Conditional estimation in two-stage adaptive designs

Per Broberg, Frank Miller

Research output: Contribution to journalArticlepeer-review

Abstract

We consider conditional estimation in two-stage sample size adjustable designs and the consequent bias. More specifically, we consider a design which permits raising the sample size when interim results look rather promising, and which retains the originally planned sample size when results look very promising. The estimation procedures reported comprise the unconditional maximum likelihood, the conditionally unbiased Rao-Blackwell estimator, the conditional median unbiased estimator, and the conditional maximum likelihood with and without bias correction. We compare these estimators based on analytical results and a simulation study. We show how they can be applied in a real clinical trial setting.

Original languageEnglish
Pages (from-to)895-904
JournalBiometrics
Volume73
Issue number3
Early online date2017
DOIs
Publication statusPublished - 2017

Subject classification (UKÄ)

  • Other Medical Sciences not elsewhere specified

Free keywords

  • Adaptive design
  • Conditional estimation
  • Sample size recalculation
  • Two-stage design

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