Two-stage designs with small sample sizes

J Biopharm Stat. 2023 Jan 2;33(1):53-59. doi: 10.1080/10543406.2022.2080691. Epub 2022 May 25.

Abstract

When applying group-sequential designs in clinical trials with normally distributed outcomes, approximate critical values are often applied. Here, normally distributed test statistics are assumed which, however, are in fact t-distributed. For small sample sizes, the approximation may lead to a serious inflation of the type I error rate. Recently, a method for computing the exact critical boundaries assuring type I error rate control was proposed and the critical boundaries for Pocock- and O'Brien-Fleming-like group-sequential designs were provided. For designs with one interim analysis, we present six alternative designs, which also control the type I error rate and in addition allow flexible design modifications. We compare the characteristics of these 6 two-stage designs. It is shown that considerable sample size savings can be achieved by including futility stopping and by optimizing the designs. Therefore, for clinical trials with small sample sizes as, for example, in the area of rare diseases, optimal two-stage designs with futility stopping may be a valuable alternative to classical group-sequential designs.

Keywords: Adaptive designs; group-sequential designs; optimal designs; rare diseases; sample size.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Humans
  • Medical Futility*
  • Research Design*
  • Sample Size