Tests whether the distribution of p-values for baseline comparisons is consistent with genuine randomisation.
Usage
carlisle_test(p_values, method = c("fisher", "ks"))Value
A list with components:
- too_similar
Logical. Suspiciously well-balanced.
- too_different
Logical. Suspiciously imbalanced.
- combined_p
Combined p-value.
- n_comparisons
Number of comparisons.
- method
Method used.
- interpretation
"plausible","too_similar", or"too_different".
Details
Implements INSPECT-SR check 4.3. Under genuine randomisation, baseline p-values should be approximately uniform. Fabricated trials often show implausibly well-matched groups (p-values near 1).
References
Carlisle JB (2017). Data fabrication and other reasons for non-random sampling in 5087 randomised, controlled trials in anaesthetic and general medical journals. Anaesthesia, 72(8), 944-952.
Examples
carlisle_test(c(0.45, 0.12, 0.78, 0.33, 0.91))
#> $too_similar
#> [1] FALSE
#>
#> $too_different
#> [1] FALSE
#>
#> $combined_p
#> [1] 0.443096
#>
#> $n_comparisons
#> [1] 5
#>
#> $method
#> [1] "fisher"
#>
#> $interpretation
#> [1] "plausible"
#>
carlisle_test(c(0.92, 0.88, 0.95, 0.91, 0.87))
#> $too_similar
#> [1] TRUE
#>
#> $too_different
#> [1] FALSE
#>
#> $combined_p
#> [1] 0.00016601
#>
#> $n_comparisons
#> [1] 5
#>
#> $method
#> [1] "fisher"
#>
#> $interpretation
#> [1] "too_similar"
#>