Understanding regression analysis: what is inside the box?

Reproductive medicine research now uses more advanced analyses and increasingly relies on multivariable regression to compare IVF treatments and adjust for baseline differences. This has improved our ability to compare treatment strategies, account for clinical differences between patients and learn from complex data. At the same time, it has created a gap between what is written in scientific papers and what many clinicians can easily understand. Regression analysis is widely used in reproductive medicine because IVF outcomes are influenced by many factors at the same time, such as age, ovarian reserve and embryo quality. Many studies report adjusted results based on regression models, and terms such as covariates, confounders, mediators and colliders frequently appear. However, the logic behind these analyses is not always clear to clinicians who use the results in daily practice. This manuscript aims to explain what lies 'inside the box' of regression analysis in IVF research. The goal is to help clinicians understand what regression can and cannot answer, what 'adjusted' really means and how to interpret regression results with clarity and caution.