Screening as an application of decision theory.

We develop a decision-theoretical approach to setting the threshold for a screening procedure that declares each examined subject as a positive or a negative. It is fundamentally different from maximising the Youden index. The method incorporates the consequences of the two kinds of bad decisions (false positives and false negatives) by means of a set of plausible loss functions elicited from a subject-matter expert or committee. We present details for several classes of loss functions and within-group distributions of the outcomes. We outline extensions related to mixture distributions and compositions of loss functions. We illustrate the method on simulated examples and apply it to real datasets.