Bayesian Estimation in Rehabilitation Medicine: Practical Insights for Clinical Application From Pilot and Randomized...

Bayesian estimation is increasingly applied in rehabilitation research, particularly in studies with small samples. However, its practical utility for interpreting findings and guiding intervention development remains underexplored.

To evaluate how Bayesian estimation supports interpretation of research outcomes and informs rehabilitation intervention design, through comparison with frequentist methods using data from a pilot study and a randomized controlled trial (RCT).

A Bayesian generalized linear mixed model (GLMM) was applied in a pilot study (n = 22) and a frequentist linear mixed model (LMM) was applied in an RCT (n = 72). Both studies used a rehabilitation process intervention designed to adjust the balance between individual abilities and task demands, with Ikigai-9 as the primary outcome. The comparison included point estimates, interval widths, convergence diagnostics and implications for interpreting results and designing trials. Importantly, we did not perform within-dataset comparisons of Bayesian and frequentist estimators; therefore, observed differences cannot be attributed solely to the estimation paradigm. Our aim is to illustrate their distinct roles within a phased framework using sequential real-world studies.

Both studies yielded intervals excluding zero, indicating positive intervention effects. The Bayesian posterior mean (4.44) and frequentist estimate (4.06) were comparable, with stable MCMC convergence. Bayesian estimation provided probability-based insights under data constraints, contributing to RCT design and interpretation.

Bayesian and frequentist methods offer complementary strengths for rehabilitation research. A clear understanding of these approaches can enhance methodological rigor and practical relevance, especially in studies with small samples or uncertainty.