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Wan Ling Wong, Xiang Li, Jialiang Li, Tien Yin Wong, Ching-Yu Cheng, Ecosse L. Lamoureux; Accounting for Standard Errors of Vision-Specific Latent Trait in Regression Models. Invest. Ophthalmol. Vis. Sci. 2014;55(9):5848-5854. doi: https://doi.org/10.1167/iovs.14-14195.
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© ARVO (1962-2015); The Authors (2016-present)
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.
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).
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.
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.
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