Observational studies allow researchers to understand the natural history of rheumatic conditions, risk factors for disease development, factors affecting important disease-related outcomes and estimate treatment effect from real world data. However, this design carries a risk of confounding bias. A propensity score is a balancing score that aims to minimize the difference between study groups and consequently potential confounding effects. The score can be applied in one of four methods in observational research: matching, stratification, adjustment, and inverse probability weighting. Systemic lupus erythematosus (SLE) is a rare disease characterized by relatively small sample size and/or low event rates. In this article, we review propensity score methods. We demonstrate application of propensity score methods to achieve study group balance in a rare disease using an example of risk of infection in SLE patients with hypogammaglobulinemia.