Ordinal Bayesian Instrument Development: Expediting the Development of Patient-Reported Outcome Measures

• Activity Number: 263
• Type: Contributed
• Date/Time: Monday, August 10, 2015 : 2:00 PM to 3:50 PM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Lili Garrard* and Larry R. Price and Marjorie J. Bott and Byron J. Gajewski
• Companies: University of Kansas Medical Center and Texas State University and University of Kansas Medical Center and University of Kansas Medical Center
• Keywords: OBID ; Bayesian psychometrics ; ordinal data analysis ; Bayesian item response theory ; survey instrument development ; patient-reported outcome measures

Abstract

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. 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 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 show that the overall performance of OBID (i.e., more reliable parameter estimates, smaller mean squared errors and higher predictive validity) is superior to that of classical approaches when the sample size is small. OBID provides efficiency and confidence for researchers and clinicians in future measurement development.