A validation study for using iterative proportional fitting to weight the Traumatic Brain Injury Model Systems National Database: an NIDRR-sponsored study.

ABSTRACT

OBJECTIVE: To 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. DESIGN: Secondary data analysis using IPF. SETTING: Inpatient rehabilitation. PARTICIPANTS: People aged 16 years and older with a primary diagnosis of traumatic brain injury receiving initial inpatient rehabilitation. INTERVENTION Not applicable. MAIN OUTCOME MEASURES: Age at injury, race, sex, marital status, rehabilitation length of stay, payer source, and motor and cognitive FIM scores. RESULTS: This 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. CONCLUSIONS: In 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.