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 Hong and Reed (2021) . The data-generating mechanism was introduced in Bom and Rachinger (2019) .
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.
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
.
Hong S, Reed WR (2021).
“Using Monte Carlo experiments to select meta-analytic estimators.”
Research Synthesis Methods, 12(2), 192-215.
doi:10.1002/jrsm.1467
.