A Validation Study for Using Iterative Proportional Fitting to Weight the Traumatic Brain Injury Model Systems National Database: An NIDRR-Sponsored Study

Abstract

ObjectiveTo verify that iterative proportional fitting (IPF), or raking, has the desired effect of aligning estimates and parameters so that researches have confidence in population projections when weighting the Traumatic Brian Injury Model Systems National Database. DesignSecondary data analysis using IPF. SettingInpatient rehabilitation. ParticipantsPeople aged 16 years and older with a primary diagnosis of traumatic brain injury receiving initial inpatient rehabilitation. InterventionNot applicable. Main Outcome MeasuresAge at injury, race, sex, marital status, rehabilitation length of stay, payer source, and motor and cognitive FIM scores. ResultsThis study demonstrates the utility of applying IPF to weight the TBI Model System National Database so that results of ensuing statistical analyses better reflect those in the United States who are 16 years and older with a primary diagnosis of TBI and are receiving inpatient rehabilitation. ConclusionsIn general, IPF aligns population estimates on the basis of weighted Traumatic Brian Injury Model Systems data and known population parameters. It is reasonable to assume that IPF has the same effect on unknown variables. This provides confidence to researchers wishing to use IPF for making population projections in analyses.