# A Priori Determination of the Pretest Probability

arXiv: https://arxiv.org/abs/2401.04086 · HTML: https://mlsanity.com/paper/2401.04086/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 48 references that are in the map.
- First posted on arXiv 8 Jan 2024.

## Builds on (most cited first, 6 of 6)

- [Prevalence Threshold and the Geometry of Screening Curves](https://mlsanity.com/paper/2006.00398/) (2020)
- [Bayesian Updating and Sequential Testing: Overcoming Inferential Limitations of Screening Tests](https://mlsanity.com/paper/2006.11641/) (2020)
- [On the Formalism of The Screening Paradox](https://mlsanity.com/paper/2011.06032/) (2020)
- [Derivation of Generalized Equations for the Predictive Value of Sequential Screening Tests](https://mlsanity.com/paper/2007.13046/) (2020)
- [Low incidence rate of COVID-19 undermines confidence in estimation of the vaccine efficacy](https://mlsanity.com/paper/2101.10005/) (2021)
- [Information Threshold, Bayesian Inference and Decision-Making](https://mlsanity.com/paper/2206.02266/) (2022)

## Abstract (arXiv)

In this manuscript, we present various proposed methods estimate the prevalence of disease, a critical prerequisite for the adequate interpretation of screening tests. To address the limitations of these approaches, which revolve primarily around their a posteriori nature, we introduce a novel method to estimate the pretest probability of disease, a priori, utilizing the Logit function from the logistic regression model. This approach is a modification of McGee's heuristic, originally designed for estimating the posttest probability of disease. In a patient presenting with nθ signs or symptoms, the minimal bound of the pretest probability, φ, can be approximated by: φ ≈ frac15lnleft[displaystyleprodθ=1ikappaθright] where ln is the natural logarithm, and kappaθ is the likelihood ratio associated with the sign or symptom in question.