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

Abstract：

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.

journal_name

Invest Ophthalmol Vis Sci

journal_title

Investigative ophthalmology & visual science

authors

Wong WL,Li X,Li J,Wong TY,Cheng CY,Lamoureux EL

doi

10.1167/iovs.14-14195

subject

Has Abstract

pub_date

2014-07-11 00:00:00

pages

5848-54

issue

eissn

0146-0404

issn

1552-5783

pii

iovs.14-14195

journal_volume

pub_type

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