Optimal Bayesian two-phase designs

Published

Journal Article

In this paper we present a Bayesian decision theoretic approach to the two-phase design problem. The solution of such sequential decision 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 Müller and Parmigiani (1995) 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 optimization of the corresponding fitted surfaces. The method is illustrated for depression in adolescents using data from past studies. © 1998 Elsevier Science B.V.

Duke Authors

Cited Authors

  • Erkanli, A; Soyer, R; Angold, A

Published Date

  • January 5, 1998

Published In

Volume / Issue

  • 66 / 1

Start / End Page

  • 175 - 191

International Standard Serial Number (ISSN)

  • 0378-3758

Citation Source

  • Scopus