dgm.Bom2019: Bom and Rachinger (2019) Data-Generating Mechanism

In PublicationBiasBenchmark: Benchmark for Publication Bias Correction Methods

Bom and Rachinger (2019) Data-Generating Mechanism

Description

Simulates univariate regression environments to estimate the effect of X1 on Y (parameter alpha1). Effect heterogeneity is introduced via an omitted variable (X2) correlated with X1, whose coefficient (alpha2) is randomly distributed with mean zero and variance sigma2_h.

The description and code is based on \insertCitehong2021using;textualPublicationBiasBenchmark. The data-generating mechanism was introduced in \insertCitebom2019kinked;textualPublicationBiasBenchmark.

Usage

## S3 method for class 'Bom2019'
dgm(dgm_name, settings)

Arguments

dgm_name	DGM name (automatically passed)
settings	List containing mean_effect Mean effect effect_heterogeneity Mean effect heterogeneity bias Proportion of studies affected by publication bias n_studies Number of effect size estimates sample_sizes Sample sizes of the effect size estimates. A vector of sample sizes needs to be supplied. The sample sizes in the vector are sequentially reused until all effect size estimates are generated.

Details

This function simulates univariate regression environments, focusing on estimating the effect of a variable X1 on a dependent variable Y, represented by the parameter alpha1. The simulation introduces variation in the standard errors of estimated effects by allowing sample sizes to differ across primary studies. Effect heterogeneity is modeled through an omitted variable (X2) that is correlated with X1, where the coefficient on the omitted variable, alpha2, is randomly distributed across studies with mean zero and variance sigma2_h.

Publication selection is modeled in two regimes: (1) no selection, and (2) 50% selection. Under 50% selection, each estimate has a 50% chance of being evaluated for inclusion. If selected, only positive and statistically significant estimates are published; otherwise, new estimates are generated until this criterion is met. This process continues until the meta-analyst’s sample reaches its predetermined size.

Value

Data frame with

yi

effect size

sei

standard error

ni

sample size

es_type

effect size type

Author(s)

František Bartoš [e-mail] (adapted from Hong and Reed 2021)

References

\insertAllCited

See Also

dgm(), validate_dgm_setting()

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