title: How item banks and their application can influence measurement practice in rehabilitation medicine: a PROMIS fatigue item bank example.
abstract: OBJECTIVE
  To illustrate how measurement practices can be advanced by using as an example the fatigue item bank (FIB) and its applications (short forms and computerized adaptive testing [CAT]) that were developed through the National Institutes of Health Patient Reported Outcomes Measurement Information System (PROMIS) Cooperative Group.
  
  
  METHODS
  Psychometric analysis of data collected by an Internet survey company using item response theory-related techniques.
  
  
  METHODS
  A U.S. general population representative sample collected through the Internet.
  
  
  METHODS
  Respondents used for dimensionality evaluation of the PROMIS FIB (N=603) and item calibrations (N=14,931).
  
  
  METHODS
  Not applicable.
  
  
  METHODS
  Fatigue items (112) developed by the PROMIS fatigue domain working group, 13-item Functional Assessment of Chronic Illness Therapy-Fatigue, and 4-item Medical Outcomes Study 36-Item Short Form Health Survey Vitality scale.
  
  
  RESULTS
  The PROMIS FIB version 1, which consists of 95 items, showed acceptable psychometric properties. CAT showed consistently better precision than short forms. However, all 3 short forms showed good precision for most participants in that more than 95% of the sample could be measured precisely with reliability greater than 0.9.
  
  
  CONCLUSIONS
  Measurement practice can be advanced by using a psychometrically sound measurement tool and its applications. This example shows that CAT and short forms derived from the PROMIS FIB can reliably estimate fatigue reported by the U.S. general population. Evaluation in clinical populations is warranted before the item bank can be used for clinical trials.
authors.affiliation: Department of Medical Social Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA. js-lai@northwestern.edu
authors.forename: Jin-Shei
authors.initials: JS
authors.lastname: Lai
authors.forename: David
authors.initials: D
authors.lastname: Cella
authors.forename: Seung
authors.initials: S
authors.lastname: Choi
authors.forename: Doerte U
authors.initials: DU
authors.lastname: Junghaenel
authors.forename: Christopher
authors.initials: C
authors.lastname: Christodoulou
authors.forename: Richard
authors.initials: R
authors.lastname: Gershon
authors.forename: Arthur
authors.initials: A
authors.lastname: Stone
mesh_terms: D002908:Chronic Disease; D003627:Data Interpretation, Statistical; D004185:Disability Evaluation; D005221:Fatigue / Q000453:epidemiology / Q000534:rehabilitation*; D006801:Humans; D007256:Information Systems; D020407:Internet; D009316:National Institutes of Health (U.S.); D017063:Outcome Assessment, Health Care; D011594:Psychometrics; D011788:Quality of Life; D012046:Rehabilitation / Q000592:standards*; D012650:Self Disclosure; D011795:Surveys and Questionnaires; D014481:United States / Q000453:epidemiology