Rapid detection of differential item functioning in assessments of health-related quality of life: The Functional Assessment of Cancer Therapy
- 142 Downloads
Reason for study
Differential item functioning (DIF) occurs when a test item functions differently in different groups when controlling for the level of the underlying construct measured by the test. DIF assessment is a first step in the evaluation of test bias. We sought to demonstrate a rapid hybrid approach to DIF detection by determining the presence and scale-level impact of DIF related to eight covariates in four domains measured by the Functional Assessment of Cancer Therapy (FACT).
The number of items found with DIF in each domain depended on the criterion chosen to define the presence of DIF. With a few exceptions, scale-level differential functioning was similar regardless of the criteria chosen. For physical well-being, there was relevant scale-level differential functioning related only to race. For social and family well-being, there was relevant scale-level differential functioning related to each of the covariates. For emotional well-being, there was relevant scale-level differential functioning related to ethnicity, language, and race. For functional well-being, there was relevant scale-level differential functioning related to ethnicity, race, education, and self- vs. interviewer-administration.
Our rapid hybrid approach to DIF detection may be broadly applicable in other studies of health-related quality of life.
KeywordsDifferential item functioning item response theory ordinal logistic regression test bias
differential item functioning. DIF occurs when item has different statistical properties in different groups when controlling for the underlying trait or ability measured by the test
Functional Assessment of Cancer Therapy; FACT-G is the FACT-General. This is a widely used assessment system for functioning and well-being. It assesses five domains, four of which are analyzed in this paper: physical well-being (PWB), social and family well-being (SFWB), emotional well-being (EWB), and functional well-being (FWB)
health-related quality of life
item response theory. This is a technique for analyzing item-level test data based on the premise that item responses are a function of the relationship between an underlying latent trait and characteristics of the item
Drs. Crane and Gibbons and Ms. Narasimhalu were supported by grant K08 022232 from the National Institute on Aging. Drs. Cella and Lai were supported by R01 61679 from the National Cancer Institute. A portion of this work was presented at the 2005 International Society for Quality of Life Research in San Francisco.
- 1.Camilli G, Shepard LA (1994) Methods for Identifying Biased Test Items. Sage, Thousand OaksGoogle Scholar
- 3.Holland PW, Wainer H (eds) (1993) Differential Item Functioning. Erlbaum, Hillsdale, NJGoogle Scholar
- 6.Prieto L, Novick D, Sacristan JA, Edgell ET, Alonso J. A Rasch model analysis to test the cross-cultural validity of the EuroQoL-5D in the Schizophrenia Outpatient Health Outcomes Study. Acta Psychiatr Scand Suppl 2003; 24–29Google Scholar
- 24.Crane PK, Gibbons LE, Jolley L, van Belle G (2006) DIF analysis with ordinal logistic regression: DIFdetect. Med Care (in press)Google Scholar
- 26.Cella D (1994) Manual for the Functional Assessment of Cancer Therapy (FACT) and Functional Assessment of HIV Infection (FAHI) Scales (Version 3). Rush-Presbyterian-St. Luke’s Medical Center, ChicagoGoogle Scholar
- 28.Cella DF, Bonomi AE. The Functional Assessment of Cancer Therapy (FACT) and Functional Assessment of HIV Infection (FAHI) quality of life measurement system. In: Spilker B (ed.), Quality of Life and Pharmacoeconomics in Clinical Trials. NY: Lippincott-Raven, 1996.Google Scholar
- 31.Cella DF (1997) Manual for the Functional Assessment of Chronic Illness Therapy (FACIT) Scales (Version 4). Center on Outcomes, Research, and Education (CORE), Evanston Northwestern Healthcare and Northwestern University, Evanston, ILGoogle Scholar
- 33.Woodcock RW, Johnson MB (1990) Woodcock–Johnson Psycho-educational Battery. DLM Teaching Resources, Allen, TXGoogle Scholar
- 34.Muraki E, Bock D. PARSCALE for Windows, version 4.1. Chicago: Scientific Software International, 2003.Google Scholar
- 35.Samejima F. Estimation of latent ability using a response pattern of graded scores. Psychometrika Monograph 1969; No. 17.Google Scholar
- 36.Samejima F. Graded response model. In: van der Linden WJ, Hambleton RK (eds.), Handbook of Modern Item Response Theory. NY: Springer, 1997Google Scholar
- 37.StataCorp. Stata statistical software: release 8.0.Google Scholar
- 38.McCullagh P, Nelder JA (1989) Generalized linear models. Chapman and Hall, LondonGoogle Scholar
- 44.Yost KJ, Sorensen MV, Hahn EA, Glendenning GA, Gnanasakthy A, Cella D (2005) Using multiple anchor- and distribution-based estimates to evaluate clinically meaningful change on the Functional Assessment of Cancer Therapy-Biologic Response Modifiers (FACT-BRM) instrument. Value Health 8: 117–27CrossRefPubMedGoogle Scholar
- 45.Shealy RT, Stout WF. An item response theory model for test bias and differential test functioning. In: Holland PW, Wainer H (eds.), Differential item functioning. Hillsdale, N.J.: Erlbaum, 1993Google Scholar
- 48.Smedley BD, Stith AY, Nelson AR. Unequal treatment: confronting racial and ethnic disparities in health care. In: Smedley BD, Stith AY, Nelson AR (eds), Washington, DC.: National Academy Press, 2002Google Scholar