Constructing Optimal Drug-Testing Plans Using A Bayesian Acceptance Sampling Model
Baker, J. R., Lattimore, P., & Matheson, L. A. (1993). Constructing Optimal Drug-Testing Plans Using A Bayesian Acceptance Sampling Model. Mathematical and Computer Modelling, 17(2), 77-88.
Drug testing has become an accepted strategy for controlling drug use, particularly among individuals in the custody of the criminal justice system. Emphasis has been placed on testing those free in the community, either on pretrial release, probation, or parole. The drug-testing strategies applied to these populations-whom and how often to test-have evolved largely on an ad hoc basis. In this paper, we investigate optimal (cost-minimizing) drug-testing strategies as a means of achieving the efficient allocation of scarce resources to meet agency goals and objectives. We propose an analytic model based on individual decision theory and Bayesian acceptance sampling and apply the model to a hypothetical criminal justice population in which drug use is presumed to be highly prevalent