Special communication| Volume 93, ISSUE 8, SUPPLEMENT , S138-S153, August 2012

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Developing and Evaluating Prediction Models in Rehabilitation Populations


      Seel RT, Steyerberg EW, Malec JF, Sherer M, Macciocchi SN. Developing and evaluating prediction models in rehabilitation populations.
      This article presents a 3-part framework for developing and evaluating prediction models in rehabilitation populations. First, a process for developing and refining prognostic research questions and the scientific approach to prediction models is presented. Primary components of the scientific approach include the study design and sampling of patients, outcome measurement, selecting predictor variable(s), minimizing methodologic sources of bias, assuring a sufficient sample size for statistical power, and selecting an appropriate statistical model. Examples focus on prediction modeling using samples of rehabilitation patients. Second, a brief overview for statistically building and validating multivariable prediction models is provided, which includes the following 7 steps: data inspection, coding of predictors, model specification, model estimation, model performance, model validation, and model presentation. Third, we propose a set of primary considerations for evaluating prediction model studies using specific quality indicators as criteria to help stakeholders evaluate the quality of a prediction model study. Lastly, we offer perspectives on the future development and use of rehabilitation prediction models.

      Key Words

      List of Abbreviations:

      AAN (American Academy of Neurology), CDE (common data element), EPV (events per variable), IRT (item response theory), LASSO (least absolute shrinkage and selection operator), NINDS (National Institute on Neurological Disorders and Stroke), RCT (randomized controlled trial), ROC (receiver operating curve), TBI (traumatic brain injury)
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