Propensity score matching is a way for researchers to make two groups in a study more similar when the study isn’t randomized or is retrospective. It’s often used in IVF research, where you can’t randomly assign patients to certain treatments.
The method works by matching people with similar baseline characteristics, like age, BMI, infertility type, or treatment protocol, so the only main difference left between groups is what the researchers want to study.
For example, if one group transferred good quality embryos and another transferred poor quality ones, propensity score matching would pair patients of similar age and medical history from each group. This helps make the comparison fairer and reduces bias.
Because some participants may not have a good match, it often reduces the total number of people included in the final analysis. This helps improve balance between groups but can lower the study’s statistical power.
Propensity score matching makes results from retrospective studies more reliable, but it can’t account for hidden factors that weren’t measured, so it’s still not as strong as a randomized controlled trial.