Article

The abundance of time-dependent information contained in the Spinal Cord Injury
and the Traumatic Brain Injury Model Systems National Databases, and the
increased prevalence of repeated-measures designs in clinical trials highlight
the need for more powerful longitudinal analytic methodologies in rehabilitation
research. This article describes the particularly versatile analytic technique of
individual growth curve (IGC) analysis. A defining characteristic of IGC analysis
is that change in outcome such as functional recovery can be described at both
the patient and group levels, such that it is possible to contrast 1 patient with
other patients, subgroups of patients, or a group as a whole. Other appealing
characteristics of IGC analysis include its flexibility in describing how
outcomes progress over time (whether in linear, curvilinear, cyclical, or other
fashion), its ability to accommodate covariates at multiple levels of analyses to
better describe change, and its ability to accommodate cases with partially
missing outcome data. These features make IGC analysis an ideal tool for
investigating longitudinal outcome data and to better equip researchers and
clinicians to explore a multitude of hypotheses. The goal of this special
communication is to familiarize the rehabilitation community with IGC analysis
and encourage the use of this sophisticated research tool to better understand
temporal change in outcomes.
CI - Copyright (c) 2013 American Congress of Rehabilitation Medicine. Published by
Elsevier Inc. All rights reserved.

Langue : ANGLAIS