title: The Accuracy of Computerized Adaptive Testing in Heterogeneous Populations: A Mixture Item-Response Theory Analysis.
abstract: BACKGROUND
  Computerized adaptive testing (CAT) utilizes latent variable measurement model parameters that are typically assumed to be equivalently applicable to all people. Biased latent variable scores may be obtained in samples that are heterogeneous with respect to a specified measurement model. We examined the implications of sample heterogeneity with respect to CAT-predicted patient-reported outcomes (PRO) scores for the measurement of pain.
  
  
  METHODS
  A latent variable mixture modeling (LVMM) analysis was conducted using data collected from a heterogeneous sample of people in British Columbia, Canada, who were administered the 36 pain domain items of the CAT-5D-QOL. The fitted LVMM was then used to produce data for a simulation analysis. We evaluated bias by comparing the referent PRO scores of the LVMM with PRO scores predicted by a "conventional" CAT (ignoring heterogeneity) and a LVMM-based "mixture" CAT (accommodating heterogeneity).
  
  
  RESULTS
  The LVMM analysis indicated support for three latent classes with class proportions of 0.25, 0.30 and 0.45, which suggests that the sample was heterogeneous. The simulation analyses revealed differences between the referent PRO scores and the PRO scores produced by the "conventional" CAT. The "mixture" CAT produced PRO scores that were nearly equivalent to the referent scores.
  
  
  CONCLUSIONS
  Bias in PRO scores based on latent variable models may result when population heterogeneity is ignored. Improved accuracy could be obtained by using CATs that are parameterized using LVMM.
authors.affiliation: School of Nursing, Trinity Western University, Langley, British Columbia, Canada.|Centre for Health Evaluation and Outcomes Science, Providence Health Care Research Institute, Vancouver, British Columbia, Canada.
authors.forename: Richard
authors.initials: R
authors.lastname: Sawatzky
authors.affiliation: Faculty of Education, University of British Columbia, Vancouver, British Columbia, Canada.
authors.forename: Pamela A
authors.initials: PA
authors.lastname: Ratner
authors.affiliation: School of Population and Public Health, University of British Columbia, Vancouver, British Columbia, Canada.|Arthritis Research Centre of Canada, Vancouver, British Columbia, Canada.
authors.forename: Jacek A
authors.initials: JA
authors.lastname: Kopec
authors.affiliation: Measurement, Evaluation & Research Methodology, University of British Columbia, Vancouver, British Columbia, Canada.
authors.forename: Amery D
authors.initials: AD
authors.lastname: Wu
authors.affiliation: Measurement, Evaluation & Research Methodology, University of British Columbia, Vancouver, British Columbia, Canada.|University of British Columbia, Vancouver, British Columbia, Canada.
authors.forename: Bruno D
authors.initials: BD
authors.lastname: Zumbo
mesh_terms: D003936:Diagnosis, Computer-Assisted; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D009010:Monte Carlo Method; D010147:Pain Measurement / Q000379:methods* / Q000592:standards; D011582:Psychological Theory; D011795:Surveys and Questionnaires / Q000592:standards; D016896:Treatment Outcome