Optimal two-stage group-sequential designs

Published

Journal Article

We derive optimal two-stage adaptive group-sequential designs for normally distributed data which achieve the minimum of a mixture of expected sample sizes at the range of plausible values of a normal mean. Unlike standard group-sequential tests, our method is adaptive in that it allows the group size at the second look to be a function of the observed test statistic at the first look. Using optimality criteria, we construct two-stage designs which we show have advantage over other popular adaptive methods. The employed computational method is a modification of the backward induction algorithm applied to a Bayesian decision problem. © 2007 Elsevier B.V. All rights reserved.

Full Text

Duke Authors

Cited Authors

  • Lokhnygina, Y; Tsiatis, AA

Published Date

  • February 1, 2008

Published In

Volume / Issue

  • 138 / 2

Start / End Page

  • 489 - 499

International Standard Serial Number (ISSN)

  • 0378-3758

Digital Object Identifier (DOI)

  • 10.1016/j.jspi.2007.06.011

Citation Source

  • Scopus