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Ivanova A, Israel E, LaVange LM, Peters MC, Denlinger LC, Moore WC, Bacharier LB, Marquis MA, Gotman NM, Kosorok MR, Tomlinson C, Mauger DT, Georas SN, Wright RJ, Noel P, Rosner GL, Akuthota P, Billheimer D, Bleecker ER, Cardet JC, Castro M, DiMango EA, Erzurum SC, Fahy JV, Fajt ML, Gaston BM, Holguin F, Jain S, Kenyon NJ, Krishnan JA, Kraft M, Kumar R, Liu MC, Ly NP, Moy JN, Phipatanakul W, Ross K, Smith LJ, Szefler SJ, Teague WG, Wechsler ME, Wenzel SE, White SR. The precision interventions for severe and/or exacerbation-prone asthma (PrecISE) adaptive platform trial: statistical considerations. J Biopharm Stat 2020; 30:1026-1037. [PMID: 32941098 PMCID: PMC7954787 DOI: 10.1080/10543406.2020.1821705] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Accepted: 08/17/2020] [Indexed: 12/24/2022]
Abstract
The Precision Interventions for Severe and/or Exacerbation-prone Asthma (PrecISE) study is an adaptive platform trial designed to investigate novel interventions to severe asthma. The study is conducted under a master protocol and utilizes a crossover design with each participant receiving up to five interventions and at least one placebo. Treatment assignments are based on the patients' biomarker profiles and precision health methods are incorporated into the interim and final analyses. We describe key elements of the PrecISE study including the multistage adaptive enrichment strategy, early stopping of an intervention for futility, power calculations, and the primary analysis strategy.
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Affiliation(s)
| | - Elliot Israel
- Brigham and Women’s Hospital and Harvard Medical School, Boston, MA
| | | | | | | | | | | | | | | | | | | | | | | | | | - Patricia Noel
- Division of Lung Diseases, National Heart, Lung and Blood Institute (NHLBI), National Institutes of Health, Bethesda, MD
| | | | - Praveen Akuthota
- Asthma and Airway Disease Research Center, University of Arizona, Tucson
| | - Dean Billheimer
- Asthma and Airway Disease Research Center, University of Arizona, Tucson
| | | | | | | | | | | | | | - Merritt L. Fajt
- Wells Center for Pediatric Research, Indiana University, Indianapolis
| | | | | | | | | | - Jerry A. Krishnan
- Asthma and Airway Disease Research Center, University of Arizona, Tucson
| | | | | | | | - Ngoc P. Ly
- Rush University Medical Center, Chicago, IL
| | - James N. Moy
- Boston Children’s Hospital and Harvard Medical School, Boston, MA
| | - Wanda Phipatanakul
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD
| | - Kristie Ross
- UH Rainbow Babies and Children’s Hospitals, Cleveland, OH
| | | | - Stanley J. Szefler
- Children’s Hospital Colorado and University of Colorado School of Medicine, Aurora, CO
| | | | | | - Sally E. Wenzel
- National Jewish Health, Denver, CO, and University of Colorado School of Medicine, Aurora, CO
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Xu Z, Proschan M, Lee S. Validity and power considerations on hypothesis testing under minimization. Stat Med 2016; 35:2315-27. [PMID: 26787557 DOI: 10.1002/sim.6874] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2015] [Revised: 10/30/2015] [Accepted: 12/23/2015] [Indexed: 12/26/2022]
Abstract
Minimization, a dynamic allocation method, is gaining popularity especially in cancer clinical trials. Aiming to achieve balance on all important prognostic factors simultaneously, this procedure can lead to a substantial reduction in covariate imbalance compared with conventional randomization in small clinical trials. While minimization has generated enthusiasm, some controversy exists over the proper analysis of such a trial. Critics argue that standard testing methods that do not account for the dynamic allocation algorithm can lead to invalid statistical inference. Acknowledging this limitation, the International Conference on Harmonization E9 guideline suggests that 'the complexity of the logistics and potential impact on analyses be carefully evaluated when considering dynamic allocation'. In this article, we investigate the proper analysis approaches to inference in a minimization design for both continuous and time-to-event endpoints and evaluate the validity and power of these approaches under a variety of scenarios both theoretically and empirically. Published 2016. This article is a U.S. Government work and is in the public domain in the USA.
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Affiliation(s)
- Zhenzhen Xu
- CBER, Food and Drug Administration, Silver Spring, MD 20993, U.S.A
| | - Michael Proschan
- Biostatistics Research Branch, National Institute of Allergy and Infectious Diseases, Bethesda, MD 20892-7609, U.S.A
| | - Shiowjen Lee
- CBER, Food and Drug Administration, Silver Spring, MD 20993, U.S.A
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Abstract
OBJECTIVE To review and describe randomization techniques used in clinical trials, including simple, block, stratified, and covariate adaptive techniques. BACKGROUND Clinical trials are required to establish treatment efficacy of many athletic training procedures. In the past, we have relied on evidence of questionable scientific merit to aid the determination of treatment choices. Interest in evidence-based practice is growing rapidly within the athletic training profession, placing greater emphasis on the importance of well-conducted clinical trials. One critical component of clinical trials that strengthens results is random assignment of participants to control and treatment groups. Although randomization appears to be a simple concept, issues of balancing sample sizes and controlling the influence of covariates a priori are important. Various techniques have been developed to account for these issues, including block, stratified randomization, and covariate adaptive techniques. ADVANTAGES Athletic training researchers and scholarly clinicians can use the information presented in this article to better conduct and interpret the results of clinical trials. Implementing these techniques will increase the power and validity of findings of athletic medicine clinical trials, which will ultimately improve the quality of care provided.
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Affiliation(s)
- Minsoo Kang
- Middle Tennessee State University, Murfreesboro, TN, USA
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