EcoNomics of Adaptive Clinical Trials (ENACT)

Value-based Adaptive Clinical Trial Designs for Efficient Delivery of NIHR Research.

On

Overview

Improving the efficiency of clinical trials is a priority for both policy makers and health service researchers. For example, NHS England (2017) highlights the aim of ‘baking in’ assessment of the value and real world cost of research as a default part of future clinical trial designs. Adaptive clinical trials are seen as one way to improve trial efficiency, yet recent research by Flight et al. (2019) found that cost-effectiveness criteria are not currently incorporated into their design.  

Previous research by collaborators in the ENACT project (Pertile et al. (2014), Chick et al. (2017) and Alban et al. (2020))  has proposed various Bayesian models for the design of a sequential clinical trial which accounts for the cost-effectiveness of research. The models provide a rule for stopping a two-armed clinical trial at interim analyses, based on balancing the costs and benefits of continuing with those of stopping. The models also offer the potential for choosing whether it is cost-effective to run a fixed sample size trial, a sequential trial or no trial at all at the trial’s commissioning stage. The first full, retrospective, application of one of these models has been to the NIHR-funded ProFHER pragmatic trial (Handoll et al., 2015) and is currently available as a working paper (Forster et al., 2019). 

Failing to consider value-based, adaptive, approaches to the design and delivery of NIHR-funded research potentially wastes limited resources, making less funding available for patient care and commissioning research elsewhere in the NHS. But how can these approaches be applied in practice, and what are the major challenges facing their implementation? This project provides a vital first step towards answering these questions. It does so by seeking to increase the number of experts trained in the methodology, improving existing understanding of the methodology’s strengths and weaknesses and providing practical resources that are required to support implementation. 

For more details or queries please contact Dr Laura Flight (l.flight@sheffield.ac.uk)


Research objectives

  1. Assessment of the feasibility of the value-based sequential model for the delivery of NIHR-funded research. 

  2. Knowledge integration for NIHR-funded CTU staff in the use of adaptive clinical trials and making them ‘value-based’.

  3. Development of a set of practical resources that can be used across NIHR CTUs to improve understanding and pathways to the implementation of value-based adaptive designs.  


Funder

The ENACT project is funded by NIHR CTU Support Funding (2019 call) to support efficient/innovative delivery of NIHR research

For more details or queries please contact Laura Flight (l.flight@sheffield.ac.uk)


Software

Matlab code for the implementation of Sequential Experimentation with Delay

Chick, S. E., Forster, M, Pertile P. (2017). htadelay


Outputs 

NIHR Clinical Research Network Virtual Symposium: Delivering Complex and Innovative Design (CID) Studies (February 2021). 

Report for the EcoNomics of Adaptive Clinical Trials (ENACT) project : Application of a Bayesian Value-Based Sequential Model of a Clinical Trial to the CACTUS and HERO Case Studies (with Guidance Material for Clinical Trials Units) 


Collaborators

School of Health and Related Research (ScHARR), University of Sheffield

Dr Laura Flight (Project Management Group)

Professor Alan Brennan (Project Management Group)

Professor Cindy Cooper

Professor Steven Julious

Department of Economics and Related Studies, University of York

Dr Martin Forster (Project Management Group)

Technology and Operations Area, INSEAD Europe Campus, Fontainebleau, France

Professor Stephen E Chick (Project Management Group)

York Trials Unit, Department of Health Sciences, University of York

Dr Stephen Brealey

Belen Corbacho

Ada Keding

Sarah Ronaldson

Dr Puvanendran Tharmanathan

Charlie Welch

Department of Economics, University of Verona, Italy

Professor Paolo Pertile


References

  1. Alban A, Chick S, Forster M. Value-based clinical trials: selecting trial lengths and recruitment rates in different regulatory contexts. Discuss Pap 20/01, Dep Econ Univ York Published Online First: 2020.https://ideas.repec.org/p/yor/yorken/20-01.html
  2. Chick S, Forster M, Pertile P (2017). A Bayesian decision theoretic model of sequential experimentation with delayed response. Journal of the Royal Statistical Society: Series B (Statistical Methodology). 2017;79(5):1439-62.
  3. Flight L, Arshad F, Barnsley R, et al. (2019). A review of clinical trials with an adaptive design and health economic analysis. Value in Health. 2019;22:391-398.
  4. Flight, L., Julious, S., Brennan, A. et al. How can health economics be used in the design and analysis of adaptive clinical trials? A qualitative analysis. Trials 21, 252 (2020). 
  5. Forster M, Brealey SD, Chick S, et al. (2019). Cost-Effective Clinical Trial Design: Application of a Bayesian Sequential Stopping Rule to the ProFHER Pragmatic Trial. Department of Economics and Related Studies discussion paper 19/01, University of York.
  6. Handoll H, Brealey S, Rangan A, et al. (2015). The ProFHER (PROximal Fracture of the Humerus: Evaluation by Randomisation) trial - a pragmatic multicentre randomised controlled trial evaluating the clinical effectiveness and cost-effectiveness of surgical compared with non-surgical treatment for proximal fracture of the humerus in adults. Health Technology Assessment. 2015;19:1–280.
  7. NHS England (2017). ‘12 Actions to support and apply research in the NHS’. Available online at https://www.england.nhs.uk/publication/12-actions-to-support-and-apply-research-in-the-nhs/. Last Accessed 02 May 2019.
  8. Pallmann P, Bedding AW, Choodari-Oskooei B, et al. (2018). Adaptive designs in clinical trials: why use them, and how to run and report them. BMC Medicine 2018;16:29.
  9. Pertile P, Forster M and La Torre D (2014). Optimal Bayesian sequential sampling rules for the economic evaluation of health technologies. With online supplementary material. Journal of the Royal Statistical Society, Series A, 2014;177(2):419–438.

Flagship institutes

The University’s four flagship institutes bring together our key strengths to tackle global issues, turning interdisciplinary and translational research into real-world solutions.