English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Bayes' Theorem confers inherent limitations on the accuracy of screeningtests as a function of disease prevalence. We have shown in previous work thata testing system can tolerate significant drops in prevalence, up until acertain well-defined point known as the $prevalence$ $threshold$, below whichthe reliability of a positive screening test drops precipitously. Herein, weestablish a mathematical model to determine whether sequential testingovercomes the aforementioned Bayesian limitations and thus improves thereliability of screening tests. We show that for a desired positive predictivevalue of $\rho$ that approaches $k$, the number of positive test iterations$n_i$ needed is: $ n_i =\lim_{\rho \tok}\left\lceil\frac{ln\left[\frac{\rho(\phi-1)}{\phi(\rho-1)}\right]}{ln\left[\frac{a}{1-b}\right]}\right\rceil$where $n_i$ = number of testing iterations necessary to achieve $\rho$, thedesired positive predictive value, a = sensitivity, b = specificity, $\phi$ =disease prevalence and $k$ = constant. Based on the aforementioned derivation,we provide reference tables for the number of test iterations needed to obtaina $\rho(\phi)$ of 50, 75, 95 and 99$\%$ as a function of various levels ofsensitivity, specificity and disease prevalence.

Bayesian Updating and Sequential Testing: Overcoming Inferential Limitations of Screening Tests