A Priori Determination of the Pretest Probability

Abstract

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 $n_{\theta }$ signs or symptoms, the minimal bound of the pretest probability, $\varphi$, can be approximated by: $\varphi \approx \frac{1}{5}ln\left[\prod _{\theta =1}^i{\kappa }_{\theta }\right]$ where $ln$ is the natural logarithm, and ${\kappa }_{\theta }$ is the likelihood ratio associated with the sign or symptom in question.