Check and Compare Statistical Test Results

Calculates and compares three equivalent test statistics from weighted survival analysis: the squared standardized weighted log-rank statistic, the log-rank chi-squared statistic, and the squared Cox model z-score. These should be approximately equal under correct implementation.

Usage

check_results(dfcount, verbose = TRUE)

Arguments

dfcount

A list or data frame from df_counting containing:

lr

Weighted log-rank statistic

sig2_lr

Variance of the weighted log-rank statistic

z.score

Standardized z-score from Cox model

logrank_results

List containing chisq, the log-rank chi-squared statistic

verbose

Logical; if TRUE, prints the comparison table to the console. Default: TRUE.

Value

A data frame with one row and three columns, returned invisibly:

zlr_sq

Squared standardized weighted log-rank: \((lr / \sqrt{sig2\_lr})^2\)

logrank_chisq

Chi-squared statistic from log-rank test

zCox_sq

Squared z-score from Cox model: \(z.score^2\)

Details

This function serves as a diagnostic tool to verify computational consistency. The three statistics should be numerically equivalent (within rounding error): $$(lr / \sqrt{sig2\_lr})^2 \approx logrank\_chisq \approx z.score^2$$

Discrepancies between these values may indicate:

• Numerical instability in variance estimation

• Incorrect weighting scheme application

• Data processing errors

Note

This function is primarily used for package development and validation. End users typically don't need to call it directly.

See also

df_counting for generating the input object

Examples

# \donttest{
# After running df_counting
library(survival)
data(veteran)
#> Warning: data set ‘veteran’ not found
veteran$treat <- as.numeric(veteran$trt) - 1

result <- df_counting(
  df = veteran,
  tte.name = "time",
  event.name = "status",
  treat.name = "treat"
)

# Check consistency of test statistics
check_results(result)
#> 
#> Test Statistic Comparison:
#> (These should be approximately equal)
#> 
#>       zlr_sq logrank_chisq     zCox_sq
#>  0.008227343   0.008227343 0.008227343
#> 
#> Relative difference: 1.423e-11%

# Store results without printing
stats_comparison <- check_results(result, verbose = FALSE)
print(stats_comparison)
#>        zlr_sq logrank_chisq     zCox_sq
#> 1 0.008227343   0.008227343 0.008227343
# }

# Simple example with constructed data
dfcount_example <- list(
  lr = 2.5,
  sig2_lr = 1.0,
  z.score = 2.5,
  logrank_results = list(chisq = 6.25)
)
check_results(dfcount_example)
#> 
#> Test Statistic Comparison:
#> (These should be approximately equal)
#> 
#>  zlr_sq logrank_chisq zCox_sq
#>    6.25          6.25    6.25
#> 
#> Relative difference: 0%