Bayesian Latent Class Models for Evaluating the Validity of Claim-based Definitions of Disease Outcomes.

ABSTRACT:

Background

Large electronic databases have been widely used in recent years; however, they can be susceptible to bias due to incomplete information. To address this, validation studies have been conducted to assess the accuracy of disease diagnoses defined in databases. However, such studies may be constrained by potential misclassification in references and the interdependence between diagnoses from the same data source.

Methods

This study employs latent class modeling with Bayesian inference to estimate the sensitivity, specificity, and positive/negative predictive values of different diagnostic definitions. Four models are defined with/without assumptions of the gold standard and conditional independence, and then compared with breast cancer study data as a motivating exa

SUBMITTER: Uno S 

PROVIDER: S-EPMC11668686 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature