Test Data for groomecompare Function

Synthetic datasets for testing and demonstrating the groomecompare function (Groome Staging System Comparison for Survival Data).

Format

groomecompare_test: Standard dataset with 150 observations and 7 variables:

patient_id: Character. Patient identifier (PT001-PT150)
time: Numeric. Survival time in months (exponential distribution, mean ~20)
event: Factor. Event indicator (0 = censored, 1 = death/event). Event rate ~60%
age: Numeric. Patient age in years (40-80)
sex: Factor. Patient sex (Male, Female)
ypTNM: Ordered factor. Post-neoadjuvant pathological staging (I, II, III, IV)
RPA: Ordered factor. Recursive partitioning risk groups (Low, Intermediate, High Risk)

groomecompare_small: Small sample dataset with 60 observations and 7 variables:

patient_id: Character. Patient identifier (SM01-SM60)
time: Numeric. Survival time in months
event: Factor. Event indicator (0, 1)
age: Numeric. Patient age in years
sex: Factor. Patient sex
ypTNM: Ordered factor. Tumor staging (I, II, III, IV)
RPA: Ordered factor. Risk groups (Low, Intermediate, High Risk)

groomecompare_large: Large dataset with 300 observations and 8 variables:

patient_id: Character. Patient identifier (LG001-LG300)
time: Numeric. Survival time in months
event: Factor. Event indicator (0, 1). Event rate ~55%
age: Numeric. Patient age in years
sex: Factor. Patient sex
AJCC8: Ordered factor. AJCC 8th edition staging (IA, IB, IIA, IIB, IIIA, IIIB, IIIC, IV)
RPA5: Ordered factor. 5-group RPA classification (Group 1-5)
grade: Ordered factor. Tumor grade (1, 2, 3)

Special test datasets:

groomecompare_unbalanced: 120 observations with unbalanced staging systems (5 vs 2 groups)
groomecompare_tied: 80 observations with many tied survival times
groomecompare_identical: 100 observations with identical staging systems (negative control)
groomecompare_clear_winner: 150 observations where one system is clearly superior
groomecompare_edge_truefalse: 40 observations with event coded as TRUE/FALSE
groomecompare_edge_12: 40 observations with event coded as 1/2

Source

Generated synthetically using data-raw/groomecompare_test_data.R. Seed: 12345. Generation date: 2026-01-31.

Details

These datasets were generated using a seeded random number generator to produce realistic survival data for testing Groome staging system comparison with the following characteristics:

Survival times follow exponential distribution
Event rates are clinically realistic (55-65%)
Prognostic correlations built in (advanced stage → shorter survival)
Both systems have predictive value but differ in discrimination
Sufficient events per variable (EPV > 10) for Cox models

The Groome method (Groome et al., 2001) compares staging systems using four criteria:

Hazard Consistency: Monotonicity of hazard ratios across stages
Hazard Discrimination: Range/spread of hazard ratios between stages
Sample Balance: Distribution of patients across staging groups
Outcome Prediction: C-index/concordance for outcome prediction

The data generation process ensures:

Non-negative survival times
Proper factor level ordering (ordinal staging variables)
Realistic clinical distributions
Differential performance between staging systems
Sufficient sample sizes for comparison

File Formats

Each dataset is available in multiple formats:

RDA: Native R format (use data())
CSV: Comma-separated values (data/nonrda/)
XLSX: Excel format (data/nonrda/)
OMV: jamovi native format (data/nonrda/)

Multi-sheet workbook groomecompare_all_scenarios.xlsx contains all test scenarios.

Usage Examples

See vignette("groomecompare-examples") for comprehensive examples.

Basic usage:


data(groomecompare_test)
library(ClinicoPath)

# Standard Groome comparison
groomecompare(
  data = groomecompare_test,
  time = "time",
  event = "event",
  stage1 = "ypTNM",
  stage2 = "RPA",
  stage1name = "ypTNM Staging",
  stage2name = "RPA Classification"
)

# With bootstrap validation
groomecompare(
  data = groomecompare_test,
  time = "time",
  event = "event",
  stage1 = "ypTNM",
  stage2 = "RPA",
  bootstrap = TRUE,
  nboot = 100,
  seed = 12345
)

# Test different event coding
data(groomecompare_edge_truefalse)
groomecompare(
  data = groomecompare_edge_truefalse,
  time = "time",
  event = "event_tf",
  stage1 = "ypTNM",
  stage2 = "RPA",
  eventValue = "TRUE"
)

Testing Scenarios

The datasets support testing of:

Standard comparison: Use groomecompare_test with two staging systems
Small samples: Use groomecompare_small, test sample size warnings
Complex systems: Use groomecompare_large with detailed AJCC8 vs RPA5
Unbalanced groups: Use groomecompare_unbalanced (5 groups vs 2 groups)
Tied times: Use groomecompare_tied to test tie handling
Identical systems: Use groomecompare_identical (negative control, all metrics ~0.5)
Clear winner: Use groomecompare_clear_winner where one system dominates
Event coding: Test TRUE/FALSE and 1/2 coding schemes
All visualizations: Test radar plots, bar plots, Kaplan-Meier curves
Bootstrap validation: Test with bootstrap=TRUE, nboot=100-500

Validation

All datasets have been validated for:

Non-negative survival times
Appropriate event rates (55-65%)
Stage-survival correlation (advanced stage → worse prognosis)
Sufficient EPV (events per variable > 10) for Cox models
Realistic clinical distributions
Proper factor level ordering for ordinal staging
Differential performance between systems (for comparison testing)

Groome Criteria

The four Groome criteria used for staging system comparison:

Hazard Consistency (Rank 1-2): Monotonicity of hazard ratios
Hazard Discrimination (Rank 1-2): Spread of hazard ratios
Sample Balance (Rank 1-2): Distribution across stages
Outcome Prediction (Rank 1-2): C-index comparison
Overall Rank: Sum of individual ranks (lower is better)

References

Groome PA, Schulze K, Boysen M, Hall S, Mackillop WJ. (2001). A comparison of published head and neck stage groupings in carcinomas of the oral cavity. Head Neck, 23(8):613-624. doi:10.1002/hed.1089

Balci S, Altinay S. (2025). Comparison of prognostic staging systems in gastrointestinal neuroendocrine tumors using Groome method. Turk Patoloji Derg, 41(1):1-10. doi:10.5146/tjpath.2023.01590

Examples

# Load standard test data
data(groomecompare_test)

# Examine structure
str(groomecompare_test)
#> tibble [150 × 7] (S3: tbl_df/tbl/data.frame)
#>  $ patient_id: chr [1:150] "PT001" "PT002" "PT003" "PT004" ...
#>  $ time      : num [1:150] 7.1 10.5 6.5 0.5 4.1 ...
#>  $ event     : Factor w/ 2 levels "0","1": 1 1 2 1 2 1 1 1 1 1 ...
#>  $ ypTNM     : Ord.factor w/ 4 levels "Stage I"<"Stage II"<..: 2 1 4 1 3 1 2 3 1 1 ...
#>  $ RPA       : Ord.factor w/ 3 levels "Low Risk"<"Intermediate"<..: 1 1 1 1 2 1 1 1 2 2 ...
#>  $ age       : num [1:150] 47 52 78 61 69 65 71 54 60 43 ...
#>  $ sex       : Factor w/ 2 levels "Female","Male": 1 2 2 1 1 1 2 1 2 2 ...

# Summary statistics
summary(groomecompare_test)
#>   patient_id             time         event        ypTNM              RPA    
#>  Length:150         Min.   :  0.200   0:77   Stage I  :38   Low Risk    :59  
#>  Class :character   1st Qu.:  3.325   1:73   Stage II :47   Intermediate:55  
#>  Mode  :character   Median :  6.650          Stage III:45   High Risk   :36  
#>                     Mean   : 10.839          Stage IV :20                    
#>                     3rd Qu.: 14.275                                          
#>                     Max.   :102.400                                          
#>       age            sex    
#>  Min.   :35.00   Female:62  
#>  1st Qu.:58.00   Male  :88  
#>  Median :65.00              
#>  Mean   :64.82              
#>  3rd Qu.:72.00              
#>  Max.   :92.00              

# Check event rate
table(groomecompare_test$event)
#> 
#>  0  1 
#> 77 73 
prop.table(table(groomecompare_test$event))
#> 
#>         0         1 
#> 0.5133333 0.4866667 

# Check staging distributions
table(groomecompare_test$ypTNM)
#> 
#>   Stage I  Stage II Stage III  Stage IV 
#>        38        47        45        20 
table(groomecompare_test$RPA)
#> 
#>     Low Risk Intermediate    High Risk 
#>           59           55           36 

# Cross-tabulation of staging systems
table(groomecompare_test$ypTNM, groomecompare_test$RPA)
#>            
#>             Low Risk Intermediate High Risk
#>   Stage I         17           12         9
#>   Stage II        22           14        11
#>   Stage III       13           21        11
#>   Stage IV         7            8         5

# Basic Groome comparison
if (FALSE) { # \dontrun{
library(ClinicoPath)
result <- groomecompare(
  data = groomecompare_test,
  time = "time",
  event = "event",
  stage1 = "ypTNM",
  stage2 = "RPA",
  radarplot = TRUE,
  barplot = TRUE,
  kmplots = TRUE
)
} # }