Article

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.
CI - Copyright (c) 2012 American Congress of Rehabilitation Medicine. Published by
Elsevier Inc. All rights reserved.

Langue : ANGLAIS