Comparison of Bayesian and frequentist methods for prevalence estimation under misclassification.

ABSTRACT:

Background

Various methods exist for statistical inference about a prevalence that consider misclassifications due to an imperfect diagnostic test. However, traditional methods are known to suffer from truncation of the prevalence estimate and the confidence intervals constructed around the point estimate, as well as from under-performance of the confidence intervals' coverage.

Methods

In this study, we used simulated data sets to validate a Bayesian prevalence estimation method and compare its performance to frequentist methods, i.e. the Rogan-Gladen estimate for prevalence, RGE, in combination with several methods of confidence interval construction. Our performance measures are (i) error distribution of the point estimate against the simulated true prevalence and (ii) cov

SUBMITTER: Flor M 

PROVIDER: S-EPMC7370479 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature