Optimal Bayesian Two-Phase Designs for Screening Tests
Nov 30 1993
In this paper we present a Bayesian decision theoretic approach by formulating the two-phase design problem as a sequential decision problem. The solutions of such problems is usually difficult to obtain because of their reliance on preposterior analysis. In overcoming this problem, we adopt the Monte Carlo based approach of Muller and Parmigiani (1994) and develop optimal Bayesian designs for two-phase screening tests. A rather attractive feature of the Monte Carlo approach is that it facilitates the preposterior analysis by replacing it with a sequence of scatter plot smoothing/regression techniques and the optimization of the corresponding fitted surfaces. The method is illustrated for depression in adolescents using data from past studies.