Likelihood Ratio Test for Publication Bias – a Proof of Concept

Publication bias poses a serious challenge to the integrity of scientific research and meta-analyses. There exist persistent methodological obstacles for estimating this bias, especially with heterogeneous dataset, where studies vary widely in methodologies and effect sizes. To address this gap, I propose a Likelihood Ratio Test for Publication Bias, a statistical method designed to detect and quantify publication bias in datasets of heterogeneous studies results. I also show the proof-of-concept implementation developed in Python and simulations that evaluate the performance. The results demonstrate that this new method clearly outperforms existing methods like Z-Curve 2 and the Caliper test in estimating the magnitude of publication bias, showing higher precision and reliability, with still some space for improvement due to spotted errors in the implemented algorithm. While inherent challenges in publication bias detection remain, such as the influence of different research practices and the need for large sample sizes, the Likelihood Ratio Test offers a significant advancement in addressing these issues.