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

arXiv: https://arxiv.org/abs/2006.11641 · HTML: https://mlsanity.com/paper/2006.11641/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jun 2020.

## Built on by (most cited first, 3 of 3)

- [Information Threshold, Bayesian Inference and Decision-Making](https://mlsanity.com/paper/2206.02266/) (2022)
- [A Priori Determination of the Pretest Probability](https://mlsanity.com/paper/2401.04086/) (2024)
- [Context-aware deep learning using individualized prior information reduces false positives in disease risk prediction and longitudinal health assessment](https://mlsanity.com/paper/2510.15591/) (2025)

## Abstract (arXiv)

Bayes' Theorem confers inherent limitations on the accuracy of screening tests as a function of disease prevalence. We have shown in previous work that a testing system can tolerate significant drops in prevalence, up until a certain well-defined point known as the prevalence threshold, below which the reliability of a positive screening test drops precipitously. Herein, we establish a mathematical model to determine whether sequential testing overcomes the aforementioned Bayesian limitations and thus improves the reliability of screening tests. We show that for a desired positive predictive value of ρ that approaches k, the number of positive test iterations ni needed is:  ni =limρ → kleftlceilfraclnleft[fracρ(φ-1)φ(ρ-1)right]lnleft[fraca1-bright]rightrceil where ni = number of testing iterations necessary to achieve ρ, the desired positive predictive value, a = sensitivity, b = specificity, φ = disease prevalence and k = constant. Based on the aforementioned derivation, we provide reference tables for the number of test iterations needed to obtain a ρ(φ) of 50, 75, 95 and 99% as a function of various levels of sensitivity, specificity and disease prevalence.