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Published in: Trials 1/2015

Open Access 01-12-2015 | Research

The Stroke Hyperglycemia Insulin Network Effort (SHINE) trial: an adaptive trial design case study

Authors: Jason T Connor, Kristine R Broglio, Valerie Durkalski, William J Meurer, Karen C Johnston

Published in: Trials | Issue 1/2015

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Abstract

Background

The ‘Adaptive Designs Accelerating Promising Trials into Treatments (ADAPT-IT)’ project is a collaborative effort supported by the National Institutes of Health (NIH) and United States Food & Drug Administration (FDA) to explore how adaptive clinical trial design might improve the evaluation of drugs and medical devices. ADAPT-IT uses the National Institute of Neurologic Disorders & Stroke-supported Neurological Emergencies Treatment Trials (NETT) network as a ‘laboratory’ in which to study the development of adaptive clinical trial designs in the confirmatory setting. The Stroke Hyperglycemia Insulin Network Effort (SHINE) trial was selected for funding by the NIH-NINDS at the start of ADAPT-IT and is currently an ongoing phase III trial of tight glucose control in hyperglycemic acute ischemic stroke patients. Within ADAPT-IT, a Bayesian adaptive Goldilocks trial design alternative was developed.

Methods

The SHINE design includes response adaptive randomization, a sample size re-estimation, and monitoring for early efficacy and futility according to a group sequential design. The Goldilocks design includes more frequent monitoring for predicted success or futility and a longitudinal model of the primary endpoint. Both trial designs were simulated and compared in terms of their mean sample size and power across a range of treatment effects and success rates for the control group.

Results

As simulated, the SHINE design tends to have slightly higher power and the Goldilocks design has a lower mean sample size. Both designs were tuned to have approximately 80% power to detect a difference of 25% versus 32% between control and treatment, respectively. In this scenario, mean sample sizes are 1,114 and 979 for the SHINE and Goldilocks designs, respectively.

Conclusions

Two designs were brought forward, and both were evaluated, revised, and improved based on the input of all parties involved in the ADAPT-IT process. However, the SHINE investigators were tasked with choosing only a single design to implement and ultimately elected not to implement the Goldilocks design. The Goldilocks design will be retrospectively executed upon completion of SHINE to later compare the designs based on their use of patient resources, time, and conclusions in a real world setting.

Trial registration

ClinicalTrials.gov NCT01369069 June 2011.
Appendix
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Literature
2.
go back to reference Meurer WJ, Lewis RJ, Tagle D, Fetters MD, Legocki L, Berry S, et al. An overview of the adaptive designs accelerating promising trials into treatments (ADAPT-IT) project. Ann Emerg Med. 2012;60:451–7.CrossRefPubMedPubMedCentral Meurer WJ, Lewis RJ, Tagle D, Fetters MD, Legocki L, Berry S, et al. An overview of the adaptive designs accelerating promising trials into treatments (ADAPT-IT) project. Ann Emerg Med. 2012;60:451–7.CrossRefPubMedPubMedCentral
4.
go back to reference Broglio KR, Connor JT, Berry SM. Not too big, not too small: a goldilocks approach to sample size selection. J Biopharm Stat. 2014;24:685–705.CrossRefPubMed Broglio KR, Connor JT, Berry SM. Not too big, not too small: a goldilocks approach to sample size selection. J Biopharm Stat. 2014;24:685–705.CrossRefPubMed
5.
go back to reference Sutton L, Julious SA, Goodacre SW. Influence of adaptive analysis on unnecessary patient recruitment: reanalysis of the RATPAC trial. Ann Emerg Med. 2012;60:442–8. e1.CrossRefPubMed Sutton L, Julious SA, Goodacre SW. Influence of adaptive analysis on unnecessary patient recruitment: reanalysis of the RATPAC trial. Ann Emerg Med. 2012;60:442–8. e1.CrossRefPubMed
6.
go back to reference Connor JT, Luce BR, Broglio KR, Ishak KJ, Mullins CD, Vanness DJ, et al. Do Bayesian adaptive trials offer advantages for comparative effectiveness research? Protocol for the RE-ADAPT study. Clin Trials. 2013;10:807–27.CrossRefPubMedPubMedCentral Connor JT, Luce BR, Broglio KR, Ishak KJ, Mullins CD, Vanness DJ, et al. Do Bayesian adaptive trials offer advantages for comparative effectiveness research? Protocol for the RE-ADAPT study. Clin Trials. 2013;10:807–27.CrossRefPubMedPubMedCentral
7.
go back to reference Bruno A, Durkalski VL, Hall CE, Juneja R, Barsan WG, Janis S, et al. The Stroke Hyperglycemia Insulin Network Effort (SHINE) trial protocol: a randomized, blinded, efficacy trial of standard vs. intensive hyperglycemia management in acute stroke. Int J Stroke. 2014;9:246–51.CrossRefPubMed Bruno A, Durkalski VL, Hall CE, Juneja R, Barsan WG, Janis S, et al. The Stroke Hyperglycemia Insulin Network Effort (SHINE) trial protocol: a randomized, blinded, efficacy trial of standard vs. intensive hyperglycemia management in acute stroke. Int J Stroke. 2014;9:246–51.CrossRefPubMed
8.
go back to reference Gould AL, Shih WJ. Modifying the design of ongoing trials without unblinding. Stat Med. 1998;17:89–100.CrossRefPubMed Gould AL, Shih WJ. Modifying the design of ongoing trials without unblinding. Stat Med. 1998;17:89–100.CrossRefPubMed
9.
go back to reference Saville BR, Connor JT, Ayers GD, Alvarez J. The utility of Bayesian predictive probabilities for interim monitoring of clinical trials. Clin Trials. 2014;11:485–93.CrossRefPubMedPubMedCentral Saville BR, Connor JT, Ayers GD, Alvarez J. The utility of Bayesian predictive probabilities for interim monitoring of clinical trials. Clin Trials. 2014;11:485–93.CrossRefPubMedPubMedCentral
10.
go back to reference R Core Development Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing Vienna Austria 2014. http:www.R-project.org. R Core Development Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing Vienna Austria 2014. http:www.​R-project.​org.
11.
go back to reference The National Institute of Neurological Disorders and Stroke rt-PA Stroke Study Group. Tissue plasminogen activator for acute ischemic stroke. N Engl J Med. 1995;333:1581–7.CrossRef The National Institute of Neurological Disorders and Stroke rt-PA Stroke Study Group. Tissue plasminogen activator for acute ischemic stroke. N Engl J Med. 1995;333:1581–7.CrossRef
12.
go back to reference Hampson LV, Jennison C. Group sequential tests for delayed responses (with discussion): Group Sequential Tests. J Royal Stat Soc Ser B. 2013;75:3–54.CrossRef Hampson LV, Jennison C. Group sequential tests for delayed responses (with discussion): Group Sequential Tests. J Royal Stat Soc Ser B. 2013;75:3–54.CrossRef
14.
go back to reference Patient-Centered Outcomes Research Institute. Draft Methodology Report: “Our Questions, Our Decisions: Standards for Patient-centered Outcomes Research”. Draft. Patient-Centered Outcomes Research Institute. 2012. p. 206. Patient-Centered Outcomes Research Institute. Draft Methodology Report: “Our Questions, Our Decisions: Standards for Patient-centered Outcomes Research”. Draft. Patient-Centered Outcomes Research Institute. 2012. p. 206.
Metadata
Title
The Stroke Hyperglycemia Insulin Network Effort (SHINE) trial: an adaptive trial design case study
Authors
Jason T Connor
Kristine R Broglio
Valerie Durkalski
William J Meurer
Karen C Johnston
Publication date
01-12-2015
Publisher
BioMed Central
Published in
Trials / Issue 1/2015
Electronic ISSN: 1745-6215
DOI
https://doi.org/10.1186/s13063-015-0574-8

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