Cargando…

Conditionally unbiased and near unbiased estimation of the selected treatment mean for multistage drop-the-losers trials

The two-stage drop-the-loser design provides a framework for selecting the most promising of K experimental treatments in stage one, in order to test it against a control in a confirmatory analysis at stage two. The multistage drop-the-losers design is both a natural extension of the original two-st...

Descripción completa

Detalles Bibliográficos
Autores principales: Bowden, Jack, Glimm, Ekkehard
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BlackWell Publishing Ltd 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4034592/
https://www.ncbi.nlm.nih.gov/pubmed/24353149
http://dx.doi.org/10.1002/bimj.201200245
_version_ 1782317985758707712
author Bowden, Jack
Glimm, Ekkehard
author_facet Bowden, Jack
Glimm, Ekkehard
author_sort Bowden, Jack
collection PubMed
description The two-stage drop-the-loser design provides a framework for selecting the most promising of K experimental treatments in stage one, in order to test it against a control in a confirmatory analysis at stage two. The multistage drop-the-losers design is both a natural extension of the original two-stage design, and a special case of the more general framework of Stallard & Friede (2008) (Stat. Med. 27, 6209–6227). It may be a useful strategy if deselecting all but the best performing treatment after one interim analysis is thought to pose an unacceptable risk of dropping the truly best treatment. However, estimation has yet to be considered for this design. Building on the work of Cohen & Sackrowitz (1989) (Stat. Prob. Lett. 8, 273–278), we derive unbiased and near-unbiased estimates in the multistage setting. Complications caused by the multistage selection process are shown to hinder a simple identification of the multistage uniform minimum variance conditionally unbiased estimate (UMVCUE); two separate but related estimators are therefore proposed, each containing some of the UMVCUEs theoretical characteristics. For a specific example of a three-stage drop-the-losers trial, we compare their performance against several alternative estimators in terms of bias, mean squared error, confidence interval width and coverage.
format Online
Article
Text
id pubmed-4034592
institution National Center for Biotechnology Information
language English
publishDate 2014
publisher BlackWell Publishing Ltd
record_format MEDLINE/PubMed
spelling pubmed-40345922014-06-02 Conditionally unbiased and near unbiased estimation of the selected treatment mean for multistage drop-the-losers trials Bowden, Jack Glimm, Ekkehard Biom J Knowledge Extraction The two-stage drop-the-loser design provides a framework for selecting the most promising of K experimental treatments in stage one, in order to test it against a control in a confirmatory analysis at stage two. The multistage drop-the-losers design is both a natural extension of the original two-stage design, and a special case of the more general framework of Stallard & Friede (2008) (Stat. Med. 27, 6209–6227). It may be a useful strategy if deselecting all but the best performing treatment after one interim analysis is thought to pose an unacceptable risk of dropping the truly best treatment. However, estimation has yet to be considered for this design. Building on the work of Cohen & Sackrowitz (1989) (Stat. Prob. Lett. 8, 273–278), we derive unbiased and near-unbiased estimates in the multistage setting. Complications caused by the multistage selection process are shown to hinder a simple identification of the multistage uniform minimum variance conditionally unbiased estimate (UMVCUE); two separate but related estimators are therefore proposed, each containing some of the UMVCUEs theoretical characteristics. For a specific example of a three-stage drop-the-losers trial, we compare their performance against several alternative estimators in terms of bias, mean squared error, confidence interval width and coverage. BlackWell Publishing Ltd 2014-03 2013-12-18 /pmc/articles/PMC4034592/ /pubmed/24353149 http://dx.doi.org/10.1002/bimj.201200245 Text en © 2014 The Author. Biometrical Journal published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim http://creativecommons.org/licenses/by/3.0/ This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Knowledge Extraction
Bowden, Jack
Glimm, Ekkehard
Conditionally unbiased and near unbiased estimation of the selected treatment mean for multistage drop-the-losers trials
title Conditionally unbiased and near unbiased estimation of the selected treatment mean for multistage drop-the-losers trials
title_full Conditionally unbiased and near unbiased estimation of the selected treatment mean for multistage drop-the-losers trials
title_fullStr Conditionally unbiased and near unbiased estimation of the selected treatment mean for multistage drop-the-losers trials
title_full_unstemmed Conditionally unbiased and near unbiased estimation of the selected treatment mean for multistage drop-the-losers trials
title_short Conditionally unbiased and near unbiased estimation of the selected treatment mean for multistage drop-the-losers trials
title_sort conditionally unbiased and near unbiased estimation of the selected treatment mean for multistage drop-the-losers trials
topic Knowledge Extraction
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4034592/
https://www.ncbi.nlm.nih.gov/pubmed/24353149
http://dx.doi.org/10.1002/bimj.201200245
work_keys_str_mv AT bowdenjack conditionallyunbiasedandnearunbiasedestimationoftheselectedtreatmentmeanformultistagedroptheloserstrials
AT glimmekkehard conditionallyunbiasedandnearunbiasedestimationoftheselectedtreatmentmeanformultistagedroptheloserstrials