  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, $\phi$, can be approximated by:
  $\phi \approx
\frac{1}{5}{ln\left[\displaystyle\prod_{\theta=1}^{i}\kappa_\theta\right]}$
  where $ln$ is the natural logarithm, and $\kappa_\theta$ is the likelihood
ratio associated with the sign or symptom in question.
