Bayesian Inference: Statistical Gimmick or Added Value?

Propensity Score Methods in Rare Disease: A Demonstration Using Observational Data in Systemic Lupus Erythematosus

Observational studies allow researchers to understand the natural history of rheumatic conditions, risk factors for disease development, and factors affecting important disease-related outcomes, and to estimate treatment effect from real-world data. However, this design carries a risk of confounding bias. A propensity score (PS) is a balancing score that aims to minimize the difference between study groups and consequently potential confounding effects. The score can be applied in 1 of 4 methods in observational research: matching, stratification, adjustment, and inverse probability weighting. Systemic lupus erythematosus (SLE) is a rare disease characterized by a relatively small sample size and/or low event rates. In this article, we review the PS methods. We demonstrate application of the PS methods to achieve study group balance in a rare disease using an example of risk of infection in SLE patients with hypogammaglobulinemia.

Applied Bayesian Methods in the Rheumatic Diseases

The use of applied Bayesian methods is increasing in rheumatology. Using the Bayes theorem, past evidence is updated with new data. Preexisting data are expressed as a prior probability distribution or prior. New observations are expressed as a likelihood. Through explicit incorporation of preexisting data and new data, this process informs how this new information should change the way we think. In this article, the authors highlight the use of applied Bayesian methods in the study of rheumatic diseases.