The growing emphasis on evidence-based methods in rehabilitation medicine calls for increase in the sophistication of study design and analytic methods across the discipline. To properly evaluate new treatment options, a physiatrist needs to be able to separate treatment effects from parallel changes that occur over time and variations that may be due to subject demographics. Simple t tests may not be appropriate where observations may vary randomly across different institutions participating in a multicenter trial, or the same rehabilitation course may lead to different outcomes because of various factors. In the analysis of any rehabilitation program, these random variations must be accounted for to receive accurate results. In this short review, we focus in one of the most common approaches that are appropriate to account for these variations, namely, linear mixed effect models.
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