Propensity Scores for Observational Studies: Putting Real World Data and Experience to Good Use in Spine Research!

Study Design Narrative Review. Objectives Observational studies using real-world data (RWD) have become increasingly popular, though they are susceptible to selection bias. Propensity score methods offer a powerful statistical approach to mitigate bias by balancing patient characteristics across treatment groups. This review demystifies the four primary propensity score techniques and highlights the growing role of a newer strategy called inverse probability weighting in registry-based spine research. Methods We explore the applications of propensity score methods in spine surgery research through the presentation of a number of hypothetical and real-world examples from recent literature. Further, we compare their utility to traditional analytic techniques such as multivariable regression. Results The four primary applications are (1) covariate adjustment using the propensity score, (2) stratification based on the propensity score, (3) matching on the propensity score, and (4) inverse probability of treatment weighting. These techniques aid in the minimization of confounding leading to spurious results, allowing for similar effects to randomization within the setting of observational research. Conclusions While propensity score methods are not a substitute for randomization, these tools provide an essential framework for strengthening causal inference assessments when randomized controlled trials (RCTs) are not feasible or appropriate. When RCTs are practical, propensity score methods may aid spine care practitioners in deriving objective, complementary findings from observational studies of RWD. Inverse probability of treatment methods are particularly promising due to their greater sample size efficiency, capability for multivariable comparisons, and potentially reduced bias compared to traditional propensity score methods.