Adaptive Designs: Results of 2016 Survey on Perception and Use.

clinical study design early stopping interim analysis sample size re-estimation seamless design

Journal

Therapeutic innovation & regulatory science
ISSN: 2168-4804
Titre abrégé: Ther Innov Regul Sci
Pays: Switzerland
ID NLM: 101597411

Informations de publication

Date de publication:
01 2020
Historique:
received: 15 07 2018
accepted: 10 09 2018
entrez: 3 2 2020
pubmed: 3 2 2020
medline: 19 5 2021
Statut: ppublish

Résumé

The DIA Adaptive Designs Scientific Working Group has a devoted subteam to performing surveys, literature reviews, and registry reviews every 4 years to assess the perception and use of adaptive designs (ADs) in the development of drugs and biologics. A survey was distributed to pharmaceutical companies, academic institutions, and contract research organizations to collect information about the usage of ADs of different types and perception of challenges for their use. Literature and registry reviews were conducted to assess the prevalence of ADs of different types in drug and biologics development. These results were compared to previous surveys and reviews using summary statistics. ADs appear to be more widely considered in the last 4 years as compared to earlier 4-year periods. The most common types of ADs remain early stopping, treatment group adaptations, and sample size re-estimation. Both stopping early for safety and changing the endpoint of the analyses were rarely mentioned in literature prior to 2012 but are now appearing more frequently. The barriers of change management and negative experiences by some institutions with ADs remain a source of concern. Additional, consistent training would be helpful to choose the right adaptation(s) needed for specific clinical trials and for planning appropriately for operational efficiency such as for drug supply management and data management. The perceived barrier of regulatory acceptance also remains a concern, which could be alleviated by additional interaction with agencies and an update of the FDA draft guidance to industry on adaptive designs.

Sections du résumé

BACKGROUND
The DIA Adaptive Designs Scientific Working Group has a devoted subteam to performing surveys, literature reviews, and registry reviews every 4 years to assess the perception and use of adaptive designs (ADs) in the development of drugs and biologics.
METHODS
A survey was distributed to pharmaceutical companies, academic institutions, and contract research organizations to collect information about the usage of ADs of different types and perception of challenges for their use. Literature and registry reviews were conducted to assess the prevalence of ADs of different types in drug and biologics development. These results were compared to previous surveys and reviews using summary statistics.
RESULTS
ADs appear to be more widely considered in the last 4 years as compared to earlier 4-year periods.
CONCLUSIONS
The most common types of ADs remain early stopping, treatment group adaptations, and sample size re-estimation. Both stopping early for safety and changing the endpoint of the analyses were rarely mentioned in literature prior to 2012 but are now appearing more frequently. The barriers of change management and negative experiences by some institutions with ADs remain a source of concern. Additional, consistent training would be helpful to choose the right adaptation(s) needed for specific clinical trials and for planning appropriately for operational efficiency such as for drug supply management and data management. The perceived barrier of regulatory acceptance also remains a concern, which could be alleviated by additional interaction with agencies and an update of the FDA draft guidance to industry on adaptive designs.

Identifiants

pubmed: 32008237
doi: 10.1007/s43441-019-00028-y
pii: 10.1007/s43441-019-00028-y
doi:

Substances chimiques

Biological Products 0

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

42-54

Auteurs

Alan Hartford (A)

Data and Statistical Sciences, AbbVie, Inc, 1 N Waukegan Road, North Chicago, IL, 60064-1802, USA. alan.hartford@abbvie.com.

Mitchell Thomann (M)

Global Statistical Sciences, Eli Lilly and Company, Indianapolis, IN, USA.

Xiaotian Chen (X)

Data and Statistical Sciences, AbbVie, Inc, 1 N Waukegan Road, North Chicago, IL, 60064-1802, USA.

Eva Miller (E)

Independent Biostatistical Consultant, Levittown, PA, USA.

Alun Bedding (A)

Biostatistics, Roche, Welwyn Garden City, United Kingdom.

Silke Jorgens (S)

Innovation Center, ICON plc, Cologne, Germany.

Lingyun Liu (L)

Consulting Department, Cytel, Cambridge, MA, USA.

Li Chen (L)

Center for Design and Analysis, Amgen Inc, Thousand Oaks, CA, USA.

Caroline Morgan (C)

Strategic Accounts, Cytel, Cambridge, MA, USA.

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Classifications MeSH