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Van Norman GA. Phase II Trials in Drug Development and Adaptive Trial Design. JACC Basic Transl Sci 2019; 4:428-437. [PMID: 31312766 PMCID: PMC6609997 DOI: 10.1016/j.jacbts.2019.02.005] [Citation(s) in RCA: 56] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/11/2019] [Accepted: 02/14/2019] [Indexed: 12/20/2022]
Abstract
Phase II clinical studies represent a critical point in determining drug costs, and phase II is a poor predictor of drug success: >30% of drugs entering phase II studies fail to progress, and >58% of drugs go on to fail in phase III. Adaptive clinical trial design has been proposed as a way to reduce the costs of phase II testing by providing earlier determination of futility and prediction of phase III success, reducing overall phase II and III trial sizes, and shortening overall drug development time. This review examines issues in phase II testing and adaptive trial design.
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Affiliation(s)
- Gail A. Van Norman
- Department of Anesthesiology and Pain Medicine, University of Washington, Seattle, Washington
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Cerqueira FP, Jesus AMC, Cotrim MD. Adaptive Design: A Review of the Technical, Statistical, and Regulatory Aspects of Implementation in a Clinical Trial. Ther Innov Regul Sci 2019. [DOI: 10.1177/2168479019831240] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Affiliation(s)
- Franck Pires Cerqueira
- Polytechnic Institute of Porto School of Health, Department of Pharmacy Porto, Portugal
- Pharmacy Faculty of the University of Coimbra, Department of Pharmacology Coimbra, Portugal
| | | | - Maria Dulce Cotrim
- Pharmacy Faculty of the University of Coimbra, Department of Pharmacology Coimbra, Portugal
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Kelly LE, Dyson MP, Butcher NJ, Balshaw R, London AJ, Neilson CJ, Junker A, Mahmud SM, Driedger SM, Wang X. Considerations for adaptive design in pediatric clinical trials: study protocol for a systematic review, mixed-methods study, and integrated knowledge translation plan. Trials 2018; 19:572. [PMID: 30340624 PMCID: PMC6194696 DOI: 10.1186/s13063-018-2934-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2018] [Accepted: 09/24/2018] [Indexed: 11/25/2022] Open
Abstract
Background Although children have historically been excluded from clinical trials (CTs), many require medicines tested and approved in CTs, forcing health care providers to treat their pediatric patients based on extrapolated data. Unfortunately, traditional randomized CTs can be slow and resource-intensive, and they often require multi-center collaboration. However, an adaptive design (AD) framework for CTs could be used to increase the efficiency of pediatric CTs by incorporating prospectively planned modifications to CT methods without undermining the integrity or validity of the study. There are many possible adaptations, but each will have ethical, logistical, and statistical implications. It remains unclear which adaptations (or combinations thereof) will lead to real-world improvements in pediatric CT efficiency. This study will identify, evaluate, and synthesize the various regulatory, ethical, logistical, and statistical considerations and emerging issues of AD in CTs that could be used to evaluate the use of drugs in children. Methods/design Following the development of a peer-reviewed search strategy, a systematic review on AD in CTs will be conducted. Data on regulatory, ethical, logistic, and statistical considerations as well as population and trial design characteristics will be synthesized. A mixed-methods study including surveys and focus groups with regulators, research ethics board members, biostatisticians, clinicians, and scientists, as well as representatives from patient groups and the public will evaluate the opportunities and challenges in applying AD in trials enrolling children and propose recommendations on best practices. Discussion This study will deliver practical recommendations on the use of AD in pediatric CTs. Collaboration and consultation with national and global partners will ensure that our results meet the needs of researchers, regulators, and patients, both locally and globally, and that they remain current and relevant by engaging a wide variety of stakeholders. Overall, this research will enrich the knowledge base regarding if, how, and when AD can be used to answer research questions with fewer resources while still meeting the highest ethical standards and regulatory requirements for CTs. In turn, this will result in increased high-quality clinical research needed by health care providers so they have access to appropriate, population-specific evidence regarding the safe and effective use of medicines in children.
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Affiliation(s)
- Lauren E Kelly
- Department of Pediatrics and Child Health, The University of Manitoba, 405 Chown, 753 McDermot Ave., Winnipeg, MB, R3E0T6, Canada. .,Clinical Trials Platform, George & Fay Yee Centre for Healthcare Innovation, Winnipeg, MB, Canada. .,Department of Community Health Sciences, University of Manitoba, Winnipeg, MB, Canada.
| | - Michele P Dyson
- Department of Pediatrics, University of Alberta, Edmonton, AB, Canada
| | - Nancy J Butcher
- Child Health Evaluative Sciences, The Hospital for Sick Children Research Institute, Toronto, ON, Canada
| | - Robert Balshaw
- Department of Community Health Sciences, University of Manitoba, Winnipeg, MB, Canada.,Data Science Platform, George & Fay Yee Centre for Healthcare Innovation, Winnipeg, MB, Canada
| | - Alex John London
- Center for Ethics and Policy, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Christine J Neilson
- Neil John Maclean Health Sciences Library, University of Manitoba, Winnipeg, MB, Canada
| | - Anne Junker
- Department of Allergy and Immunology, British Columbia Children's Hospital, Vancouver, BC, Canada
| | - Salaheddin M Mahmud
- Clinical Trials Platform, George & Fay Yee Centre for Healthcare Innovation, Winnipeg, MB, Canada.,Department of Community Health Sciences, University of Manitoba, Winnipeg, MB, Canada.,Vaccine and Drug Evaluation Centre, University of Manitoba, Winnipeg, MB, Canada
| | - S Michelle Driedger
- Department of Community Health Sciences, University of Manitoba, Winnipeg, MB, Canada
| | - Xikui Wang
- Department of Statistics, University of Manitoba, Winnipeg, MB, Canada
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Abstract
OBJECTIVES This review investigates characteristics of implemented adaptive design clinical trials and provides examples of regulatory experience with such trials. DESIGN Review of adaptive design clinical trials in EMBASE, PubMed, Cochrane Registry of Controlled Clinical Trials, Web of Science and ClinicalTrials.gov. Phase I and seamless Phase I/II trials were excluded. Variables extracted from trials included basic study characteristics, adaptive design features, size and use of independent data monitoring committees (DMCs) and blinded interim analyses. We also examined use of the adaptive trials in new drug submissions to the Food and Drug Administration (FDA) and European Medicines Agency (EMA) and recorded regulators' experiences with adaptive designs. RESULTS 142 studies met inclusion criteria. There has been a recent growth in publicly reported use of adaptive designs among researchers around the world. The most frequently appearing types of adaptations were seamless Phase II/III (57%), group sequential (21%), biomarker adaptive (20%), and adaptive dose-finding designs (16%). About one-third (32%) of trials reported an independent DMC, while 6% reported blinded interim analysis. We found that 9% of adaptive trials were used for FDA product approval consideration, and 12% were used for EMA product approval consideration. International regulators had mixed experiences with adaptive trials. Many product applications with adaptive trials had extensive correspondence between drug sponsors and regulators regarding the adaptive designs, in some cases with regulators requiring revisions or alterations to research designs. CONCLUSIONS Wider use of adaptive designs will necessitate new drug application sponsors to engage with regulatory scientists during planning and conduct of the trials. Investigators need to more consistently report protections intended to preserve confidentiality and minimise potential operational bias during interim analysis.
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Affiliation(s)
- Laura E Bothwell
- Program on Regulation, Therapeutics, and Law (PORTAL), Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Jerry Avorn
- Program on Regulation, Therapeutics, and Law (PORTAL), Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Nazleen F Khan
- Program on Regulation, Therapeutics, and Law (PORTAL), Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Aaron S Kesselheim
- Program on Regulation, Therapeutics, and Law (PORTAL), Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
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Olesen SW, Gurry T, Alm EJ. Designing fecal microbiota transplant trials that account for differences in donor stool efficacy. Stat Methods Med Res 2017; 27:2906-2917. [DOI: 10.1177/0962280216688502] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Fecal microbiota transplantation is a highly effective intervention for patients suffering from recurrent Clostridium difficile, a common hospital-acquired infection. Fecal microbiota transplantation’s success as a therapy for C. difficile has inspired interest in performing clinical trials that experiment with fecal microbiota transplantation as a therapy for other conditions like inflammatory bowel disease, obesity, diabetes, and Parkinson’s disease. Results from clinical trials that use fecal microbiota transplantation to treat inflammatory bowel disease suggest that, for at least one condition beyond C. difficile, most fecal microbiota transplantation donors produce stool that is not efficacious. The optimal strategies for identifying and using efficacious donors have not been investigated. We therefore examined the optimal Bayesian response-adaptive strategy for allocating patients to donors and formulated a computationally tractable myopic heuristic. This heuristic computes the probability that a donor is efficacious by updating prior expectations about the efficacy of fecal microbiota transplantation, the placebo rate, and the fraction of donors that produce efficacious stool. In simulations designed to mimic a recent fecal microbiota transplantation clinical trial, for which traditional power calculations predict [Formula: see text] statistical power, we found that accounting for differences in donor stool efficacy reduced the predicted statistical power to [Formula: see text]. For these simulations, using the heuristic Bayesian allocation strategy more than quadrupled the statistical power to [Formula: see text]. We use the results of similar simulations to make recommendations about the number of patients, the number of donors, and the choice of clinical endpoint that clinical trials should use to optimize their ability to detect if fecal microbiota transplantation is effective for treating a condition.
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Affiliation(s)
- Scott W Olesen
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, USA
| | - Thomas Gurry
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, USA
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, USA
| | - Eric J Alm
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, USA
- Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, USA
- The Broad Institute of MIT and Harvard, Cambridge, USA
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Meurer WJ, Legocki L, Mawocha S, Frederiksen SM, Guetterman TC, Barsan W, Lewis R, Berry D, Fetters M. Attitudes and opinions regarding confirmatory adaptive clinical trials: a mixed methods analysis from the Adaptive Designs Accelerating Promising Trials into Treatments (ADAPT-IT) project. Trials 2016; 17:373. [PMID: 27473126 PMCID: PMC4966769 DOI: 10.1186/s13063-016-1493-z] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2015] [Accepted: 07/07/2016] [Indexed: 12/03/2022] Open
Abstract
Background Adaptive designs have been increasingly used in the pharmaceutical and device industries, but adoption within the academic setting has been less widespread — particularly for confirmatory phase trials. We sought to understand perceptions about understanding, acceptability, and scientific validity of adaptive clinical trials (ACTs). Methods We used a convergent mixed methods design using survey and mini-focus group data collection procedures to elucidate attitudes and opinions among “trial community” stakeholders regarding understanding, acceptability, efficiency, scientific validity, and speed of discovery with adaptive designs. Data were collected about various aspects of ACTs using self-administered surveys (paper or Web-based) with visual analog scales (VASs) with free text responses and with mini-focus groups of key stakeholders. Participants were recruited as part of an ongoing NIH/FDA-funded research project exploring the incorporation of ACTs into an existing NIH network that focuses on confirmatory phase clinical trials in neurological emergencies. “Trial community” representatives, namely, clinical investigators, biostatisticians, NIH officials, and FDA scientists involved in the planning of four clinical trials, were eligible to participate. In addition, recent and current members of a clinical trial-oriented NIH study section were also eligible. Results A total of 76 stakeholders completed the survey (out of 91 who were offered it, response rate 84 %). While the VAS attitudinal data showed substantial variability across respondents about acceptability and understanding of ACTs by various constituencies, respondents perceived clinicians to be less likely to understand ACTs and that ACTs probably would increase the efficiency of discovery. Textual and focus group responses emerged into several themes that enhanced understanding of VAS attitudinal data including the following: acceptability of adaptive designs depends on constituency and situation; there is variable understanding of ACTs (limited among clinicians, perceived to be higher at FDA); views about the potential for efficiency depend on the situation and implementation. Participants also frequently mentioned a need for greater education within the academic community. Finally, the empiric, non-quantitative selection of treatments for phase III trials based on limited phase II trials was highlighted as an opportunity for improvement and a potential explanation for the high number of neutral confirmatory trials. Conclusions These data show considerable variations in attitudes and beliefs about ACTs among trial community representatives. For adaptive trials to be fully considered when appropriate and for the research enterprise to realize the full potential of adaptive designs will likely require extensive experience and trust building within the trial community. Electronic supplementary material The online version of this article (doi:10.1186/s13063-016-1493-z) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- William J Meurer
- Department of Emergency Medicine, University of Michigan, TC B1-354 1500 E. Medical Center Drive, Ann Arbor, MI, 48109, USA. .,Department of Neurology, University of Michigan, TC B1-354 1500 E. Medical Center Drive, Ann Arbor, MI, 48109, USA.
| | - Laurie Legocki
- Department of Family Medicine, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Samkeliso Mawocha
- Department of Emergency Medicine, University of Michigan, TC B1-354 1500 E. Medical Center Drive, Ann Arbor, MI, 48109, USA
| | - Shirley M Frederiksen
- Department of Emergency Medicine, University of Michigan, TC B1-354 1500 E. Medical Center Drive, Ann Arbor, MI, 48109, USA
| | - Timothy C Guetterman
- Department of Family Medicine, University of Michigan, Ann Arbor, MI, 48109, USA
| | - William Barsan
- Department of Emergency Medicine, University of Michigan, TC B1-354 1500 E. Medical Center Drive, Ann Arbor, MI, 48109, USA
| | - Roger Lewis
- Harbor-UCLA Medical Center, Torrance, CA, 90502, USA
| | - Donald Berry
- University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Michael Fetters
- Department of Family Medicine, University of Michigan, Ann Arbor, MI, 48109, USA
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Shih WJ, Li G, Wang Y. Methods for flexible sample-size design in clinical trials: Likelihood, weighted, dual test, and promising zone approaches. Contemp Clin Trials 2015; 47:40-8. [PMID: 26674739 DOI: 10.1016/j.cct.2015.12.007] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2015] [Revised: 11/29/2015] [Accepted: 12/03/2015] [Indexed: 11/25/2022]
Abstract
Sample size plays a crucial role in clinical trials. Flexible sample-size designs, as part of the more general category of adaptive designs that utilize interim data, have been a popular topic in recent years. In this paper, we give a comparative review of four related methods for such a design. The likelihood method uses the likelihood ratio test with an adjusted critical value. The weighted method adjusts the test statistic with given weights rather than the critical value. The dual test method requires both the likelihood ratio statistic and the weighted statistic to be greater than the unadjusted critical value. The promising zone approach uses the likelihood ratio statistic with the unadjusted value and other constraints. All four methods preserve the type-I error rate. In this paper we explore their properties and compare their relationships and merits. We show that the sample size rules for the dual test are in conflict with the rules of the promising zone approach. We delineate what is necessary to specify in the study protocol to ensure the validity of the statistical procedure and what can be kept implicit in the protocol so that more flexibility can be attained for confirmatory phase III trials in meeting regulatory requirements. We also prove that under mild conditions, the likelihood ratio test still preserves the type-I error rate when the actual sample size is larger than the re-calculated one.
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Affiliation(s)
- Weichung Joe Shih
- Department of Biostatistics, Rutgers School of Public Health, Rutgers University, Piscataway, NJ 08854, United States.
| | - Gang Li
- Janssen Pharmaceutical Research and Development, Raritan, NJ 08869, United States
| | - Yining Wang
- Janssen Pharmaceutical Research and Development, Raritan, NJ 08869, United States
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Majid A, Bae ON, Redgrave J, Teare D, Ali A, Zemke D. The Potential of Adaptive Design in Animal Studies. Int J Mol Sci 2015; 16:24048-58. [PMID: 26473839 PMCID: PMC4632737 DOI: 10.3390/ijms161024048] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2015] [Revised: 09/23/2015] [Accepted: 09/27/2015] [Indexed: 11/30/2022] Open
Abstract
Clinical trials are the backbone of medical research, and are often the last step in the development of new therapies for use in patients. Prior to human testing, however, preclinical studies using animal subjects are usually performed in order to provide initial data on the safety and effectiveness of prospective treatments. These studies can be costly and time consuming, and may also raise concerns about the ethical treatment of animals when potentially harmful procedures are involved. Adaptive design is a process by which the methods used in a study may be altered while it is being conducted in response to preliminary data or other new information. Adaptive design has been shown to be useful in reducing the time and costs associated with clinical trials, and may provide similar benefits in preclinical animal studies. The purpose of this review is to summarize various aspects of adaptive design and evaluate its potential for use in preclinical research.
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Affiliation(s)
- Arshad Majid
- Sheffield Institute for Translational Neuroscience, University of Sheffield, Sheffield S10 2HQ, UK.
| | - Ok-Nam Bae
- College of Pharmacy Institute of Pharmaceutical Science and Technology, Hanyang University, Ansan 426-791, Korea.
| | - Jessica Redgrave
- Sheffield Institute for Translational Neuroscience, University of Sheffield, Sheffield S10 2HQ, UK.
| | - Dawn Teare
- School of Health and Related Research, University of Sheffield, Sheffield S10 2HQ, UK.
| | - Ali Ali
- Sheffield Institute for Translational Neuroscience, University of Sheffield, Sheffield S10 2HQ, UK.
| | - Daniel Zemke
- Sheffield Institute for Translational Neuroscience, University of Sheffield, Sheffield S10 2HQ, UK.
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Neuroprotection in acute brain injury: an up-to-date review. CRITICAL CARE : THE OFFICIAL JOURNAL OF THE CRITICAL CARE FORUM 2015; 19:186. [PMID: 25896893 PMCID: PMC4404577 DOI: 10.1186/s13054-015-0887-8] [Citation(s) in RCA: 95] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Neuroprotective strategies that limit secondary tissue loss and/or improve functional outcomes have been identified in multiple animal models of ischemic, hemorrhagic, traumatic and nontraumatic cerebral lesions. However, use of these potential interventions in human randomized controlled studies has generally given disappointing results. In this paper, we summarize the current status in terms of neuroprotective strategies, both in the immediate and later stages of acute brain injury in adults. We also review potential new strategies and highlight areas for future research.
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Babbs CF. Choosing inclusion criteria that minimize the time and cost of clinical trials. World J Methodol 2014; 4:109-122. [PMID: 25332910 PMCID: PMC4202479 DOI: 10.5662/wjm.v4.i2.109] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/14/2013] [Accepted: 04/16/2014] [Indexed: 02/06/2023] Open
Abstract
AIM: To present statistical tools to model and optimize the cost of a randomized clinical trial as a function of the stringency of patient inclusion criteria.
METHODS: We consider a two treatment, dichotomous outcome trial that includes a proportion of patients who are strong responders to the tested intervention. Patients are screened for inclusion using an arbitrary number of test results that are combined into an aggregate suitability score. The screening score is regarded as a diagnostic test for the responsive phenotype, having a specific cutoff value for inclusion and a particular sensitivity and specificity. The cutoff is a measure of stringency of inclusion criteria. Total cost is modeled as a function of the cutoff value, number of patients screened, the number of patients included, the case occurrence rate, response probabilities for control and experimental treatments, and the trial duration required to produce a statistically significant result with a specified power. Regression methods are developed to estimate relevant model parameters from pilot data in an adaptive trial design.
RESULTS: The patient numbers and total cost are strongly related to the choice of the cutoff for inclusion. Clear cost minimums exist between 5.6 and 6.1 on a representative 10-point scale of exclusiveness. Potential cost savings for typical trial scenarios range in millions of dollars. As the response rate for controls approaches 50%, the proper choice of inclusion criteria can mean the difference between a successful trial and a failed trial.
CONCLUSION: Early formal estimation of optimal inclusion criteria allows planning of clinical trials to avoid high costs, excessive delays, and moral hazards of Type II errors.
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Yasmina A, Deneer VHM, Maitland-van der Zee AH, van Staa TP, de Boer A, Klungel OH. Application of routine electronic health record databases for pharmacogenetic research. J Intern Med 2014; 275:590-604. [PMID: 24581153 DOI: 10.1111/joim.12226] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Inter-individual variability in drug responses is a common problem in pharmacotherapy. Several factors (non-genetic and genetic) influence drug responses in patients. When aiming to obtain an optimal benefit-risk ratio of medicines and with the emergence of genotyping technology, pharmacogenetic studies are important for providing recommendations on drug treatments. Advances in electronic healthcare information systems can contribute to increasing the quality and efficiency of such studies. This review describes the definition of pharmacogenetics, gene selection and study design for pharmacogenetic research. It also summarizes the potential of linking pharmacoepidemiology and pharmacogenetics (along with its strengths and limitations) and provides examples of pharmacogenetic studies utilizing electronic health record databases.
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Affiliation(s)
- A Yasmina
- Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Utrecht, The Netherlands; Department of Pharmacology and Therapeutics, Faculty of Medicine, Lambung Mangkurat University, Banjarmasin, Indonesia
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Franchetti Y, Anderson SJ, Sampson AR. An adaptive two-stage dose-response design method for establishing proof of concept. J Biopharm Stat 2014; 23:1124-54. [PMID: 23957520 DOI: 10.1080/10543406.2013.813519] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Abstract
We propose an adaptive two-stage dose-response design where a prespecified adaptation rule is used to add and/or drop treatment arms between the stages. We extend the multiple comparison procedures-modeling (MCP-Mod) approach into a two-stage design. In each stage, we use the same set of candidate dose-response models and test for a dose-response relationship or proof of concept (PoC) via model-associated statistics. The stage-wise test results are then combined to establish "global" PoC using a conditional error function. Our simulation studies showed good and more robust power in our design method compared to conventional and fixed designs.
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Affiliation(s)
- Yoko Franchetti
- Department of Biostatistics and Computational Biology, Dana-Faber Cancer Institute and Harvard School of Public Health, Boston, Massachusetts 02215, USA.
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14
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Dong G. A varying-stage adaptive phase II/III clinical trial design. Stat Med 2013; 33:1272-87. [DOI: 10.1002/sim.6036] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2013] [Accepted: 10/18/2013] [Indexed: 11/10/2022]
Affiliation(s)
- Gaohong Dong
- Biometrics & Statistical Science, Integrated Information Sciences (IIS)-Integrated Hospital Care (IHC); Novartis Pharmaceuticals Corporation; East Hanover NJ U.S.A
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15
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Campbell G. Similarities and Differences of Bayesian Designs and Adaptive Designs for Medical Devices: A Regulatory View. Stat Biopharm Res 2013. [DOI: 10.1080/19466315.2013.846873] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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Boessen R, van der Baan F, Groenwold R, Egberts A, Klungel O, Grobbee D, Knol M, Roes K. Optimizing trial design in pharmacogenetics research: comparing a fixed parallel group, group sequential, and adaptive selection design on sample size requirements. Pharm Stat 2013; 12:366-74. [DOI: 10.1002/pst.1599] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2012] [Revised: 08/02/2013] [Accepted: 08/26/2013] [Indexed: 01/09/2023]
Affiliation(s)
- Ruud Boessen
- Julius Center for Health Sciences and Primary Care; University Medical Center Utrecht; Utrecht The Netherlands
| | - Frederieke van der Baan
- Julius Center for Health Sciences and Primary Care; University Medical Center Utrecht; Utrecht The Netherlands
- Division of Pharmacoepidemiology and Clinical Pharmacology, Faculty of Science, Utrecht Institute for Pharmaceutical Sciences; Utrecht University; Utrecht The Netherlands
| | - Rolf Groenwold
- Julius Center for Health Sciences and Primary Care; University Medical Center Utrecht; Utrecht The Netherlands
| | - Antoine Egberts
- Division of Pharmacoepidemiology and Clinical Pharmacology, Faculty of Science, Utrecht Institute for Pharmaceutical Sciences; Utrecht University; Utrecht The Netherlands
- Department of Clinical Pharmacy; University Medical Center Utrecht; Utrecht The Netherlands
| | - Olaf Klungel
- Julius Center for Health Sciences and Primary Care; University Medical Center Utrecht; Utrecht The Netherlands
- Division of Pharmacoepidemiology and Clinical Pharmacology, Faculty of Science, Utrecht Institute for Pharmaceutical Sciences; Utrecht University; Utrecht The Netherlands
| | - Diederick Grobbee
- Julius Center for Health Sciences and Primary Care; University Medical Center Utrecht; Utrecht The Netherlands
| | - Mirjam Knol
- Julius Center for Health Sciences and Primary Care; University Medical Center Utrecht; Utrecht The Netherlands
- Division of Pharmacoepidemiology and Clinical Pharmacology, Faculty of Science, Utrecht Institute for Pharmaceutical Sciences; Utrecht University; Utrecht The Netherlands
| | - Kit Roes
- Julius Center for Health Sciences and Primary Care; University Medical Center Utrecht; Utrecht The Netherlands
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Hyslop T, Michael Y, Avery T, Rui H. Population and target considerations for triple-negative breast cancer clinical trials. Biomark Med 2013; 7:11-21. [PMID: 23387481 PMCID: PMC3677035 DOI: 10.2217/bmm.12.114] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Triple-negative breast cancer (TNBC) is an aggressive disease subtype that has a poor prognosis. Extensive epidemiological evidence demonstrates clear socioeconomic and demographic associations with increased likelihood of TNBC in both poorer and minority populations. Thus, biological aggressiveness with few known therapeutic directions generates disparities in breast cancer outcomes for vulnerable populations. Emerging molecular evidence of potential targets in triple-negative subpopulations offers great potential for future clinical trial directions. However, trials must appropriately consider populations at risk for aggressive subtypes of disease in order to address this disparity most completely. New US FDA draft guidance documents provide both flexible outcomes for accelerated approvals as well as flexibility in design with adaptive trials. Careful planning with design, potential patient population and choices of molecular targets informed by biomarkers will be critical to address TNBC clinical care.
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Affiliation(s)
- Terry Hyslop
- Department of Pharmacology & Experimental Therapeutics, Division of Biostatistics, Thomas Jefferson University, Kimmel Cancer Center, Philadelphia, PA, USA.
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Meurer WJ, Lewis RJ, Tagle D, Fetters MD, Legocki L, Berry S, Connor J, Durkalski V, Elm J, Zhao W, Frederiksen S, Silbergleit R, Palesch Y, Berry DA, Barsan WG. An overview of the adaptive designs accelerating promising trials into treatments (ADAPT-IT) project. Ann Emerg Med 2012; 60:451-7. [PMID: 22424650 PMCID: PMC3557826 DOI: 10.1016/j.annemergmed.2012.01.020] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2011] [Revised: 01/17/2012] [Accepted: 01/23/2012] [Indexed: 11/17/2022]
Abstract
Randomized clinical trials, which aim to determine the efficacy and safety of drugs and medical devices, are a complex enterprise with myriad challenges, stakeholders, and traditions. Although the primary goal is scientific discovery, clinical trials must also fulfill regulatory, clinical, and ethical requirements. Innovations in clinical trials methodology have the potential to improve the quality of knowledge gained from trials, the protection of human subjects, and the efficiency of clinical research. Adaptive clinical trial methods represent a broad category of innovations intended to address a variety of long-standing challenges faced by investigators, such as sensitivity to previous assumptions and delayed identification of ineffective treatments. The implementation of adaptive clinical trial methods, however, requires greater planning and simulation compared with a more traditional design, along with more advanced administrative infrastructure for trial execution. The value of adaptive clinical trial methods in exploratory phase (phase 2) clinical research is generally well accepted, but the potential value and challenges of applying adaptive clinical trial methods in large confirmatory phase clinical trials are relatively unexplored, particularly in the academic setting. In the Adaptive Designs Accelerating Promising Trials Into Treatments (ADAPT-IT) project, a multidisciplinary team is studying how adaptive clinical trial methods could be implemented in planning actual confirmatory phase trials in an established, National Institutes of Health-funded clinical trials network. The overarching objectives of ADAPT-IT are to identify and quantitatively characterize the adaptive clinical trial methods of greatest potential value in confirmatory phase clinical trials and to elicit and understand the enthusiasms and concerns of key stakeholders that influence their willingness to try these innovative strategies.
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Affiliation(s)
- William J. Meurer
- Departments of Emergency Medicine and Neurology, University of Michigan, Ann Arbor
| | - Roger J. Lewis
- Department of Emergency Medicine, Harbor-UCLA Medical Center, Los Angeles; Los Angeles Biomedical Research Institute; David Geffen School of Medicine - UCLA
| | - Danilo Tagle
- Extramural Research Program, National Institutes of Neurological Disorders and Stroke
| | | | - Laurie Legocki
- Department of Family Medicine, University of Michigan, Ann Arbor
| | | | | | - Valerie Durkalski
- Division of Biostatistics and Epidemiology, Medical University of South Carolina
| | - Jordan Elm
- Division of Biostatistics and Epidemiology, Medical University of South Carolina
| | - Wenle Zhao
- Division of Biostatistics and Epidemiology, Medical University of South Carolina
| | | | | | - Yuko Palesch
- Division of Biostatistics and Epidemiology, Medical University of South Carolina
| | - Donald A. Berry
- Department of Biostatistics, University of Texas M.D. Anderson Cancer Center, Houston, Texas; Berry Consultants
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Antman E, Weiss S, Loscalzo J. Systems pharmacology, pharmacogenetics, and clinical trial design in network medicine. WILEY INTERDISCIPLINARY REVIEWS-SYSTEMS BIOLOGY AND MEDICINE 2012; 4:367-83. [PMID: 22581565 DOI: 10.1002/wsbm.1173] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
The rapidly growing disciplines of systems biology and network science are now poised to meet the fields of clinical medicine and pharmacology. Principles of systems pharmacology can be applied to drug design and, ultimately, testing in human clinical trials. Rather than focusing exclusively on single drug targets, systems pharmacology examines the holistic response of a phenotype-dependent pathway or pathways to drug perturbation. Knowledge of individual pharmacogenetic profiles further modulates the responses to these drug perturbations, moving the field toward more individualized ('personalized') drug development. The speed with which the information required to assess these system responses and their genomic underpinnings is changing and the importance of identifying the optimal drug or drug combinations for maximal benefit and minimal risk require that clinical trial design strategies be adaptable. In this paper, we review the tenets of adaptive clinical trial design as they may apply to an era of expanding knowledge of systems pharmacology and pharmacogenomics, and clinical trail design in network medicine.
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Affiliation(s)
- Elliott Antman
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
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21
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van der Baan FH, Knol MJ, Klungel OH, Egberts ACG, Grobbee DE, Roes KCB. Potential of adaptive clinical trial designs in pharmacogenetic research. Pharmacogenomics 2012; 13:571-8. [DOI: 10.2217/pgs.12.10] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Adaptive trial designs can be beneficial in pharmacogenetic research when prior uncertainty exists regarding the exact role and clinical relevance of genetic variability in drug response. This type of design enables us to learn about the effect of the genetic variability on drug response and to immediately use this information for the remainder of the study. For different types of adaptive trial designs, we discuss when and how the designs are suitable for pharmacogenetic research: adaptation of randomization, adaptation of patient enrollment and adaptive enrichment. To illustrate the potential benefits of an adaptive design over a fixed design, we simulated an adaptive trial based on the results of the IPASS trial. With a simple model we show that for this example an adaptive enrichment design would have led to a smaller trial, with less EGF receptor mutation-negative patients unnecessarily exposed to the drug, without compromising the α level or reducing power.
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Affiliation(s)
- Frederieke H van der Baan
- Julius Center for Health Sciences & Primary Care, University Medical Center Utrecht, The Netherlands
- Division of Pharmacoepidemiology & Clinical Pharmacology, Faculty of Science, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Utrecht, The Netherlands
| | - Mirjam J Knol
- Julius Center for Health Sciences & Primary Care, University Medical Center Utrecht, The Netherlands
- Division of Pharmacoepidemiology & Clinical Pharmacology, Faculty of Science, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Utrecht, The Netherlands
| | - Olaf H Klungel
- Julius Center for Health Sciences & Primary Care, University Medical Center Utrecht, The Netherlands
- Division of Pharmacoepidemiology & Clinical Pharmacology, Faculty of Science, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Utrecht, The Netherlands
| | - Antoine CG Egberts
- Division of Pharmacoepidemiology & Clinical Pharmacology, Faculty of Science, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Utrecht, The Netherlands
- Department of Clinical Pharmacy, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Diederick E Grobbee
- Julius Center for Health Sciences & Primary Care, University Medical Center Utrecht, The Netherlands
| | - Kit CB Roes
- Julius Center for Health Sciences & Primary Care, University Medical Center Utrecht, The Netherlands
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22
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Englert S, Kieser M. Adaptive designs for single-arm phase II trials in oncology. Pharm Stat 2012; 11:241-9. [PMID: 22411839 DOI: 10.1002/pst.541] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2011] [Revised: 08/02/2011] [Accepted: 10/09/2011] [Indexed: 12/19/2022]
Abstract
Clinical phase II trials in oncology are conducted to determine whether the activity of a new anticancer treatment is promising enough to merit further investigation. Two-stage designs are commonly used for this situation to allow for early termination. Designs proposed in the literature so far have the common drawback that the sample sizes for the two stages have to be specified in the protocol and have to be adhered to strictly during the course of the trial. As a consequence, designs that allow a higher extent of flexibility are desirable. In this article, we propose a new adaptive method that allows an arbitrary modification of the sample size of the second stage using the results of the interim analysis or external information while controlling the type I error rate. If the sample size is not changed during the trial, the proposed design shows very similar characteristics to the optimal two-stage design proposed by Chang et al. (Biometrics 1987; 43:865-874). However, the new design allows the use of mid-course information for the planning of the second stage, thus meeting practical requirements when performing clinical phase II trials in oncology.
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Affiliation(s)
- Stefan Englert
- Institute of Medical Biometry and Informatics, University of Heidelberg, Heidelberg, Germany
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23
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Lang T. Adaptive trial design: could we use this approach to improve clinical trials in the field of global health? Am J Trop Med Hyg 2012; 85:967-70. [PMID: 22144428 PMCID: PMC3225172 DOI: 10.4269/ajtmh.2011.11-0151] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
We need more clinical trials in the world's poorest regions to evaluate new drugs and vaccines, and also to find better ways to manage health issues. Clinical trials are expensive, time consuming, and cumbersome. However, in wealthier regions these limiting factors are being addressed to make trials less administrative and improve the designs. A good example is adaptive trial design. This innovation is becoming accepted by the regulators and has been taken up by the pharmaceutical industry to reduce product development times and costs. If this approach makes trials easier and less expensive surely we should be implementing this approach in the field of tropical medicine and international health? As yet this has rarely been proposed and there are few examples. There is a need for raising the awareness of these design approaches because they could be used to make dramatic improvements to clinical research in developing countries.
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Affiliation(s)
- Trudie Lang
- Global Health Clinical Trials Programme, Centre for Tropical Medicine, University of Oxford, Oxford, United Kingdom.
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24
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Chevret S. Bayesian adaptive clinical trials: a dream for statisticians only? Stat Med 2011; 31:1002-13. [PMID: 21905067 DOI: 10.1002/sim.4363] [Citation(s) in RCA: 40] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2010] [Accepted: 07/11/2011] [Indexed: 01/06/2023]
Abstract
Adaptive or 'flexible' designs have emerged, mostly within frequentist frameworks, as an effective way to speed up the therapeutic evaluation process. Because of their flexibility, Bayesian methods have also been proposed for Phase I through Phase III adaptive trials; however, it has been reported that they are poorly used in practice. We aim to describe the international scientific production of Bayesian clinical trials by investigating the actual development and use of Bayesian 'adaptive' methods in the setting of clinical trials. A bibliometric study was conducted using the PubMed and Science Citation Index-Expanded databases. Most of the references found were biostatistical papers from various teams around the world. Most of the authors were from the US, and a large proportion was from the MD Anderson Cancer Center (University of Texas, Houston, TX). The spread and use of these articles depended heavily on their topic, with 3.1% of the biostatistical articles accumulating at least 25 citations within 5 years of their publication compared with 15% of the reviews and 32% of the clinical articles. We also examined the reasons for the limited use of Bayesian adaptive design methods in clinical trials and the areas of current and future research to address these challenges. Efforts to promote Bayesian approaches among statisticians and clinicians appear necessary.
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Affiliation(s)
- Sylvie Chevret
- Biostatistics Department, Saint-Louis Hospital, AP-HP, Paris, France.
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25
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Abstract
We review adaptive designs for clinical trials, giving special attention to the control of the Type I error in late-phase confirmatory trials, when the trial planner wishes to adjust the final sample size of the study in response to an unblinded analysis of interim estimates of treatment effects. We point out that there is considerable inefficiency in using the adaptive designs that employ conditional power calculations to reestimate the sample size and that maintain the Type I error by using certain weighted test statistics. Although these adaptive designs have little advantage over familiar group-sequential designs, our review also describes recent developments in adaptive designs that are both flexible and efficient. We also discuss the use of Bayesian designs, when the context of use demands control over operating characteristics (Type I and II errors) and correction of the bias of estimated treatment effects.
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Affiliation(s)
- Tze Leung Lai
- Department of Statistics, Stanford University, Stanford, California 94305
- Department of Health Research and Policy, Stanford University, Stanford, California 94305
| | - Philip William Lavori
- Department of Statistics, Stanford University, Stanford, California 94305
- Department of Health Research and Policy, Stanford University, Stanford, California 94305
| | - Mei-Chiung Shih
- Department of Health Research and Policy, Stanford University, Stanford, California 94305
- Cooperative Studies Program, U.S. Department of Veterans Affairs, Mountain View, California 94043
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26
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Abstract
In addition to the expensive and lengthy process of developing a new medicine, the attrition rate in clinical research was on the rise, resulting in stagnation in the development of new compounds. As a consequence to this, the US Food and Drug Administration released a critical path initiative document in 2004, highlighting the need for developing innovative trial designs. One of the innovations suggested the use of adaptive designs for clinical trials. Thus, post critical path initiative, there is a growing interest in using adaptive designs for the development of pharmaceutical products. Adaptive designs are expected to have great potential to reduce the number of patients and duration of trial and to have relatively less exposure to new drug. Adaptive designs are not new in the sense that the task of interim analysis (IA)/review of the accumulated data used in adaptive designs existed in the past too. However, such reviews/analyses of accumulated data were not necessarily planned at the stage of planning clinical trial and the methods used were not necessarily compliant with clinical trial process. The Bayesian approach commonly used in adaptive designs was developed by Thomas Bayes in the 18th century, about hundred years prior to the development of modern statistical methods by the father of modern statistics, Sir Ronald A. Fisher, but the complexity involved in Bayesian approach prevented its use in real life practice. The advances in the field of computer and information technology over the last three to four decades has changed the scenario and the Bayesian techniques are being used in adaptive designs in addition to other sequential methods used in IA. This paper attempts to describe the various adaptive designs in clinical trial and views of stakeholders about feasibility of using them, without going into mathematical complexities.
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Affiliation(s)
- Suresh Bowalekar
- Managing Director, PharmaNet Clinical Services Pvt. Ltd., Mumbai, Maharashtra, India
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27
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Hung HMJ, Wang SJ, O'Neill R. Flexible design clinical trial methodology in regulatory applications. Stat Med 2011; 30:1519-27. [PMID: 21344470 DOI: 10.1002/sim.4021] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2010] [Accepted: 06/11/2010] [Indexed: 11/06/2022]
Abstract
Adaptive designs or flexible designs in a broader sense have increasingly been considered in planning pivotal registration clinical trials. Sample size reassessment design and adaptive selection design are two of such designs that appear in regulatory applications. At the design stage, consideration of sample size reassessment at an interim time of the trial should lead to extensive discussion about how to appropriately size the trial. Additionally, careful attention needs to be paid to the issue of how the size of the trial is impacted by the requirement that the final p-value of the trial meets the specific threshold of a clinically meaningful effect. These issues are not straightforward and will be discussed in this work. In a trial design that allows selection between a pre-specified patient subgroup and the initially planned overall patient population based on the accumulating data, there is an issue of what the 'overall' population means. In addition, it is critically important to know how such selection influences the validity of statistical inferences on the potentially modified overall population. This work presents the biases that may incur under adaptive patient selection designs.
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Affiliation(s)
- H M James Hung
- Division of Biometrics I, Office of Biostatistics, OTS, CDER, Food and Drug Administration, Silver Spring, MD 20994-0002, USA.
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28
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Jenkins M, Stone A, Jennison C. An adaptive seamless phase II/III design for oncology trials with subpopulation selection using correlated survival endpoints†. Pharm Stat 2010; 10:347-56. [DOI: 10.1002/pst.472] [Citation(s) in RCA: 135] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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29
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Cheng B, Chow SC. On Flexibility of Adaptive Designs and Criteria for Choosing A Good One—A Discussion of FDA Draft Guidance. J Biopharm Stat 2010; 20:1171-7. [DOI: 10.1080/10543406.2010.514460] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Affiliation(s)
- Bin Cheng
- a Department of Biostatistics , Columbia University , New York, New York, USA
| | - Shein-Chung Chow
- b Department of Biostatistics and Bioinformatics , Duke University School of Medicine , Durham, North Carolina, USA
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30
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Guidance for Industry: Adaptive Design Clinical Trials for Drugs and Biologics [excerpts]. Biotechnol Law Rep 2010. [DOI: 10.1089/blr.2010.9977] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
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31
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Bretz F, Branson M, Burman CF, Chuang-Stein C, Coffey CS. Adaptivity in drug discovery and development. Drug Dev Res 2009. [DOI: 10.1002/ddr.20285] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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32
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Miksad RA, Gönen M, Lynch TJ, Roberts TG. Interpreting trial results in light of conflicting evidence: a Bayesian analysis of adjuvant chemotherapy for non-small-cell lung cancer. J Clin Oncol 2009; 27:2245-52. [PMID: 19307513 PMCID: PMC2674005 DOI: 10.1200/jco.2008.16.2586] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2008] [Accepted: 11/18/2008] [Indexed: 01/10/2023] Open
Abstract
PURPOSE When successive randomized trials contradict prior evidence, clinicians may be unsure how to evaluate them: Does accumulating evidence warrant changing practice? An increasingly popular solution, Bayesian statistics quantitatively evaluate new results in context. This study provides a clinically relevant example of Bayesian methods. METHODS Three recent non-small-cell lung cancer adjuvant chemotherapy trials were evaluated in light of prior conflicting data. Results were used from International Adjuvant Lung Trial (IALT), JBR.10, and Adjuvant Navelbine International Trialist Association (ANITA). Prior evidence was sequentially updated to calculate the probability of each survival benefit level (overall and by stage) and variance. Sensitivity analysis was performed using expert opinion and uninformed estimates of survival benefit prior probability. RESULTS The probability of a 4% survival benefit increased from 33% before IALT to 64% after IALT. After sequential updating with JBR.10 and ANITA, this probability was 82% (hazard ratio = 0.84; 95% CI, 0.77 to 0.91). IALT produced the largest decrease in variance (61%) and decreased the chance of survival decrement to 0%. Sensitivity analysis did not support a survival benefit after IALT. However, sequential updating substantiated a 4% survival benefit and, for stage II and III, more than 90% probability of a 6% benefit and 50% probability of a 12% benefit. CONCLUSION When evaluated in context with prior data, IALT did not support a 4% survival benefit. However, sequential updating with JBR.10 and ANITA did. A model for future assessments, this study demonstrates the unique ability of Bayesian analysis to evaluate results that contradict prior evidence.
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Affiliation(s)
- Rebecca A Miksad
- Department of Medicine, Division of Hematology and Oncology, Beth Israel Deaconess Hospital, Harvard Medical School, Boston, MA 02215, USA.
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33
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Hung HJ. Considerations in Adapting Clinical Trial Design. J Formos Med Assoc 2008; 107:14-8. [DOI: 10.1016/s0929-6646(09)60004-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
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34
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Brannath W, Mehta CR, Posch M. Exact Confidence Bounds Following Adaptive Group Sequential Tests. Biometrics 2008; 65:539-46. [DOI: 10.1111/j.1541-0420.2008.01101.x] [Citation(s) in RCA: 29] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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35
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Cummings JL. Optimizing phase II of drug development for disease-modifying compounds. Alzheimers Dement 2008; 4:S15-20. [PMID: 18631992 DOI: 10.1016/j.jalz.2007.10.002] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2007] [Accepted: 10/24/2007] [Indexed: 11/24/2022]
Abstract
Phase II proof of concept (POC) (IIa) and dose-finding (IIb) studies represent major challenges in drug development. Prolonged development times delay effective therapies from reaching patients in need and adversely affect industry goals of decreasing time to market. Biomarkers including magnetic resonance imaging, cerebrospinal fluid tau and amyloid beta, and amyloid positron emission tomography have been considered as alternative outcomes to clinical measures. None of these is yet validated. Population enrichment is another possible solution to POC studies. More rapid progression to prespecified milestones can be achieved by enriching the population with risk factors. Conclusions based on enriched populations must be extrapolated with caution. Clinical measures with greater sensitivity than standard trial instruments might represent another strategy applicable to POC studies. Adaptive dose-response designs are being considered as a means of shortening phase IIb studies and creating a seamless interface with phase III. None of these strategies have been validated in a successful drug development program; all have some promise for reforming phase II and answering the central question of "how much information is sufficient to proceed to phase III without excessive risk for failure?"
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Affiliation(s)
- Jeffrey L Cummings
- Department of Neurology and Psychiatry, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.
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36
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Bartroff J, Lai TL. Generalized Likelihood Ratio Statistics and Uncertainty Adjustments in Efficient Adaptive Design of Clinical Trials. Seq Anal 2008. [DOI: 10.1080/07474940802241009] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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37
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Abstract
Novel approaches to drug development along with incentives to reward risk undertaken during early drug development have been proposed to improve the medical product development process. The American College of Clinical Pharmacology held its 19th Frontiers Symposium in mid-2007 to provide a forum to share new knowledge about disease progression models and adaptive and other novel trial designs. These topics reflect the changing paradigm of drug development and regulatory innovation as outlined in the Food and Drug Administration's Critical Path Initiative. This meeting report summarizes some of the presentations and panel discussions among academic, industrial, and regulatory participants regarding clinical trial design, including adaptive Bayesian approaches.
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Affiliation(s)
- Barbara Ameer
- University of Medicine and Dentistry of New Jersey, Robert Wood Johnson Medical School, Department of Medicine, New Brunswick, NJ, USA.
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38
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Editorial – Year 2007 Report. Biom J 2008. [DOI: 10.1002/bimj.200810411] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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39
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Hung HMJ, Wang SJ, O'Neill R. Statistical considerations for testing multiple endpoints in group sequential or adaptive clinical trials. J Biopharm Stat 2008; 17:1201-10. [PMID: 18027226 DOI: 10.1080/10543400701645405] [Citation(s) in RCA: 38] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
Many clinical trials are designed with a fixed sample size or total number of events to detect a postulated size of treatment effect on a primary efficacy endpoint. When the trial is completed and the primary efficacy endpoint achieves statistical significance, formal statistical testing of other clinically important secondary endpoints often follows in order for the statistically and clinically significant results of these endpoints to be included in the label of the test pharmaceutical product. In conventional fixed designs without any interim analysis or trial extension, these endpoints are often tested in a pre-specified hierarchical order, following the closed testing principle. This testing strategy ensures a strong control of the overall type I error. However, when trials are conducted using a group-sequential design with interim analyses or can be extended using an adaptive design with an increase of sample size or total number of events, this conventional hierarchical testing strategy may violate the closure principle and the overall type I error rate may not be controlled in the strong sense.
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Affiliation(s)
- H M James Hung
- Division of Biometrics I, OB/OTS/CDER, FDA, Silver Spring, Maryland 20993-0002, USA.
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40
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Jennison C, Turnbull BW. Adaptive Seamless Designs: Selection and Prospective Testing of Hypotheses. J Biopharm Stat 2007; 17:1135-61. [DOI: 10.1080/10543400701645215] [Citation(s) in RCA: 38] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Affiliation(s)
| | - Bruce W. Turnbull
- b Department of Statistical Science , Cornell University , Ithaca, New York, USA
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41
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Chang M, Kenley S, Bull J, Chiu YY, Wang W, Wakeford C, McCarthy K. Innovative approaches in drug development. J Biopharm Stat 2007; 17:775-89. [PMID: 17885865 DOI: 10.1080/10543400701513926] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
This is the white paper on Innovative Approaches in Drug Development developed by the Biotechnology Industry Organization's (BIO) Clinical Trial Designs Workgroup. As recognized by the Food and Drug Administration's Critical Path Opportunities Report, the need to develop and apply innovative approaches to create new trial designs and clinical development programs is rapidly on the rise. Such novel approaches hold tremendous potential in the ability to refine mechanisms lying at the fundamental core of product safety and efficacy. The paper addresses the opportunities, challenges in pre-clinical and clinical trial design innovations; emphasizes the importance of safety database; and makes recommendations for carrying out the innovative approaches. The views expressed here in are solely those of the authors and do not reflect the official views of the Biotechnology Industry Organization.
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Affiliation(s)
- Mark Chang
- Millennium Pharmaceuticals, Inc., Cambridge, Massachusetts 02139, USA.
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42
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Brunner E, Schumacher M. Year 2006 – Report. Biom J 2007. [DOI: 10.1002/bimj.200710327] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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43
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Abstract
Flexible designs offer a large amount of flexibility in clinical trials with control of the type I error rate. This allows the combination of trials from different clinical phases of a drug development process. Such combinations require designs where hypotheses are selected and/or added at interim analysis without knowing the selection rule in advance so that both flexibility and multiplicity issues arise. The paper reviews the basic principles and some of the common methods for reaching flexibility while controlling the family-wise error rate in the strong sense. Flexible designs have been criticized because they may lead to different weights for the patients from the different stages when reassessing sample sizes. Analyzing the data in a conventional way avoids such unequal weighting but may inflate the multiple type I error rate. In cases where the conditional type I error rates of the new design (and conventional analysis) are below the conditional type I error rates of the initial design the conventional analysis may, however, be done without inflating the type I error rate. Focusing on a parallel group design with two treatments and a common control, we use this principle to investigate when we can select one treatment, reassess sample sizes and test the corresponding null hypotheses by the conventional level alpha z-test without compromising on the multiple type I error rate.
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44
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Wang SJ, O'Neill RT, Hung HMJ. Approaches to evaluation of treatment effect in randomized clinical trials with genomic subset. Pharm Stat 2007; 6:227-44. [PMID: 17688238 DOI: 10.1002/pst.300] [Citation(s) in RCA: 215] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
With the advances in human genomic/genetic studies, the clinical trial community gradually recognizes that phenotypically homogeneous patients may be heterogeneous at the genomic level. The genomic technology brings a possible avenue for developing a genomic (composite) biomarker to predict a genomically responsive patient subset that may have a (much) higher likelihood of benefiting from a treatment. Randomized controlled trial is the mainstay to provide scientifically convincing evidence of a purported effect a new treatment may demonstrate. In conventional clinical trials, the primary clinical hypothesis pertains to the therapeutic effect in all patients who are eligible for the study defined by the primary efficacy endpoint. The aspect of one-size-fits-all surrounding the conventional design has been challenged, particularly when the diseases may be heterogeneous due to observable clinical characteristics and/or unobservable underlying the genomic characteristics. Extension from the conventional single population design objective to an objective that encompasses two possible patient populations will allow more informative evaluation in the patients having different degrees of responsiveness to medication. Building in conventional clinical trials, an additional genomic objective can generate an appealing conceptual framework from the patient's perspective in addressing personalized medicine in well-controlled clinical trials. There are many perceived benefits of personalized medicine that are based on the notion of being genomically proactive in the identification of disease and prevention of disease or recurrence. In this paper, we show that an adaptive design approach can be constructed to study a clinical hypothesis of overall treatment effect and a hypothesis of treatment effect in a genomic subset more efficiently than the conventional non-adaptive approach.
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Affiliation(s)
- Sue-Jane Wang
- Office of Biostatistics, Office of Translational Sciences, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, MD 20993, USA.
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Schäfer H, Timmesfeld N, Müller HH. An overview of statistical approaches for adaptive designs and design modifications. Biom J 2006; 48:507-20. [PMID: 16972702 DOI: 10.1002/bimj.200510234] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
With adaptive design methods for clinical trials, design elements such as sample size or further interim sample sizes may be changed during the course of the trial depending on all previously collected data. The focus of the overview is on group sequential approaches where the types of adaptations need not be specified in advance. An overview of the different statistical approaches for adaptive design methods proposed in the literature is given, relations between these methods are described and some perspectives of application and for future research are discussed.
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Affiliation(s)
- Helmut Schäfer
- Institute of Medical Biometry and Epidemiology, Philipps-University of Marburg, Bunsenstrasse 3, D-35037 Marburg, Germany.
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46
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Abstract
This is a discussion of the following three papers appearing in this special issue on adaptive designs: 'FDA's critical path initiative: A perspective on contributions of biostatistics' by Robert T. O'Neill, 'A regulatory view on adaptive/flexible clinical trial design' by H. M. James Hung, Robert T. O'Neill, Sue-Jane Wang and John Lawrence; and 'Confirmatory clinical trials with an adaptive design' by Armin Koch.
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Affiliation(s)
- Peter Bauer
- Core Unit for Medical Statistics and Informatics, Section of Medical Statistics, Medical University of Vienna, Spitalgasse 23, 1090 Vienna, Austria.
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47
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Schmidli H, Bretz F, Racine A, Maurer W. Confirmatory seamless phase II/III clinical trials with hypotheses selection at interim: applications and practical considerations. Biom J 2006; 48:635-43. [PMID: 16972715 DOI: 10.1002/bimj.200510231] [Citation(s) in RCA: 80] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Adaptive seamless phase II/III designs combine a phase II and a phase III study into one single confirmatory clinical trial. Several examples of such designs are presented, where the primary endpoint is binary, time-to-event or continuous. The interim adaptations considered include the selection of treatments and the selection of hypotheses related to a pre-specified subgroup of patients. Practical aspects concerning the planning and implementation of adaptive seamless confirmatory studies are also discussed.
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Affiliation(s)
- Heinz Schmidli
- Novartis Pharma AG, Lichtstrasse 35, 4002 Basel, Switzerland
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48
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Abstract
This is a discussion of the following three papers appearing in this special issue on adaptive designs: 'FDA's critical path initiative: A perspective on contributions of biostatistics' by Robert T. O'Neill, 'A regulatory view on adaptive/flexible clinical trial design' by H. M. James Hung, Robert T. O'Neill, Sue-Jane Wang and John Lawrence; and 'Confirmatory clinical trials with an adaptive design' by Armin Koch.
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Affiliation(s)
- Helmut Schäfer
- Institute of Medical Biometry and Epidemiology, Philipps-University Marburg, Germany.
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49
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Abstract
This is a discussion of the following three papers appearing in this special issue on adaptive designs: 'A regulatory view on adaptive/flexible clinical trial design' by H. M. James Hung, Robert T. O'Neill, Sue-Jane Wang and John Lawrence; 'Confirmatory clinical trials with an adaptive design' by Armin Koch; and 'FDA's critical path initiative: A perspective on contributions of biostatistics' by Robert T. O'Neill.
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Affiliation(s)
- Janet Wittes
- Statistics Collaborative, 1625 Massachusetts Ave., NW, Washington, DC 20036, USA.
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50
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Mehta CR, Jemiai Y. A consultant's perspective on the regulatory hurdles to adaptive trials. Biom J 2006; 48:604-8; discussion 613-22. [PMID: 16972712 DOI: 10.1002/bimj.200610249] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
This is a discussion of the following two papers appearing in this special issue on adaptive designs: 'A regulatory view on adaptive/flexible clinical trial design' by H. M. James Hung, Robert T. O'Neill, Sue-Jane Wang and John Lawrence and 'Confirmatory clinical trials with an adaptive design' by Armin Koch.
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Affiliation(s)
- Cyrus R Mehta
- Cytel Inc., 675 Massachusetts Ave., Cambridge, MA 02139-3309, USA.
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