A Bayesian Probabilistic Framework for Identification of Individuals with Dyslexia.

ABSTRACT:

Purpose

Bayesian-based models for diagnosis are common in medicine but have not been incorporated into identification models for dyslexia. The purpose of the present study was to evaluate Bayesian identification models that included a broader set of predictors and that capitalized on recent developments in modeling the prevalence of dyslexia.

Method

Model-based meta-analysis was used to create a composite correlation matrix that included common predictors of dyslexia such as decoding, phonological awareness, oral language, but also included response to intervention (RTI) and family risk for dyslexia. Bayesian logistic regression models were used to predict poor reading comprehension, unexpectedly poor reading comprehension, poor decoding, and unexpectedly poor decoding, all

SUBMITTER: Wagner RK 

PROVIDER: S-EPMC9851422 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature