  table_theme = "publication"
)
} # }