Adding New Methods"
In PublicationBiasBenchmark: Benchmark for Publication Bias Correction Methods

knitr::opts_chunk$set(
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This vignette explains how to add new publication bias correction methods to the PublicationBiasBenchmark package. In the following, we will use the PET (Precision-Effect Test) method as an example.

Overview

Each method in the package consists of three key components:

Main method function: Implements the statistical method
Settings function: Defines available method configurations
Extra columns function: Specifies additional result columns

All three functions must be implemented in a single file named method-{METHOD_NAME}.R in the R/ directory. Implementation of these three functions allows users to apply the method via the run_method() function.

File Structure and Naming

For a method called "PET", you need to create a file named R/method-PET.R containing three functions:

method.PET(): The main implementation
method_settings.PET(): Available settings/configurations
method_extra_columns.PET(): Additional result columns

The naming pattern is crucial for the package's S3 method dispatch system to work correctly.

1. Main Method Function: `method.{METHOD_NAME}()`

This is the core function that implements your statistical method. Here is the PET implementation as an example:

#' @title PET (Precision-Effect Test) Method
#'
#' @description
#' Implements the Precision-Effect Test for publication bias correction.
#' PET regresses effect sizes against standard errors to test for and correct
#' publication bias. The intercept represents the bias-corrected effect size
#' estimate.
#'
#' @param method_name Method name (automatically passed)
#' @param data Data frame with yi (effect sizes) and sei (standard errors)
#' @param settings List of method settings
#'
#' @return Data frame with PET results
#'
#' @export
method.PET <- function(method_name, data, settings = NULL) {

  # Extract data
  effect_sizes    <- data$yi
  standard_errors <- data$sei

  # Input validation and error handling
  if (length(effect_sizes) < 3)
    stop("At least 3 estimates required for PET analysis", call. = FALSE)

  if (stats::var(standard_errors) <= 0)
    stop("No variance in standard errors", call. = FALSE)

  # Implement the statistical method
  pet_model <- stats::lm(effect_sizes ~ standard_errors, 
                        weights = 1/standard_errors^2)

  # Extract and process results
  coefficients    <- stats::coef(pet_model)
  se_coefficients <- summary(pet_model)$coefficients[, "Std. Error"]
  p_values        <- summary(pet_model)$coefficients[, "Pr(>|t|)"]

  # Main estimates
  estimate    <- coefficients[1]  # Intercept = bias-corrected effect
  estimate_se <- se_coefficients[1]
  estimate_p  <- p_values[1]

  # Additional method-specific results
  bias_coefficient <- coefficients[2]
  bias_p_value     <- p_values[2]

  # Calculate confidence intervals
  estimate_lci <- estimate - 1.96 * estimate_se
  estimate_uci <- estimate + 1.96 * estimate_se

  # Return standardized results
  return(data.frame(
    method         = method_name,
    estimate       = estimate,
    standard_error = estimate_se,
    ci_lower       = estimate_lci,
    ci_upper       = estimate_uci,
    p_value        = estimate_p,
    BF             = NA,
    convergence    = TRUE,
    note           = NA,
    # Method-specific columns
    bias_coefficient = bias_coefficient,
    bias_p_value     = bias_p_value
  ))
}

Key Requirements for the Main Function:

Input Parameters:

method_name: Automatically passed by the framework
data: Data frame with yi (effect sizes), sei (standard errors), and ni (sample sizes).
settings: Optional list of method-specific settings

Output: Must return a data frame with these required columns:

method: Method name
estimate: Meta-analytic effect size estimate
standard_error: Standard error of the estimate
ci_lower: Lower confidence interval bound (95%)
ci_upper: Upper confidence interval bound (95%)
p_value: P-value for the estimate
BF: Bayes factor for the estimate
convergence: Logical indicating successful convergence
note: Character string with notes

If your method does not provide certain values (e.g., Bayes factor), use NA.

Error Handling:

Include input validation and meaningful error messages
Use stop() with call. = FALSE for user-friendly errors
The framework handles errors automatically - your function can throw errors. The package will catch the errors and attach them to an empty output with convergence = FALSE and the error message in note.

2. Settings Function: `method_settings.{METHOD_NAME}()`

This function defines the available configurations for your method:

#' @export
method_settings.PET <- function(method_name) {

  settings <- list(
    "default" = list() # PET has no configurable settings
  )

  return(settings)
}

The selected settings are passed to your main function as the settings parameter. There, the main function can use these settings to adjust its behavior. Once defined, these settings cannot be changed retrospectively to ensure reproducibility and continuity of the benchmark.

For an example of a method that defines several settings we can examine the random effects meta-analysis (RMA) method:

# Example with multiple settings (from RMA method)
method_settings.RMA <- function(method_name) {

  settings <- list(
    "default" = list(
      method = "REML", 
      test.uni = "knha", 
      test.mv = "t", 
      control = list(stepadj = 0.5, maxiter = 500)
    )
  )

  return(settings)
}

3. Extra Columns Function: `method_extra_columns.{METHOD_NAME}()`

This function specifies additional columns that your method returns beyond the required ones:

#' @export
method_extra_columns.PET <- function(method_name) {
  c("bias_coefficient", "bias_p_value")
}

The column names must match exactly the additional columns that your main function returns. These columns will be included in the final output data frame alongside the required columns and guarantee that the results can be merged in case the method fails with an error.

Use character(0) if your method has no extra columns

Using Your New Method

Once implemented, your method can be used through a unified interface:

# Create 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)
)

# Run your method
result <- run_method("PET", data)
print(result)

# Use specific settings (if available)
result <- run_method("PET", data, "default")