Article

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.
CI - Copyright (c) 2015 American Congress of Rehabilitation Medicine. Published by
Elsevier Inc. All rights reserved.

Langue : ANGLAIS