BACKGROUND: Developing valid and reliable @entity1 -reported outcome measures (PROMs) is a critical step in promoting @entity1 -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 @entity245 ( @entity245 ) method that seamlessly integrates expert and @entity1 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 @entity1 ' data. The efficiency of @entity245 is evaluated by comparing its performance to classical instrument development performance using actual and simulation data. RESULTS  AND DISCUSSION : The overall performance of @entity245 (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 @entity245 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 @entity245 approach is more efficient than the classical approach when the sample size is small. @entity245 promises an efficient and reliable method for researchers and clinicians in future PROMs development for small populations or rare diseases.

A novel method for expediting the development of XXXX -reported outcome measures and an evaluation of its performance via simulation.

@entity1 :: ('9606', 'Species') :: ['participants', 'participant', 'patient'] @entity245 :: ('MESH:D002658', 'Disease') :: ['Ordinal Bayesian Instrument Development', 'OBID']
@entity1 :: (9606,Species) :: ['patient']
