Tracking Functional Status Across the Spinal Cord Injury Lifespan: Linking Pediatric and Adult Patient-Reported Outcome Scores



      To use item response theory (IRT) methods to link scores from 2 recently developed contemporary functional outcome measures, the adult Spinal Cord Injury–Functional Index (SCI-FI) and the Pedi SCI (both the parent version and the child version).


      Secondary data analysis of the physical functioning items of the adult SCI-FI and the Pedi SCI instruments. We used a nonequivalent group design with items common to both instruments and the Stocking-Lord method for the linking. Linking was conducted so that the adult SCI-FI and Pedi SCI scaled scores could be compared.




      This study included a total sample of 1558 participants. Pedi SCI items were administered to a sample of children (n=381) with SCI aged 8 to 21 years, and of parents/caregivers (n=322) of children with SCI aged 4 to 21 years. Adult SCI-FI items were administered to a sample of adults (n=855) with SCI aged 18 to 92 years.


      Not applicable.

      Main Outcome Measures

      Five scales common to both instruments were included in the analysis: Wheelchair, Daily Routine/Self-care, Daily Routine/Fine Motor, Ambulation, and General Mobility functioning.


      Confirmatory factor analysis and exploratory factor analysis results indicated that the 5 scales are unidimensional. A graded response model was used to calibrate the items. Misfitting items were identified and removed from the item banks. Items that function differently between the adult and child samples (ie, exhibit differential item functioning) were identified and removed from the common items used for linking. Domain scores from the Pedi SCI instruments were transformed onto the adult SCI-FI metric.


      This IRT linking allowed estimation of adult SCI-FI scale scores based on Pedi SCI scale scores and vice versa; therefore, it provides clinicians with a means of tracking long-term functional data for children with an SCI across their entire lifespan.


      List of abbreviations:

      CFA (confirmatory factor analysis), CFI (comparative fit index), DIF (differential item functioning), EFA (exploratory factor analysis), IRT (item response theory), ISNCSCI (International Standards for Neurological Classification of Spinal Cord Injury), MDS (minimal data set), PRO (patient-reported outcome), SCI (spinal cord injury), SCI-FI (Spinal Cord Injury–Functional Index), TLI (Tucker-Lewis index)
      To read this article in full you will need to make a payment


      Subscribe to Archives of Physical Medicine and Rehabilitation
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect


        • Williams B.C.
        • Li Y.
        • Fries B.E.
        • Warren R.L.
        Predicting patient scores between the Functional Independence Measure and the minimum data set: development and performance of a FIM-MDS “crosswalk.”.
        Arch Phys Med Rehabil. 1997; 78: 48-54
        • Buchanan J.L.
        • Andres P.L.
        • Haley S.M.
        • Paddock S.M.
        • Zaslavsky A.M.
        An assessment tool translation study.
        Health Care Financ Rev. 2003; 24: 45-60
        • Buchanan J.L.
        • Andres P.L.
        • Haley S.M.
        • Paddock S.M.
        • Zaslavsky A.M.
        Evaluating the planned substitution of the minimum data set-post acute care for use in the rehabilitation hospital prospective payment system.
        Med Care. 2004; 42: 155-163
        • Hambleton R.K.
        Applications of item response theory to improve health outcomes assessment: developing item banks, linking instruments, and computer-adaptive testing.
        in: Lipscomb J. Gotay C.C. Snyder C. Outcomes assessment in cancer. Cambridge University Pr, Cambridge2005: 445-464
        • Dorans N.J.
        Linking scores from multiple health outcome instruments.
        Qual Life Res. 2007; 16: 85-94
        • Calhoun C.L.
        • Haley S.M.
        • Riley A.
        • Vogel L.C.
        • McDonald C.M.
        • Mulcahey M.J.
        Development of items designed to evaluate activity performance and participation in children and adolescents with spinal cord injury.
        Int J Pediatr. 2009; : 854-904
        • Slavin M.D.
        • Kisala P.A.
        • Jette A.M.
        • Tulsky D.S.
        Developing a contemporary functional outcome measure for spinal cord injury research.
        Spinal Cord. 2010; 48: 262-267
        • Tulsky D.S.
        • Jette A.M.
        • Kisala P.A.
        • et al.
        Spinal Cord Injury-Functional Index: item banks to measure physical functioning in individuals with spinal cord injury.
        Arch Phys Med Rehabil. 2012; 93: 1722-1732
        • Jette A.M.
        • Tulsky D.S.
        • Ni P.
        • et al.
        Development and initial evaluation of the Spinal Cord Injury-Functional Index.
        Arch Phys Med Rehabil. 2012; 93: 1733-1750
        • American Spinal Injury Association
        International Standards for Neurological Classification of Spinal Cord Injury, revised 2011.
        American Spinal Injury Association, Atlanta2011
      1. Kolen MJ, Brennan RL. Test equating, scaling, and linking: methods and practices. 2nd ed. New York: Springer-Verlag; 2004:372.

        • Reeve B.B.
        • Hays R.D.
        • Bjorner J.B.
        • et al.
        Psychometric evaluation and calibration of health-related quality of life item banks: plans for the Patient-Reported Outcomes Measurement Information System (PROMIS).
        Med Care. 2007; 45: S22-31
        • Thissen D.
        • Reeve B.B.
        • Bjorner J.B.
        • Chang C.H.
        Methodological issues for building item banks and computerized adaptive scales.
        Qual Life Res. 2007; 16: 109-119
        • Hu L.
        • Bentler P.M.
        Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives.
        Struct Equ Modeling. 1999; 6: 1-55
        • Browne M.W.
        • Cudeck R.
        Alternative ways of assessing model fit.
        in: Bollen K.A. Long J.S. Testing structural equation models. Sage, Newbury Park1993: 136-162
        • Samejima F.
        Graded response model.
        in: van der Linden W.J. Hambleton R.K. Handbook of modern item response theory. Springer, New York1997: 85-100
        • Muraki E.
        • Bock R.D.
        PARSCALE: IRT item analysis and test scoring for rating—scale data.
        Scientific Software International, Chicago1997
        • Stone C.A.
        Empirical power and type I error rates for an IRT fit statistic that considers the precision of ability estimates.
        Educ Psychol Meas. 2003; 63: 566-583
        • Zumbo B.D.
        A handbook on the theory and methods of differential item functioning (DIF): logistic regression modeling as a unitary framework for binary and Likert-type (ordinal) item scores.
        Directorate of Human Resources Research and Evaluation, Department of National Defense, Ottawa1999
        • Jodoin M.G.
        • Gierl M.J.
        Evaluating type I error and power rates using an effect size measure with the logistic regression procedure for DIF detection.
        Appl Meas Educ. 2001; 4: 329-349
        • Mulcahey M.J.
        • Calhoun C.L.
        • Tian F.
        • Ni P.
        • Vogel L.C.
        • Haley S.M.
        Evaluation of newly developed item banks for child-reported outcomes of participation following spinal cord injury.
        Spinal Cord. 2012; 50: 915-919
        • Vetter T.R.
        • Bridgewater C.L.
        • McGwin Jr., G.
        An observational study of patient versus parental perceptions of health-related quality of life in children and adolescents with a chronic pain condition: who should the clinician believe?.
        Health Qual Life Outcomes. 2012; 10: 85
        • Varni J.W.
        • Limbers C.A.
        • Burwinkle T.M.
        Parent proxy-report of their children's health-related quality of life: an analysis of 13,878 parents' reliability and validity across age subgroups using the PedsQL 4.0 Generic Core Scales.
        Health Qual Life Outcomes. 2007; 5: 2
        • Gabbe B.J.
        • Simpson P.M.
        • Sutherland A.M.
        • et al.
        Agreement between parent and child report of health-related quality of life: impact of time postinjury.
        J Trauma. 2010; 69: 1578-1582
        • Mulcahey M.J.
        • Calhoun C.
        • Riley A.
        • Haley S.
        Children’s report of activity and participation after sustaining a spinal cord injury: a cognitive interview study.
        Dev Neurorehabil. 2009; 12: 191-200
        • Drasgow F.
        An evaluation of marginal maximum likelihood estimation for the two-parameter logistic model.
        Appl Psychol Meas. 1998; 13: 77-90
        • Harwell M.R.
        • Janosky J.E.
        An empirical study of the effects of small datasets and varying prior variances on item parameter estimation in BILOG.
        Appl Psychol Meas. 1991; 15: 279-291
        • Chuah S.C.
        • Drasgow F.
        • Luecht R.
        How big is big enough? Sample size requirements for CAST item parameter estimation.
        Appl Meas Educ. 2006; 19: 241-255
        • Reise S.P.
        • Yu J.
        Parameter recovery in the graded response model using MULTILOG.
        J Educ Meas. 1990; 27: 133-144