Login / Signup

Conditionally unbiased estimation in the normal setting with unknown variances.

David S RobertsonEkkehard Glimm
Published in: Communications in statistics: theory and methods (2018)
To efficiently and completely correct for selection bias in adaptive two-stage trials, uniformly minimum variance conditionally unbiased estimators (UMVCUEs) have been derived for trial designs with normally distributed data. However, a common assumption is that the variances are known exactly, which is unlikely to be the case in practice. We extend the work of Cohen and Sackrowitz (Statistics & Probability Letters, 8(3):273-278, 1989), who proposed an UMVCUE for the best performing candidate in the normal setting with a common unknown variance. Our extension allows for multiple selected candidates, as well as unequal stage one and two sample sizes.
Keyphrases
  • healthcare
  • primary care
  • study protocol
  • clinical trial
  • phase iii
  • electronic health record
  • phase ii
  • big data
  • quality improvement
  • machine learning
  • neural network