Bayesian Carlisle's Test for Baseline Balance

Computes a Bayes factor comparing genuine randomisation (uniform p-values) to fabrication (non-uniform).

Usage

bayes_carlisle_test(p_values, prior_a = 1, prior_b = 1, n_grid = 200)

Arguments

p_values

Numeric vector. P-values from baseline comparisons.

prior_a, prior_b

Numeric. Beta prior shape parameters (default 1, 1).

n_grid

Integer. Grid size for numerical integration (default 200).

Value

A list with components:

bf_too_similar

BF for p-values biased towards 1.

bf_too_different

BF for p-values biased towards 0.

bf_nonuniform

Overall BF for non-uniformity.

posterior_prob_fabrication

Posterior probability.

posterior_mean_p

Mean of observed p-values.

n_comparisons

Number of comparisons.

interpretation

Evidence description.

Examples

bayes_carlisle_test(c(0.45, 0.12, 0.78, 0.33, 0.91))
#> $bf_too_similar
#> [1] 0.07146978
#> 
#> $bf_too_different
#> [1] 0.05348527
#> 
#> $bf_nonuniform
#> [1] 0.06351762
#> 
#> $posterior_prob_fabrication
#> [1] 0.05972409
#> 
#> $posterior_mean_p
#> [1] 0.518
#> 
#> $n_comparisons
#> [1] 5
#> 
#> $interpretation
#> [1] "consistent_with_randomisation"
#> 
bayes_carlisle_test(c(0.93, 0.81, 0.95, 0.94, 0.85, 0.95))
#> $bf_too_similar
#> [1] 578.1149
#> 
#> $bf_too_different
#> [1] 0.04764676
#> 
#> $bf_nonuniform
#> [1] 287.6387
#> 
#> $posterior_prob_fabrication
#> [1] 0.9965355
#> 
#> $posterior_mean_p
#> [1] 0.905
#> 
#> $n_comparisons
#> [1] 6
#> 
#> $interpretation
#> [1] "strong_evidence_too_similar"
#>