Alan Hartford1, Mitchell Thomann2, Xiaotian Chen3, Eva Miller4, Alun Bedding5, Silke Jorgens6, Lingyun Liu7, Li Chen8, Caroline Morgan9. 1. Data and Statistical Sciences, AbbVie, Inc, 1 N Waukegan Road, North Chicago, IL, 60064-1802, USA. alan.hartford@abbvie.com. 2. Global Statistical Sciences, Eli Lilly and Company, Indianapolis, IN, USA. 3. Data and Statistical Sciences, AbbVie, Inc, 1 N Waukegan Road, North Chicago, IL, 60064-1802, USA. 4. Independent Biostatistical Consultant, Levittown, PA, USA. 5. Biostatistics, Roche, Welwyn Garden City, United Kingdom. 6. Innovation Center, ICON plc, Cologne, Germany. 7. Consulting Department, Cytel, Cambridge, MA, USA. 8. Center for Design and Analysis, Amgen Inc, Thousand Oaks, CA, USA. 9. Strategic Accounts, Cytel, Cambridge, MA, USA.
Abstract
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also remains a concern, which could be alleviated by additional interaction with agencies and an update of the FDA draft guidance to industry on adaptive designs.
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.
Keywords:
clinical study design; early stopping; interim analysis; sample size re-estimation; seamless design
Authors: Munyaradzi Dimairo; Philip Pallmann; James Wason; Susan Todd; Thomas Jaki; Steven A Julious; Adrian P Mander; Christopher J Weir; Franz Koenig; Marc K Walton; Jon P Nicholl; Elizabeth Coates; Katie Biggs; Toshimitsu Hamasaki; Michael A Proschan; John A Scott; Yuki Ando; Daniel Hind; Douglas G Altman Journal: BMJ Date: 2020-06-17
Authors: Xiaoyun Li; Chengxing Lu; Kristine Broglio; Paul Bycott; Jie Chen; Qi Jiang; Jianchang Lin; Jingjing Ye; Jun Yin Journal: Ann Transl Med Date: 2022-09
Authors: Munyaradzi Dimairo; Philip Pallmann; James Wason; Susan Todd; Thomas Jaki; Steven A Julious; Adrian P Mander; Christopher J Weir; Franz Koenig; Marc K Walton; Jon P Nicholl; Elizabeth Coates; Katie Biggs; Toshimitsu Hamasaki; Michael A Proschan; John A Scott; Yuki Ando; Daniel Hind; Douglas G Altman Journal: Trials Date: 2020-06-17 Impact factor: 2.279