Accounting for standard errors of vision-specific latent trait in regression models.

Published online

Journal Article (Review)

PURPOSE: To demonstrate the effectiveness of Hierarchical Bayesian (HB) approach in a modeling framework for association effects that accounts for SEs of vision-specific latent traits assessed using Rasch analysis. METHODS: A systematic literature review was conducted in four major ophthalmic journals to evaluate Rasch analysis performed on vision-specific instruments. The HB approach was used to synthesize the Rasch model and multiple linear regression model for the assessment of the association effects related to vision-specific latent traits. The effectiveness of this novel HB one-stage "joint-analysis" approach allows all model parameters to be estimated simultaneously and was compared with the frequently used two-stage "separate-analysis" approach in our simulation study (Rasch analysis followed by traditional statistical analyses without adjustment for SE of latent trait). RESULTS: Sixty-six reviewed articles performed evaluation and validation of vision-specific instruments using Rasch analysis, and 86.4% (n = 57) performed further statistical analyses on the Rasch-scaled data using traditional statistical methods; none took into consideration SEs of the estimated Rasch-scaled scores. The two models on real data differed for effect size estimations and the identification of "independent risk factors." Simulation results showed that our proposed HB one-stage "joint-analysis" approach produces greater accuracy (average of 5-fold decrease in bias) with comparable power and precision in estimation of associations when compared with the frequently used two-stage "separate-analysis" procedure despite accounting for greater uncertainty due to the latent trait. CONCLUSIONS: Patient-reported data, using Rasch analysis techniques, do not take into account the SE of latent trait in association analyses. The HB one-stage "joint-analysis" is a better approach, producing accurate effect size estimations and information about the independent association of exposure variables with vision-specific latent traits.

Full Text

Duke Authors

Cited Authors

  • Wong, WL; Li, X; Li, J; Wong, TY; Cheng, C-Y; Lamoureux, EL

Published Date

  • July 11, 2014

Published In

Volume / Issue

  • 55 / 9

Start / End Page

  • 5848 - 5854

PubMed ID

  • 25015350

Pubmed Central ID

  • 25015350

Electronic International Standard Serial Number (EISSN)

  • 1552-5783

Digital Object Identifier (DOI)

  • 10.1167/iovs.14-14195

Language

  • eng

Conference Location

  • United States