Article

Deriving clinical prediction rules (CPRs) to identify specific characteristics of
patients who would likely respond to certain interventions has become a research
priority in physical rehabilitation. Understanding the appropriate statistical
principles and methods of analyses underlying the derivation of CPRs is important
for future rehabilitation research and clinical applications. In this article, we
aimed to provide an overview of statistical techniques used for the derivation of
CPRs to predict success following physical therapy interventions and to generate
recommendations for improvements in CPR derivation research and statistical
analysis in rehabilitation. We have summarized the current state of CPR
intervention-related research by reviewing 26 studies.
A common technique was
found in most studies and included univariate association of factors with
treatment success, stepwise logistic regression to determine the most
parsimonious set of predictors for success, and calculation of accuracy
statistics (focusing on positive likelihood ratios). We identified several
shortcomings related to inadequate ratio of events by number of predictors, lack
of standardization regarding acceptable interobserver reliability of predictors,
questionable handling of predictors including reliance on univariate analysis and
early categorization, and not accounting for dependence and collinearity of
predictors in multivariable model construction. Interpretation of the derived
CPRs was found to be difficult due to lack of precision of estimates and
paradoxical findings when a subset of the predictors yielded a larger positive
likelihood ratio than did the full set of predictors.
Finally, we make
recommendations regarding how to strengthen the use of statistical principles and
methods to create consistency across rehabilitation research for CPR derivations.
CI - Copyright (c) 2014 American Congress of Rehabilitation Medicine. Published by
Elsevier Inc. All rights reserved.

Langue : ANGLAIS