Endogenous Kink Method

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 Bom and Rachinger (2019) 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

References

Bom PR, Rachinger H (2019). “A kinked meta-regression model for publication bias correction.” Research Synthesis Methods, 10(4), 497-514. doi:10.1002/jrsm.1352 .

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)
#>   method   estimate standard_error   ci_lower ci_upper   p_value BF convergence
#> 1     EK -0.1360294      0.1863442 -0.7290598 0.457001 0.5182334 NA        TRUE
#>   note method_setting
#> 1   NA        default