Bayesian Thinking in Rehabilitation Research

To describe key conceptual differences between frequentist and Bayesian statistical approaches and illustrate their relevance to rehabilitation research, where treatment effects are often modest and clinical decision-making occurs under uncertainty.

Perspective article comparing inferential frameworks with emphasis on interpretation rather than mathematical formulation. Illustrative examples include neuromodulation studies and a Bayesian reanalysis of the MISTIE III trial.

Frequentist methods, centered on P values and confidence intervals, provide established tools for indirect assessment of treatment effects and remain central to trial interpretation, but do not directly estimate the probability that a treatment is beneficial. Bayesian approaches combine prior evidence with observed data to estimate posterior probabilities, including the probability of any benefit or benefit exceeding a clinically meaningful threshold. In illustrative examples, Bayesian interpretation provided a complementary lens for characterizing uncertainty beyond binary significant/nonsignificant conclusions.

Bayesian methods complement traditional analyses by providing directly interpretable probabilities of treatment benefit and supporting decision-making under uncertainty. Their conclusions depend on prior assumptions and do not replace rigorous trial design, frequentist inference, or replication.