Journal:Archives of Physical Medicine and Rehabilitation
Year, Volume, Issue, Page(s):13, 94, 3, 589-596
This article describes and demonstrates the application of individual growth curve (IGC) analysis methods and highlights the benefits and appropriateness of this approach in modeling rehabilitation outcomes. 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. One of these powerful yet underutilized methodologies is IGC analysis, also known as latent growth curve analysis, hierarchical linear modeling, mixed-effect modeling, random effects modeling, and multilevel modeling. 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.