Premium
Using R and WinBUGS to fit a generalized partial credit model for developing and evaluating patient‐reported outcomes assessments
Author(s) -
Li Yuelin,
Baser Ray
Publication year - 2012
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.4475
Subject(s) - item response theory , computer science , polytomous rasch model , econometrics , exposition (narrative) , food and drug administration , psychometrics , psychology , statistics , artificial intelligence , mathematics , art , literature
The US Food and Drug Administration recently announced the final guidelines on the development and validation of patient‐reported outcomes (PROs) assessments in drug labeling and clinical trials. This guidance paper may boost the demand for new PRO survey questionnaires. Henceforth, biostatisticians may encounter psychometric methods more frequently, particularly item response theory (IRT) models to guide the shortening of a PRO assessment instrument. This article aims to provide an introduction on the theory and practical analytic skills in fitting a generalized partial credit model (GPCM) in IRT. GPCM theory is explained first, with special attention to a clearer exposition of the formal mathematics than what is typically available in the psychometric literature. Then, a worked example is presented, using self‐reported responses taken from the international personality item pool. The worked example contains step‐by‐step guides on using the statistical languages r and WinBUGS in fitting the GPCM. Finally, the Fisher information function of the GPCM model is derived and used to evaluate, as an illustrative example, the usefulness of assessment items by their information contents. This article aims to encourage biostatisticians to apply IRT models in the re‐analysis of existing data and in future research. Copyright © 2012 John Wiley & Sons, Ltd.