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| Main Author: | |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2401.04086 |
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Table of Contents:
- 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 $n_θ$ signs or symptoms, the minimal bound of the pretest probability, $ϕ$, can be approximated by: $ϕ\approx \frac{1}{5}{ln\left[\displaystyle\prod_{θ=1}^{i}κ_θ\right]}$ where $ln$ is the natural logarithm, and $κ_θ$ is the likelihood ratio associated with the sign or symptom in question.