title: A novel method for expediting the development of patient-reported outcome measures and an evaluation of its performance via simulation.
abstract: BACKGROUND
  Developing valid and reliable patient-reported outcome measures (PROMs) is a critical step in promoting patient-centered health care, a national priority in the U.S. Small populations or rare diseases often pose difficulties in developing PROMs using traditional methods due to small samples.
  
  
  METHODS
  To overcome the small sample size challenge while maintaining psychometric soundness, we propose an innovative Ordinal Bayesian Instrument Development (OBID) method that seamlessly integrates expert and participant data in a Bayesian item response theory (IRT) with a probit link model framework. Prior distributions obtained from expert data are imposed on the IRT model parameters and are updated with participants' data. The efficiency of OBID is evaluated by comparing its performance to classical instrument development performance using actual and simulation data. RESULTS AND DISCUSSION : The overall performance of OBID (i.e., more reliable parameter estimates, smaller mean squared errors (MSEs) and higher predictive validity) is superior to that of classical approaches when the sample size is small (e.g. less than 100 subjects). Although OBID may exhibit larger bias, it reduces the MSEs by decreasing variances. Results also closely align with recommendations in the current literature that six subject experts will be sufficient for establishing content validity evidence. However, in the presence of highly biased experts, three experts will be adequate.
  
  
  CONCLUSIONS
  This study successfully demonstrated that the OBID approach is more efficient than the classical approach when the sample size is small. OBID promises an efficient and reliable method for researchers and clinicians in future PROMs development for small populations or rare diseases.
authors.affiliation: Department of Biostatistics, University of Kansas Medical Center, Mail Stop 1026, 3901 Rainbow Blvd., Kansas City, KS, 66160, USA. lgarrard@kumc.edu.
authors.forename: Lili
authors.initials: L
authors.lastname: Garrard
authors.affiliation: College of Education, Texas State University, San Marcos, TX, 78666, USA. lprice@txstate.edu.
authors.forename: Larry R
authors.initials: LR
authors.lastname: Price
authors.affiliation: University of Kansas School of Nursing, Mail Stop 4043, 3901 Rainbow Blvd., Kansas City, KS, 66160, USA. mbott@kumc.edu.
authors.forename: Marjorie J
authors.initials: MJ
authors.lastname: Bott
authors.affiliation: Department of Biostatistics, University of Kansas Medical Center, Mail Stop 1026, 3901 Rainbow Blvd., Kansas City, KS, 66160, USA. bgajewski@kumc.edu.|University of Kansas School of Nursing, Mail Stop 4043, 3901 Rainbow Blvd., Kansas City, KS, 66160, USA. bgajewski@kumc.edu.
authors.forename: Byron J
authors.initials: BJ
authors.lastname: Gajewski
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D003198:Computer Simulation; D006801:Humans; D063868:Patient Outcome Assessment; D057566:Self Report; D016896:Treatment Outcome