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Adaptive seamless clinical trials using early outcomes for treatment or subgroup selection: Methods, simulation model and their implementation in R

Adaptive seamless designs combine confirmatory testing, a domain of phase III trials, with features such as treatment or subgroup selection, typically associated with phase II trials. They promise to increase the efficiency of development programmes of new drugs, for example, in terms of sample size...

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Detalles Bibliográficos
Autores principales: Friede, Tim, Stallard, Nigel, Parsons, Nicholas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8614126/
https://www.ncbi.nlm.nih.gov/pubmed/32118317
http://dx.doi.org/10.1002/bimj.201900020
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author Friede, Tim
Stallard, Nigel
Parsons, Nicholas
author_facet Friede, Tim
Stallard, Nigel
Parsons, Nicholas
author_sort Friede, Tim
collection PubMed
description Adaptive seamless designs combine confirmatory testing, a domain of phase III trials, with features such as treatment or subgroup selection, typically associated with phase II trials. They promise to increase the efficiency of development programmes of new drugs, for example, in terms of sample size and/or development time. It is well acknowledged that adaptive designs are more involved from a logistical perspective and require more upfront planning, often in the form of extensive simulation studies, than conventional approaches. Here, we present a framework for adaptive treatment and subgroup selection using the same notation, which links the somewhat disparate literature on treatment selection on one side and on subgroup selection on the other. Furthermore, we introduce a flexible and efficient simulation model that serves both designs. As primary endpoints often take a long time to observe, interim analyses are frequently informed by early outcomes. Therefore, all methods presented accommodate interim analyses informed by either the primary outcome or an early outcome. The R package asd, previously developed to simulate designs with treatment selection, was extended to include subgroup selection (so‐called adaptive enrichment designs). Here, we describe the functionality of the R package asd and use it to present some worked‐up examples motivated by clinical trials in chronic obstructive pulmonary disease and oncology. The examples both illustrate various features of the R package and provide insights into the operating characteristics of adaptive seamless studies.
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spelling pubmed-86141262021-11-30 Adaptive seamless clinical trials using early outcomes for treatment or subgroup selection: Methods, simulation model and their implementation in R Friede, Tim Stallard, Nigel Parsons, Nicholas Biom J Clinical Trials Adaptive seamless designs combine confirmatory testing, a domain of phase III trials, with features such as treatment or subgroup selection, typically associated with phase II trials. They promise to increase the efficiency of development programmes of new drugs, for example, in terms of sample size and/or development time. It is well acknowledged that adaptive designs are more involved from a logistical perspective and require more upfront planning, often in the form of extensive simulation studies, than conventional approaches. Here, we present a framework for adaptive treatment and subgroup selection using the same notation, which links the somewhat disparate literature on treatment selection on one side and on subgroup selection on the other. Furthermore, we introduce a flexible and efficient simulation model that serves both designs. As primary endpoints often take a long time to observe, interim analyses are frequently informed by early outcomes. Therefore, all methods presented accommodate interim analyses informed by either the primary outcome or an early outcome. The R package asd, previously developed to simulate designs with treatment selection, was extended to include subgroup selection (so‐called adaptive enrichment designs). Here, we describe the functionality of the R package asd and use it to present some worked‐up examples motivated by clinical trials in chronic obstructive pulmonary disease and oncology. The examples both illustrate various features of the R package and provide insights into the operating characteristics of adaptive seamless studies. John Wiley and Sons Inc. 2020-03-02 2020-09 /pmc/articles/PMC8614126/ /pubmed/32118317 http://dx.doi.org/10.1002/bimj.201900020 Text en © 2020 The Authors. Biometrical Journal published by WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Clinical Trials
Friede, Tim
Stallard, Nigel
Parsons, Nicholas
Adaptive seamless clinical trials using early outcomes for treatment or subgroup selection: Methods, simulation model and their implementation in R
title Adaptive seamless clinical trials using early outcomes for treatment or subgroup selection: Methods, simulation model and their implementation in R
title_full Adaptive seamless clinical trials using early outcomes for treatment or subgroup selection: Methods, simulation model and their implementation in R
title_fullStr Adaptive seamless clinical trials using early outcomes for treatment or subgroup selection: Methods, simulation model and their implementation in R
title_full_unstemmed Adaptive seamless clinical trials using early outcomes for treatment or subgroup selection: Methods, simulation model and their implementation in R
title_short Adaptive seamless clinical trials using early outcomes for treatment or subgroup selection: Methods, simulation model and their implementation in R
title_sort adaptive seamless clinical trials using early outcomes for treatment or subgroup selection: methods, simulation model and their implementation in r
topic Clinical Trials
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8614126/
https://www.ncbi.nlm.nih.gov/pubmed/32118317
http://dx.doi.org/10.1002/bimj.201900020
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