SM (Selection Models) Method

Implements selection models for publication bias correction in meta-analysis. The method first fits a random effects meta-analysis model, then applies selection modeling to adjust for publication bias using the metafor package. Selection models account for the probability that studies are published based on their p-values or effect sizes. See Vevea and Hedges (1995) for details.

Usage

# S3 method for class 'SM'
method(method_name, data, settings)

Arguments

method_name

Method name (automatically passed)

data

Data frame with yi (effect sizes) and sei (standard errors)

settings

List of method settings (see Details)

Value

Data frame with SM results

Details

The following settings are implemented

"default" or "3PSM"

3-parameter step function selection model with Maximum Likelihood estimator (method = "ML") and one step at one-sided p = 0.025 (i.e., selection for significance))

"4PSM"

4-parameter step function selection model with Maximum Likelihood estimator (method = "ML") and two steps at one-sided p = 0.025 and p = 0.50 (i.e., selection for significance and direction of the effect)

References

Vevea JL, Hedges LV (1995). “A general linear model for estimating effect size in the presence of publication bias.” Psychometrika, 60(3), 419–435. doi:10.1007/BF02294384 .

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 SM method
result <- run_method("SM", data, "3PSM")
#> Error : Optimizer (optim) did not achieve convergence (convergence = 1).
print(result)
#>   method estimate standard_error ci_lower ci_upper p_value BF convergence
#> 1     SM       NA             NA       NA       NA      NA NA       FALSE
#>                                                                         note
#> 1 Error : Optimizer (optim) did not achieve convergence (convergence = 1).\n
#>   tau_estimate tau_ci_lower tau_ci_upper tau_p_value bias_coefficient
#> 1           NA           NA           NA          NA               NA
#>   bias_coefficient_se bias_p_value method_setting
#> 1                  NA           NA           3PSM