  Balancing a rare and serious possibility against a more common and less
serious one is a familiar problem in many situations, such as the prediction of
rare diseases. The relative costs of forecasting errors can be used for any
prediction method that gives an estimated probability of a future event. The
probability at which the likely cost (defined as cost x probability) of a
possible false negative is exactly equal to that of a possible false positive
gives the relevant cutpoint and all subjects with probability of disease
greater than this have a positive test result. All standard methods of logistic
regression will give the log-odds and hence the predicted probability of a
positive outcome for every subject:
