method.EK: Endogenous Kink Method

In PublicationBiasBenchmark: Benchmark for Publication Bias Correction Methods

Endogenous Kink Method

Description

Implements the endogenous kink (EK) method proposed by Bom and Rachinger for publication bias correction in meta-analysis. This method modifies the PET-PEESE approach by incorporating a non-linear relationship between publication bias and standard errors through a kinked regression specification. The method recognizes that when the true effect is non-zero, there is minimal publication selection when standard errors are very small (since most estimates are significant), but selection increases as standard errors grow. The kink point is endogenously determined using a two-step procedure based on the confidence interval of the initial effect estimate. See \insertCitebom2019kinked;textualPublicationBiasBenchmark for details.

Usage

## S3 method for class 'EK'
method(method_name, data, settings = NULL)

Arguments

method_name	Method name (automatically passed)
data	Data frame with yi (effect sizes) and sei (standard errors)
settings	List of method settings (no settings version are implemented)

Value

Data frame with EK results

Author(s)

František Bartoš [e-mail]

References

\insertAllCited

Examples

# Generate some example data
data <- data.frame(
  yi = c(0.2, 0.3, 0.1, 0.4, 0.25),
  sei = c(0.1, 0.15, 0.08, 0.12, 0.09)
)

# Apply EK method
result <- run_method("EK", data)
print(result)

PublicationBiasBenchmark documentation built on March 16, 2026, 5:07 p.m.