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Establishing accurate mathematical models of outcome measures is essential in understanding change throughout the rehabilitation process. The goal of this study is to identify the best-fitting descriptive models for a set of commonly adopted outcome measures found within the Traumatic Brain Injury Model Systems National Database where the modeling is based on data submission through 2011 and the complete range of recorded time points since injury for each individual, where time points range from admission to rehabilitation to 20 years postinjury. The statistical methodology and the application of the methodology contained herein may be used to assist researchers and clinicians in (1) modeling the outcome measures considered, (2) modeling various portions of these outcomes by stratification and/or truncating time periods, (3) modeling longitudinal outcome measures not considered, and (4) establishing models as a necessary precursor in conducting individual growth curve analysis., (Copyright © 2013 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.)

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., (Copyright © 2013 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.)