WAAPWLS (Weighted Average of Adequately Powered Studies) Method

Implements the WAAP-WLS method for meta-analysis, which combines WLS and WAAP approaches. First fits a WLS model to all studies, then identifies high-powered studies based on the criterion that the WLS estimate divided by 2.8 is greater than or equal to the standard error. If at least 2 high-powered studies are found, uses WAAP (weighted average of adequate power studies only), otherwise uses the original WLS estimate. See Stanley et al. (2017) for details.

Usage

# S3 method for class 'WAAPWLS'
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 WAAPWLS results

References

Stanley TD, Doucouliagos H, Ioannidis JP (2017). “Finding the power to reduce publication bias.” Statistics in Medicine, 36(10), 1580-1598. doi:10.1002/sim.7228 .

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 WAAPWLS method
result <- run_method("WAAPWLS", data)
print(result)
#>              method  estimate standard_error  ci_lower  ci_upper    p_value BF
#> (Intercept) WAAPWLS 0.2179928     0.04998789 0.1200165 0.3159691 0.01205352 NA
#>             convergence note selected_method n_high_powered method_setting
#> (Intercept)        TRUE   NA             WLS              0        default