An introduction to applying individual growth curve models to evaluate change in rehabilitation: a National Institute on Disability and Rehabilitation Research Traumatic Brain Injury Model Systems report.
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.
Center for Rehabilitation Outcomes Research, Rehabilitation Institute of Chicago, IL 60611, USA. akozlowski@ric.org
Allan J
AJ
Kozlowski
Christopher R
CR
Pretz
Kristen
K
Dams-O'Connor
Scott
S
Kreider
Gale
G
Whiteneck
D001930:Brain Injuries / Q000503:physiopathology* / Q000534:rehabilitation*; D004185:Disability Evaluation; D006233:Persons with Disabilities / Q000534:rehabilitation*; D006801:Humans; D015233:Models, Statistical; D020127:Recovery of Function; D012107:Research Design