Skip to contents

Small Sample Edge Cases Dataset

Source: R/data_toxicityprofile_docs.R
toxicityprofile_small_sample.Rd

Minimal adverse event dataset with very small sample size designed for edge case testing, validation of statistical methods with limited data, and assessment of function robustness with minimal observations. Essential for testing graceful degradation and error handling.

Usage

toxicityprofile_small_sample

Format

A data frame with 15 adverse events from 15 patients and 8 variables:

patient_id

Character. Simple patient identifier (SML_01 to SML_15)

treatment_group

Factor. Binary treatment assignment ("A", "B")

adverse_event

Factor. Limited adverse events (3 types: "Fatigue", "Nausea", "Rash")

toxicity_grade

Integer. CTCAE grade (1-3 range for testing)

system_organ_class

Factor. Basic SOC categories

time_to_event

Integer. Days to AE onset (1-30 days range)

patient_age

Integer. Adult age range (25-70 years)

patient_sex

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

Source

Simulated data generated using create_toxicityprofile_test_data.R

Details

This minimal dataset tests the robustness of toxicity profile analysis with very small sample sizes, which can reveal edge cases in statistical calculations, visualization algorithms, and summary statistics. It validates graceful handling of insufficient data scenarios common in early-phase trials or rare disease studies.

Clinical Context:

  • Minimal sample size scenario (N=15)

  • Simple two-arm comparison

  • Limited adverse event types

  • Basic severity grading

  • Short assessment timeframe

Key Characteristics:

  • Only 15 total adverse events

  • Three basic adverse event types

  • Simple binary treatment comparison

  • Small cell counts for statistical testing

  • Minimal data for visualization algorithms

Testing Scenarios:

  • Statistical method robustness with small samples

  • Visualization algorithm edge cases

  • Confidence interval calculation with limited data

  • Group comparison with small cell counts

  • Error handling and graceful degradation

  • Minimum viable analysis requirements

Expected Behaviors:

  • Appropriate handling of small sample statistics

  • Clear visualization despite limited data

  • Robust confidence interval calculations

  • Appropriate warnings for limited statistical power

  • Graceful handling of empty cells in cross-tabulations

Recommended Analysis Scenarios:

  • Small sample robustness testing

  • Edge case validation

  • Statistical method verification

  • Minimum sample size assessment

  • Error handling validation

  • Algorithm stability testing

See also

toxicityprofile, toxicityprofile_datasets

Examples

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

# Minimal sample analysis
result <- toxicityprofile(
  data = toxicityprofile_small_sample,
  patientID = "patient_id",
  adverseEvent = "adverse_event",
  grade = "toxicity_grade",
  treatment = "treatment_group",
  plotType = "stacked_bar"
)

# Edge case testing with group comparison
result_comparison <- toxicityprofile(
  data = toxicityprofile_small_sample,
  patientID = "patient_id",
  adverseEvent = "adverse_event",
  grade = "toxicity_grade",
  treatment = "treatment_group",
  groupComparison = TRUE,
  showConfidenceIntervals = TRUE
)

# Minimal visualization testing
result_minimal <- toxicityprofile(
  data = toxicityprofile_small_sample,
  patientID = "patient_id",
  adverseEvent = "adverse_event",
  grade = "toxicity_grade",
  plotType = "dot_plot",
  minIncidence = 1
)
} # }