A brief note on the common (fixed)-effect meta-analysis model

Meta-analysis is a statistical method used to combine results from multiple studies, providing a quantitative summary of their findings. One of the fundamental decisions in conducting a meta-analysis is choosing an appropriate model to estimate the overall effect size and its CI. In this article, we focus on the common-effect (also referred to as the fixed-effect) model, and in a companion article, the random-effects model. These models are the two prevailing meta-analysis models employed in the literature. In this article, we outline the key assumption underlying the common-effect model, describe different common-effect methods (ie, inverse variance, Peto, and Mantel-Haenszel), and highlight characteristics of the meta-analysis that should be considered when selecting a method. Furthermore, we demonstrate the application of these methods to a dataset. Understanding the common-effect model is important for knowing when to use the model and how to interpret the overall effect size and its CI.