1
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Dong Y, Paux G, Broglio K, Cooner F, Gao G, He W, Gao L, Xue X, He P. Use of Seamless Study Designs in Oncology Clinical Development- A Survey Conducted by IDSWG Oncology Sub-team. Ther Innov Regul Sci 2024:10.1007/s43441-024-00676-9. [PMID: 38909174 DOI: 10.1007/s43441-024-00676-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Accepted: 06/07/2024] [Indexed: 06/24/2024]
Abstract
Seamless study designs have the potential to accelerate clinical development. The use of innovative seamless designs has been increasing in the oncology area; however, while the concept of seamless designs becomes more popular and accepted, many challenges remain in both the design and conduct of these trials. This may be especially true when seamless designs are used in late phase development supporting regulatory decision-making. The Innovative Design Scientific Working Group (IDSWG) Oncology team conducted a survey to understand the current use of seamless study designs for registration purposes in oncology clinical development. The survey was designed to provide insights into the benefits and to identify the roadblocks. A total of 16 questions were included in the survey that was distributed using the ASA Biopharmaceutical Section and IDSWG email listings from August to September 2022. A total of 51 responses were received, with 39 (76%) respondents indicating that their organizations had seamless oncology studies in planning or implementation for registration purposes. Detailed survey results are presented in the manuscript. Overall, while seamless designs offer advantages in terms of timeline reduction and cost saving, they also present challenges related to additional complexity and the need for efficient surrogate clinical endpoints in oncology drug development.
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Affiliation(s)
| | | | | | | | | | - Wei He
- AstraZeneca, Cambridge, MA, USA
| | - Lei Gao
- Moderna, Inc, Cambridge, MA, USA
| | | | - Philip He
- Daiichi Sankyo, Basking Ridge, NJ, USA
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2
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Li L, Ivanova A. Efficient testing of the biomarker positive and negative subgroups in a biomarker-stratified trial. Biometrics 2024; 80:ujae056. [PMID: 38861372 PMCID: PMC11166030 DOI: 10.1093/biomtc/ujae056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Revised: 05/01/2024] [Accepted: 05/20/2024] [Indexed: 06/13/2024]
Abstract
In many randomized placebo-controlled trials with a biomarker defined subgroup, it is believed that this subgroup has the same or higher treatment effect compared with its complement. These subgroups are often referred to as the biomarker positive and negative subgroups. Most biomarker-stratified pivotal trials are aimed at demonstrating a significant treatment effect either in the biomarker positive subgroup or in the overall population. A major shortcoming of this approach is that the treatment can be declared effective in the overall population even though it has no effect in the biomarker negative subgroup. We use the isotonic assumption about the treatment effects in the two subgroups to construct an efficient way to test for a treatment effect in both the biomarker positive and negative subgroups. A substantial reduction in the required sample size for such a trial compared with existing methods makes evaluating the treatment effect in both the biomarker positive and negative subgroups feasible in pivotal trials especially when the prevalence of the biomarker positive subgroup is less than 0.5.
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Affiliation(s)
- Lang Li
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-7420, USA
| | - Anastasia Ivanova
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-7420, USA
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3
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Luo Y, Guo X. Inference on tree-structured subgroups with subgroup size and subgroup effect relationship in clinical trials. Stat Med 2023; 42:5039-5053. [PMID: 37732390 DOI: 10.1002/sim.9900] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 08/18/2023] [Accepted: 09/01/2023] [Indexed: 09/22/2023]
Abstract
When multiple candidate subgroups are considered in clinical trials, we often need to make statistical inference on the subgroups simultaneously. Classical multiple testing procedures might not lead to an interpretable and efficient inference on the subgroups as they often fail to take subgroup size and subgroup effect relationship into account. In this paper, built on the selective traversed accumulation rules (STAR), we propose a data-adaptive and interactive multiple testing procedure for subgroups which can take subgroup size and subgroup effect relationship into account under prespecified tree structure. The proposed method is easy-to-implement and can lead to a more interpretable and efficient inference on prespecified tree-structured subgroups. Possible accommodations to post hoc identified tree-structure subgroups are also discussed in the paper. We demonstrate the merit of our proposed method by re-analyzing the panitumumab trial with the proposed method.
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Affiliation(s)
- Yuanhui Luo
- Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong, People's Republic of China
| | - Xinzhou Guo
- Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong, People's Republic of China
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4
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Frieri R, Rosenberger WF, Flournoy N, Lin Z. Design considerations for two-stage enrichment clinical trials. Biometrics 2023; 79:2565-2576. [PMID: 36435977 DOI: 10.1111/biom.13805] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Revised: 09/09/2022] [Accepted: 11/18/2022] [Indexed: 11/28/2022]
Abstract
When there is a predictive biomarker, enrichment can focus the clinical trial on a benefiting subpopulation. We describe a two-stage enrichment design, in which the first stage is designed to efficiently estimate a threshold and the second stage is a "phase III-like" trial on the enriched population. The goal of this paper is to explore design issues: sample size in Stages 1 and 2, and re-estimation of the Stage 2 sample size following Stage 1. By treating these as separate trials, we can gain insight into how the predictive nature of the biomarker specifically impacts the sample size. We also show that failure to adequately estimate the threshold can have disastrous consequences in the second stage. While any bivariate model could be used, we assume a continuous outcome and continuous biomarker, described by a bivariate normal model. The correlation coefficient between the outcome and biomarker is the key to understanding the behavior of the design, both for predictive and prognostic biomarkers. Through a series of simulations we illustrate the impact of model misspecification, consequences of poor threshold estimation, and requisite sample sizes that depend on the predictive nature of the biomarker. Such insight should be helpful in understanding and designing enrichment trials.
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Affiliation(s)
- Rosamarie Frieri
- Department of Statistical Sciences, University of Bologna, Bologna, Italy
| | | | - Nancy Flournoy
- Department of Statistics, University of Missouri, Columbia, Missouri, USA
| | - Zhantao Lin
- Department of Statistics, Data and Analytics, Eli Lilly and Company, Indianapolis, Indiana, USA
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5
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Baldi Antognini A, Frieri R, Zagoraiou M. New insights into adaptive enrichment designs. Stat Pap (Berl) 2023. [DOI: 10.1007/s00362-023-01433-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/03/2023]
Abstract
AbstractThe transition towards personalized medicine is happening and the new experimental framework is raising several challenges, from a clinical, ethical, logistical, regulatory, and statistical perspective. To face these challenges, innovative study designs with increasing complexity have been proposed. In particular, adaptive enrichment designs are becoming more attractive for their flexibility. However, these procedures rely on an increasing number of parameters that are unknown at the planning stage of the clinical trial, so the study design requires particular care. This review is dedicated to adaptive enrichment studies with a focus on design aspects. While many papers deal with methods for the analysis, the sample size determination and the optimal allocation problem have been overlooked. We discuss the multiple aspects involved in adaptive enrichment designs that contribute to their advantages and disadvantages. The decision-making process of whether or not it is worth enriching should be driven by clinical and ethical considerations as well as scientific and statistical concerns.
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6
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Stallard N. Adaptive enrichment designs with a continuous biomarker. Biometrics 2023; 79:9-19. [PMID: 35174875 DOI: 10.1111/biom.13644] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Accepted: 09/23/2021] [Indexed: 12/01/2022]
Abstract
A popular design for clinical trials assessing targeted therapies is the two-stage adaptive enrichment design with recruitment in stage 2 limited to a biomarker-defined subgroup chosen based on data from stage 1. The data-dependent selection leads to statistical challenges if data from both stages are used to draw inference on treatment effects in the selected subgroup. If subgroups considered are nested, as when defined by a continuous biomarker, treatment effect estimates in different subgroups follow the same distribution as estimates in a group-sequential trial. This result is used to obtain tests controlling the familywise type I error rate (FWER) for six simple subgroup selection rules, one of which also controls the FWER for any selection rule. Two approaches are proposed: one based on multivariate normal distributions suitable if the number of possible subgroups, k, is small, and one based on Brownian motion approximations suitable for large k. The methods, applicable in the wide range of settings with asymptotically normal test statistics, are illustrated using survival data from a breast cancer trial.
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Affiliation(s)
- Nigel Stallard
- Statistics and Epidemiology, Division of Health Sciences, Warwick Medical School, University of Warwick, Coventry, UK
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7
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Placzek M, Friede T. Blinded sample size recalculation in adaptive enrichment designs. Biom J 2023; 65:e2000345. [PMID: 35983952 DOI: 10.1002/bimj.202000345] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 09/24/2021] [Accepted: 11/07/2021] [Indexed: 12/17/2022]
Abstract
In the precision medicine era, (prespecified) subgroup analyses are an integral part of clinical trials. Incorporating multiple populations and hypotheses in the design and analysis plan, adaptive designs promise flexibility and efficiency in such trials. Adaptations include (unblinded) interim analyses (IAs) or blinded sample size reviews. An IA offers the possibility to select promising subgroups and reallocate sample size in further stages. Trials with these features are known as adaptive enrichment designs. Such complex designs comprise many nuisance parameters, such as prevalences of the subgroups and variances of the outcomes in the subgroups. Additionally, a number of design options including the timepoint of the sample size review and timepoint of the IA have to be selected. Here, for normally distributed endpoints, we propose a strategy combining blinded sample size recalculation and adaptive enrichment at an IA, that is, at an early timepoint nuisance parameters are reestimated and the sample size is adjusted while subgroup selection and enrichment is performed later. We discuss implications of different scenarios concerning the variances as well as the timepoints of blinded review and IA and investigate the design characteristics in simulations. The proposed method maintains the desired power if planning assumptions were inaccurate and reduces the sample size and variability of the final sample size when an enrichment is performed. Having two separate timepoints for blinded sample size review and IA improves the timing of the latter and increases the probability to correctly enrich a subgroup.
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Affiliation(s)
- Marius Placzek
- Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany
| | - Tim Friede
- Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany.,DZHK (German Center for Cardiovascular Research), partner site Göttingen, Göttingen, Germany
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8
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Chou R. Comparative Effectiveness of Mask Type in Preventing SARS-CoV-2 in Health Care Workers: Uncertainty Persists. Ann Intern Med 2022; 175:1763-1764. [PMID: 36442057 PMCID: PMC9707439 DOI: 10.7326/m22-3219] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
Abstract
Two and a half years after the emergence of the COVID-19 pandemic, Loeb and colleagues reported the first randomized trial of N95 respirators versus medical masks in health care workers. The editorialist discusses the findings and highlights remaining areas of uncertainty about optimal mask type.
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Affiliation(s)
- Roger Chou
- Oregon Health & Science University, Portland, Oregon
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9
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Temkin N, Machamer J, Dikmen S, Nelson LD, Barber J, Hwang PH, Boase K, Stein MB, Sun X, Giacino J, McCrea MA, Taylor SR, Jain S, Manley G. Risk Factors for High Symptom Burden Three Months after Traumatic Brain Injury and Implications for Clinical Trial Design: A Transforming Research and Clinical Knowledge in Traumatic Brain Injury Study. J Neurotrauma 2022; 39:1524-1532. [PMID: 35754333 PMCID: PMC9689769 DOI: 10.1089/neu.2022.0113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
More than 75% of patients presenting to level I trauma centers in the United States with suspicion of TBI sufficient to require a clinical computed tomography scan report injury-related symptoms 3 months later. There are currently no approved treatments, and few clinical trials have evaluated possible treatments. Efficient trials will require subject inclusion and exclusion criteria that balance cost-effective recruitment with enrolling individuals with a higher chance of benefiting from the interventions. Using data from the Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) study, we examined the relationship of 3-month symptoms to pre-injury, demographic, and acute characteristics as well as 2-week symptoms and blood-based biomarkers to identify and evaluate factors that may be used for sample enrichment for clinical trials. Many of the risk factors for TBI symptoms reported in the literature were supported, but the effect sizes of each were small or moderate (< 0.5). The only factors with large effect sizes when predicting 3-month symptom burden were TBI-related (i.e., post-concussive) and post-traumatic stress symptom levels at 2 weeks (respective effect sizes 1.13 and 1.34). TBI severity was not significantly associated with 3-month symptom burden (p = 0.37). Using simulated data to evaluate the effect of enrichment, we showed that including only people with high symptom burden at 2 weeks would permit trials to reduce the sample size by half, with minimal increase in screening, as compared with enrolling an unenriched sample. Clinical trials aimed at reducing symptoms after TBI can be efficiently conducted by enriching the included sample with people reporting a high early symptom burden.
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Affiliation(s)
- Nancy Temkin
- Department of Neurological Surgery, University of Washington, Seattle, Washington, USA
- Department of Biostatistics, University of Washington, Seattle, Washington, USA
| | - Joan Machamer
- Department of Neurological Surgery, University of Washington, Seattle, Washington, USA
| | - Sureyya Dikmen
- Department of Rehabilitation Medicine, University of Washington, Seattle, Washington, USA
| | - Lindsay D. Nelson
- Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Jason Barber
- Department of Neurological Surgery, University of Washington, Seattle, Washington, USA
| | - Phillip H. Hwang
- Department of Anatomy and Neurobiology, Boston University, Boston Massachusetts, USA
| | - Kim Boase
- Department of Neurological Surgery, University of Washington, Seattle, Washington, USA
| | - Murray B. Stein
- Department of Psychiatry and Herbert Wertheim School of Public Health, University of California, San Diego, California, USA
| | - Xiaoying Sun
- Biostatistics Research Center Herbert Wertheim School of Public Health, University of California, San Diego, California, USA
| | - Joseph Giacino
- Department of Rehabilitation Medicine, Spaulding Rehabilitation Hospital, Harvard Medical School, Charlestown, Massachusetts, USA
| | - Michael A. McCrea
- Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
- Department of Neurology, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Sabrina R. Taylor
- Brain and Spinal Injury Center, San Francisco California, USA
- Department of Neurological Surgery, University of California, San Francisco, California, USA
| | - Sonia Jain
- Biostatistics Research Center Herbert Wertheim School of Public Health, University of California, San Diego, California, USA
| | - Geoff Manley
- Brain and Spinal Injury Center, San Francisco California, USA
- Department of Neurological Surgery, University of California, San Francisco, California, USA
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10
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Wu L, Li Q, Liu M, Lin J. Incorporating Surrogate Information for Adaptive Subgroup Enrichment Design with Sample Size Re-estimation. Stat Biopharm Res 2022. [DOI: 10.1080/19466315.2022.2046150] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Affiliation(s)
- Liwen Wu
- Takeda Pharmaceuticals, 40 Landsdowne Street, Cambridge, MA, 02139, USA
| | - Qing Li
- MorphoSys US Inc., 470 Atlantic Ave 14th Floor, Boston, MA, 02210, USA
| | - Mengya Liu
- Takeda Pharmaceuticals, 40 Landsdowne Street, Cambridge, MA, 02139, USA
| | - Jianchang Lin
- Takeda Pharmaceuticals, 40 Landsdowne Street, Cambridge, MA, 02139, USA
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11
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Lin Z, Flournoy N, Rosenberger WF. Inference for a two-stage enrichment design. Ann Stat 2021. [DOI: 10.1214/21-aos2051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Affiliation(s)
- Zhantao Lin
- Department of Statistics, George Mason University
| | - Nancy Flournoy
- Department of Statistics, University of Missouri, Columbia
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12
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Edgar K, Jackson D, Rhodes K, Duffy T, Burman CF, Sharples LD. Frequentist rules for regulatory approval of subgroups in phase III trials: A fresh look at an old problem. Stat Methods Med Res 2021; 30:1725-1743. [PMID: 34077288 PMCID: PMC8411475 DOI: 10.1177/09622802211017574] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Background The number of Phase III trials that include a biomarker in design and
analysis has increased due to interest in personalised medicine. For genetic
mutations and other predictive biomarkers, the trial sample comprises two
subgroups, one of which, say B+ is known or suspected to achieve a larger treatment effect
than the other B−. Despite treatment effect heterogeneity, trials often draw
patients from both subgroups, since the lower responding B− subgroup may also gain benefit from the intervention. In
this case, regulators/commissioners must decide what constitutes sufficient
evidence to approve the drug in the B− population. Methods and Results Assuming trial analysis can be completed using generalised linear models, we
define and evaluate three frequentist decision rules for approval. For rule
one, the significance of the average treatment effect in B− should exceed a pre-defined minimum value, say
ZB−>L. For rule two, the data from the low-responding group
B− should increase statistical significance. For rule three,
the subgroup-treatment interaction should be non-significant, using type I
error chosen to ensure that estimated difference between the two subgroup
effects is acceptable. Rules are evaluated based on conditional power, given
that there is an overall significant treatment effect. We show how different
rules perform according to the distribution of patients across the two
subgroups and when analyses include additional (stratification) covariates
in the analysis, thereby conferring correlation between subgroup
effects. Conclusions When additional conditions are required for approval of a new treatment in a
lower response subgroup, easily applied rules based on minimum effect sizes
and relaxed interaction tests are available. Choice of rule is influenced by
the proportion of patients sampled from the two subgroups but less so by the
correlation between subgroup effects.
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Affiliation(s)
- K Edgar
- Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK
| | - D Jackson
- Statistical Innovation, Oncology R&D, AstraZeneca, AstraZeneca, Cambridge, UK
| | - K Rhodes
- Statistical Innovation, Oncology R&D, AstraZeneca, AstraZeneca, Cambridge, UK
| | - T Duffy
- Statistical Innovation, BioPharmaceutical R&D, AstraZeneca, Gothenburg, Sweden
| | - C-F Burman
- Wolfson Institute of Preventive Medicine, Queen Mary University of London, London, UK
| | - L D Sharples
- Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK
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13
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Natanegara F, Zariffa N, Buenconsejo J, Ran Liao, Cooner F, Lakshminarayanan D, Ghosh S, Schindler JS, Gamalo M. Statistical Opportunities to Accelerate Development for COVID-19 Therapeutics. Stat Biopharm Res 2021. [DOI: 10.1080/19466315.2020.1865195] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Fanni Natanegara
- Research and Development – Statistics, Eli Lilly and Co, Indianapolis, IN, USA
| | | | - Joan Buenconsejo
- Biometrics, Cardiovascular, Renal and Metabolism, BioPharmaceuticals R&D, AstraZeneca, Gaithersburg, MD, USA
| | - Ran Liao
- Research and Development – Statistics, Eli Lilly and Co, Indianapolis, IN, USA
| | - Freda Cooner
- Center for Design and Analysis, Amgen, Thousand Oaks, CA, USA
| | - Divya Lakshminarayanan
- Clinical Statistics, COVID-19, Biostatistics R&D, GlaxoSmithKline, Collegeville, PA, USA
| | - Samiran Ghosh
- Department of Family Medicine & Public Health Sciences and Center for Molecular Medicine and Genetics, School of Medicine, Wayne State University, Detroit, MI, USA
| | | | - Margaret Gamalo
- Research and Development – Statistics, Eli Lilly and Co, Indianapolis, IN, USA
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14
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Lin R, Yang Z, Yuan Y, Yin G. Sample size re-estimation in adaptive enrichment design. Contemp Clin Trials 2020; 100:106216. [PMID: 33246098 DOI: 10.1016/j.cct.2020.106216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Revised: 10/23/2020] [Accepted: 11/10/2020] [Indexed: 10/22/2022]
Abstract
Clinical trial participants are often heterogeneous, which is a fundamental problem in the rapidly developing field of precision medicine. Participants heterogeneity causes considerable difficulty in the current phase III trial designs. Adaptive enrichment designs provide a flexible and intuitive solution. At the interim analysis, we enrich the subgroup of trial participants who have a higher likelihood to benefit from the new treatment. However, it is critical to control the level of the test size and maintain adequate power after enrichment of certain subgroup of participants. We develop two adaptive enrichment strategies with sample size re-estimation and verify their feasibility and practicability through extensive simulations and sensitivity analyses. The simulation studies show that the proposed methods can control the overall type I error rate and exhibit competitive improvement in terms of statistical power and expected sample size. The proposed designs are exemplified with a real trial application.
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Affiliation(s)
- Ruitao Lin
- Department of Biostatistics, The University of Texas M. D. Anderson Cancer Center, Houston, TX 77030, USA
| | - Zhao Yang
- Department of Statistics and Actuarial Science, The University of Hong Kong, Pokfulam Road, Hong Kong, China
| | - Ying Yuan
- Department of Biostatistics, The University of Texas M. D. Anderson Cancer Center, Houston, TX 77030, USA
| | - Guosheng Yin
- Department of Statistics and Actuarial Science, The University of Hong Kong, Pokfulam Road, Hong Kong, China.
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15
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Kunz CU, Jörgens S, Bretz F, Stallard N, Van Lancker K, Xi D, Zohar S, Gerlinger C, Friede T. Clinical Trials Impacted by the COVID-19 Pandemic: Adaptive Designs to the Rescue? Stat Biopharm Res 2020; 12:461-477. [PMID: 34191979 PMCID: PMC8011492 DOI: 10.1080/19466315.2020.1799857] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Revised: 07/17/2020] [Accepted: 07/18/2020] [Indexed: 01/09/2023]
Abstract
Very recently the new pathogen severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was identified and the coronavirus disease 2019 (COVID-19) declared a pandemic by the World Health Organization. The pandemic has a number of consequences for ongoing clinical trials in non-COVID-19 conditions. Motivated by four current clinical trials in a variety of disease areas we illustrate the challenges faced by the pandemic and sketch out possible solutions including adaptive designs. Guidance is provided on (i) where blinded adaptations can help; (ii) how to achieve Type I error rate control, if required; (iii) how to deal with potential treatment effect heterogeneity; (iv) how to use early read-outs; and (v) how to use Bayesian techniques. In more detail approaches to resizing a trial affected by the pandemic are developed including considerations to stop a trial early, the use of group-sequential designs or sample size adjustment. All methods considered are implemented in a freely available R shiny app. Furthermore, regulatory and operational issues including the role of data monitoring committees are discussed.
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Affiliation(s)
| | | | - Frank Bretz
- Novartis Pharma AG, Basel, Switzerland
- Section for Medical Statistics, Medical University of Vienna, Vienna, Austria
| | - Nigel Stallard
- Division of Health Sciences, Warwick Medical School, The University of Warwick, Coventry, UK
| | - Kelly Van Lancker
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium
| | - Dong Xi
- Novartis Pharmaceuticals, East Hanover, NJ
| | - Sarah Zohar
- INSERM, Centre de Recherche des Cordeliers, Sorbonne Université, Université de Paris, Paris, France
| | - Christoph Gerlinger
- Statistics and Data Insights, Bayer AG, Berlin, Germany
- Department of Gynecology, Obstetrics and Reproductive Medicine, University Medical School of Saarland, Homburg/Saar, Germany
| | - Tim Friede
- Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany
- DZHK (German Center for Cardiovascular Research), Partner Site Göttingen, Göttingen, Germany
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16
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Rosenblum M, Fang EX, Liu H. Optimal, two-stage, adaptive enrichment designs for randomized trials, using sparse linear programming. J R Stat Soc Series B Stat Methodol 2020. [DOI: 10.1111/rssb.12366] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Affiliation(s)
| | | | - Han Liu
- Northwestern University; Evanston USA
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17
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Friede T, Stallard N, Parsons N. Adaptive seamless clinical trials using early outcomes for treatment or subgroup selection: Methods, simulation model and their implementation in R. Biom J 2020; 62:1264-1283. [PMID: 32118317 PMCID: PMC8614126 DOI: 10.1002/bimj.201900020] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2019] [Revised: 01/10/2020] [Accepted: 01/12/2020] [Indexed: 11/12/2022]
Abstract
Adaptive seamless designs combine confirmatory testing, a domain of phase III trials, with features such as treatment or subgroup selection, typically associated with phase II trials. They promise to increase the efficiency of development programmes of new drugs, for example, in terms of sample size and/or development time. It is well acknowledged that adaptive designs are more involved from a logistical perspective and require more upfront planning, often in the form of extensive simulation studies, than conventional approaches. Here, we present a framework for adaptive treatment and subgroup selection using the same notation, which links the somewhat disparate literature on treatment selection on one side and on subgroup selection on the other. Furthermore, we introduce a flexible and efficient simulation model that serves both designs. As primary endpoints often take a long time to observe, interim analyses are frequently informed by early outcomes. Therefore, all methods presented accommodate interim analyses informed by either the primary outcome or an early outcome. The R package asd, previously developed to simulate designs with treatment selection, was extended to include subgroup selection (so‐called adaptive enrichment designs). Here, we describe the functionality of the R package asd and use it to present some worked‐up examples motivated by clinical trials in chronic obstructive pulmonary disease and oncology. The examples both illustrate various features of the R package and provide insights into the operating characteristics of adaptive seamless studies.
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Affiliation(s)
- Tim Friede
- Department of Medical StatisticsUniversity Medical Center GöttingenGöttingen Germany
| | - Nigel Stallard
- Division of Health SciencesWarwick Medical SchoolUniversity of WarwickCoventry UK
| | - Nicholas Parsons
- Division of Health SciencesWarwick Medical SchoolUniversity of WarwickCoventry UK
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18
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Bhattacharyya A, Rai SN. Adaptive Signature Design- review of the biomarker guided adaptive phase -III controlled design. Contemp Clin Trials Commun 2019; 15:100378. [PMID: 31289760 PMCID: PMC6591770 DOI: 10.1016/j.conctc.2019.100378] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2018] [Revised: 04/26/2019] [Accepted: 05/15/2019] [Indexed: 11/16/2022] Open
Abstract
Genomics having a profound impact on oncology drug development necessitates the use of genomic signatures for therapeutic strategy and emerging medicine proposals. Since its advent in the arena of clinical trials biomarker-related predictive methods for the identification and selection of patient subgroups, with optimal treatment response, are widely used. Genetic signatures which are accountable for the differential response to treatments are experimentally recognizable and analytically validated in phase II stage of clinical trials. The availability of robust and validated biomarkers in phase III is limited. Hence, the development of a clinical trial design without the availability of biomarker identity for treatment-sensitive patients becomes indispensable. Adaptive Signature Design (ASD) is a design procedure of developing and validating a predictive classifier (diagnostic testing strategy) when the signature of subjects responding differentially to treatment is remote in the context of the study. This review provides a detailed methodology and statistical background of this pioneering design developed by Freidlin and Simon (2005). In addition, it concentrates on the advances in ASD regarding statistical issues such as predictive assay identification, classification techniques, statistical methods, subgroup search, choice of differentially expressed genes, and multiplicity correction. The statistical methodology behind the design is explained with the intent of building the ground steps for future research approachable, especially for beginning researchers. Most of the existing research articles give a microcosmic view of the design and lack in describing the details behind the methodology. This study covers those details and marks the novelty of our research.
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Affiliation(s)
- Arinjita Bhattacharyya
- Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, KY, USA
| | - Shesh N. Rai
- Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, KY, USA
- Biostatistics and Bioinformatics Facility, JG Brown Cancer Center, University of Louisville, Louisville, KY, USA
- The Christina Lee Brown Envirome Institute, University of Louisville, Louisville, KY, USA
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19
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Placzek M, Friede T. A conditional error function approach for adaptive enrichment designs with continuous endpoints. Stat Med 2019; 38:3105-3122. [PMID: 31066093 DOI: 10.1002/sim.8154] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2018] [Revised: 02/22/2019] [Accepted: 03/09/2019] [Indexed: 12/15/2022]
Abstract
Adaptive enrichment designs offer an efficient and flexible way to demonstrate the efficacy of a treatment in a clinically defined full population or in, eg, biomarker-defined subpopulations while controlling the family-wise Type I error rate in the strong sense. Frequently used testing strategies in designs with two or more stages include the combination test and the conditional error function approach. Here, we focus on the latter and present some extensions. In contrast to previous work, we allow for multiple subgroups rather than one subgroup only. For nested as well as nonoverlapping subgroups with normally distributed endpoints, we explore the effect of estimating the variances in the subpopulations. Instead of using a normal approximation, we derive new t-distribution-based methods for two different scenarios. First, in the case of equal variances across the subpopulations, we present exact results using a multivariate t-distribution. Second, in the case of potentially varying variances across subgroups, we provide some improved approximations compared to the normal approximation. The performance of the proposed conditional error function approaches is assessed and compared to the combination test in a simulation study. The proposed methods are motivated by an example in pulmonary arterial hypertension.
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Affiliation(s)
- Marius Placzek
- Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany
| | - Tim Friede
- Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany.,DZHK (German Center for Cardiovascular Research), Partner Site Göttingen, Göttingen, Germany
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20
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Stallard N, Todd S, Parashar D, Kimani PK, Renfro LA. On the need to adjust for multiplicity in confirmatory clinical trials with master protocols. Ann Oncol 2019; 30:506-509. [PMID: 30715156 PMCID: PMC6503623 DOI: 10.1093/annonc/mdz038] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Affiliation(s)
- N Stallard
- Statistics and Epidemiology, Warwick Medical School, University of Warwick, Coventry.
| | - S Todd
- Department of Mathematics and Statistics, University of Reading, Reading
| | - D Parashar
- Statistics and Epidemiology, Warwick Medical School, University of Warwick, Coventry; The Alan Turing Institute, London; Warwick Cancer Research Centre, University of Warwick, Coventry, UK
| | - P K Kimani
- Statistics and Epidemiology, Warwick Medical School, University of Warwick, Coventry
| | - L A Renfro
- Division of Biostatistics, University of Southern California, Los Angeles, USA
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21
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Jörgens S, Wassmer G, König F, Posch M. Nested combination tests with a time-to-event endpoint using a short-term endpoint for design adaptations. Pharm Stat 2019; 18:329-350. [PMID: 30652401 DOI: 10.1002/pst.1926] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2017] [Revised: 10/16/2018] [Accepted: 12/14/2018] [Indexed: 12/11/2022]
Abstract
Adaptive trial methodology for multiarmed trials and enrichment designs has been extensively discussed in the past. A general principle to construct test procedures that control the family-wise Type I error rate in the strong sense is based on combination tests within a closed test. Using survival data, a problem arises when using information of patients for adaptive decision making, which are under risk at interim. With the currently available testing procedures, either no testing of hypotheses in interim analyses is possible or there are restrictions on the interim data that can be used in the adaptation decisions as, essentially, only the interim test statistics of the primary endpoint may be used. We propose a general adaptive testing procedure, covering multiarmed and enrichment designs, which does not have these restrictions. An important application are clinical trials, where short-term surrogate endpoints are used as basis for trial adaptations, and we illustrate how such trials can be designed. We propose statistical models to assess the impact of effect sizes, the correlation structure between the short-term and the primary endpoint, the sample size, the timing of interim analyses, and the selection rule on the operating characteristics.
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Affiliation(s)
- Silke Jörgens
- Innovation Center, ICON Clinical Research Inc, Cologne, Germany
| | - Gernot Wassmer
- Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria
| | - Franz König
- Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria
| | - Martin Posch
- Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria
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22
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Collignon O, Koenig F, Koch A, Hemmings RJ, Pétavy F, Saint-Raymond A, Papaluca-Amati M, Posch M. Adaptive designs in clinical trials: from scientific advice to marketing authorisation to the European Medicine Agency. Trials 2018; 19:642. [PMID: 30454061 PMCID: PMC6245528 DOI: 10.1186/s13063-018-3012-x] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2018] [Accepted: 10/21/2018] [Indexed: 12/15/2022] Open
Abstract
Background In recent years, experience on the application of adaptive designs in confirmatory clinical trials has accumulated. Although planning such trials comes at the cost of additional operational complexity, adaptive designs offer the benefit of flexibility to update trial design and objectives as data accrue. In 2007, the European Medicines Agency (EMA) provided guidance on confirmatory clinical trials with adaptive (or flexible) designs. In order to better understand how adaptive trials are implemented in practice and how they may impact medicine approval within the EMA centralised procedure, we followed on 59 medicines for which an adaptive clinical trial had been submitted to the EMA Scientific Advice (SA) and analysed previously in a dedicated EMA survey of scientific advice letters. We scrutinized in particular the submission of the corresponding medicines for a marketing authorisation application (MAA). We also discuss the current regulatory perspective as regards the implementation of adaptive designs in confirmatory clinical trials. Methods Using the internal EMA MAA database, the AdisInsight database and related trial registries, we analysed how many of these 59 trials actually started, the completion status, results, the time to trial start, the adaptive elements finally implemented after SA, their possible influence on the success of the trial and corresponding product approval. Results Overall 31 trials out of 59 (53%) were retrieved. Thirty of them (97%) have been started and 23 (74%) concluded. Nine of these trials (39% out of 23) demonstrated a significant treatment effect on their primary endpoint and 4 (17% out of 23) supported a marketing authorisation (MA). An additional two trials were stopped using pre-defined criteria for futility, efficiently identifying trials on which further resources should not be spent. Median time to trial start after SA letter was given by EMA was 5 months. In the investigated trial registries, at least 18 trial (58% of 31 retrieved trials) designs were implemented with adaptive elements, which were predominantly dose selection, sample size reassessment (SSR) and stopping for futility (SFF). Among the 11 completed trials including adaptive elements, 6 demonstrated a significant treatment effect on their primary endpoint (55%). Conclusions Adaptive designs are now well established in the drug development landscape. If properly pre-planned, adaptations can play a key role in the success of some of these trials, for example to help successfully select the most promising dose regimens for phase II/III trials. Interim analyses can also enable stopping of trials for futility when they do not hold their promises. Type I error rate control, trial integrity and results consistency between the different stages of the analyses are fundamental aspects to be discussed thoroughly. Engaging early dialogue with regulators and implementing the scientific advice received is strongly recommended, since much experience in discussing adaptive designs and assessing their results has been accumulated.
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Affiliation(s)
- Olivier Collignon
- European Medicines Agency, 30 Churchill Place, London, E14 5EU, UK. .,Competence Center for Methodology and Statistics, Luxembourg Institute of Health, 1A-B, rue Thomas Edison, L-1445, Strassen, Luxembourg.
| | - Franz Koenig
- Section for Medical Statistics, Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Spitalgasse 23, 1090, Vienna, Austria
| | - Armin Koch
- Institut für Biometrie, Medizinische Hochschule Hannover, OE 8410, 30625, Hanover, Germany
| | - Robert James Hemmings
- Medicines and Healthcare Products Regulatory Agency, 151 Buckingham Palace Road, London, SW1W 9SZ, UK
| | - Frank Pétavy
- European Medicines Agency, 30 Churchill Place, London, E14 5EU, UK
| | | | | | - Martin Posch
- Section for Medical Statistics, Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Spitalgasse 23, 1090, Vienna, Austria
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23
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Vo TT, Vivot A, Porcher R. Impact of Biomarker-based Design Strategies on the Risk of False-Positive Findings in Targeted Therapy Evaluation. Clin Cancer Res 2018; 24:6257-6264. [PMID: 30166443 DOI: 10.1158/1078-0432.ccr-18-0328] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2018] [Revised: 05/24/2018] [Accepted: 08/27/2018] [Indexed: 11/16/2022]
Abstract
PURPOSE When there is more than one potentially predictive biomarker for a new drug, the drug is often evaluated in different subpopulations defined by different biomarkers. We aim to (i) estimate the risk of false-positive findings with this approach and (ii) evaluate the cross-validated adaptive signature design (CVASD) as a potential alternative. EXPERIMENTAL DESIGN By using numerically simulated data, we compare the current approach and the CVASD across different settings and scenarios. We consider three strategies for CVASD. The first two CVASD strategies are different in terms of the partitioning of the overall significance level (between the population test and the subgroup test). In the third CVASD strategy, the order of the two tests is reversed, that is, the population test is realized when the prioritized subgroup test is not statistically significant. RESULTS The current approach results in a high risk of false-positive findings, whereas this risk is close to the nominal level of 5% once applying the CVASD, regardless of the strategy. When the treatment is equally effective to all patients, only the CVASD strategies could specify correctly the absence of a sensitive subgroup. When the treatment is only effective for some sensitive responders, the third CVASD strategy stands out by its ability to correctly identify the predictive biomarker(s). CONCLUSIONS The drug-biomarker coevaluation based on a series of independent enrichment trials can result in a high risk of false-positive findings. CVASD with some appropriate adjustments can be a good alternative to overcome this multiplicity issue.
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Affiliation(s)
- Tat-Thang Vo
- INSERM, UMR1153 Epidemiology and Statistics Sorbonne Paris Cité Research Center (CRESS), METHODS Team, Paris Descartes University, Paris, France.,Department of Applied Mathematics, Computer Science & Statistics, Faculty of Science, Ghent University, Ghent, Belgium
| | - Alexandre Vivot
- INSERM, UMR1153 Epidemiology and Statistics Sorbonne Paris Cité Research Center (CRESS), METHODS Team, Paris Descartes University, Paris, France. .,Assistance Publique des Hôpitaux de Paris (AP-HP), Hôpital Hôtel Dieu, Centre d'Épidémiologie Clinique, Paris, France
| | - Raphaël Porcher
- INSERM, UMR1153 Epidemiology and Statistics Sorbonne Paris Cité Research Center (CRESS), METHODS Team, Paris Descartes University, Paris, France.,Assistance Publique des Hôpitaux de Paris (AP-HP), Hôpital Hôtel Dieu, Centre d'Épidémiologie Clinique, Paris, France
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24
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Chiu YD, Koenig F, Posch M, Jaki T. Design and estimation in clinical trials with subpopulation selection. Stat Med 2018; 37:4335-4352. [PMID: 30088280 PMCID: PMC6282861 DOI: 10.1002/sim.7925] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2017] [Revised: 05/23/2018] [Accepted: 07/06/2018] [Indexed: 11/10/2022]
Abstract
Population heterogeneity is frequently observed among patients' treatment responses in clinical trials because of various factors such as clinical background, environmental, and genetic factors. Different subpopulations defined by those baseline factors can lead to differences in the benefit or safety profile of a therapeutic intervention. Ignoring heterogeneity between subpopulations can substantially impact on medical practice. One approach to address heterogeneity necessitates designs and analysis of clinical trials with subpopulation selection. Several types of designs have been proposed for different circumstances. In this work, we discuss a class of designs that allow selection of a predefined subgroup. Using the selection based on the maximum test statistics as the worst‐case scenario, we then investigate the precision and accuracy of the maximum likelihood estimator at the end of the study via simulations. We find that the required sample size is chiefly determined by the subgroup prevalence and show in simulations that the maximum likelihood estimator for these designs can be substantially biased.
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Affiliation(s)
- Yi-Da Chiu
- Medical and Pharmaceutical Statistics Research Unit, Department of Mathematics and Statistics, Lancaster University, Lancashire, UK
| | - Franz Koenig
- Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria
| | - Martin Posch
- Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria
| | - Thomas Jaki
- Medical and Pharmaceutical Statistics Research Unit, Department of Mathematics and Statistics, Lancaster University, Lancashire, UK
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25
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Ondra T, Jobjörnsson S, Beckman RA, Burman CF, König F, Stallard N, Posch M. Optimized adaptive enrichment designs. Stat Methods Med Res 2017; 28:2096-2111. [PMID: 29254436 PMCID: PMC6613177 DOI: 10.1177/0962280217747312] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
Based on a Bayesian decision theoretic approach, we optimize frequentist single-
and adaptive two-stage trial designs for the development of targeted therapies,
where in addition to an overall population, a pre-defined subgroup is
investigated. In such settings, the losses and gains of decisions can be
quantified by utility functions that account for the preferences of different
stakeholders. In particular, we optimize expected utilities from the
perspectives both of a commercial sponsor, maximizing the net present value, and
also of the society, maximizing cost-adjusted expected health benefits of a new
treatment for a specific population. We consider single-stage and adaptive
two-stage designs with partial enrichment, where the proportion of patients
recruited from the subgroup is a design parameter. For the adaptive designs, we
use a dynamic programming approach to derive optimal adaptation rules. The
proposed designs are compared to trials which are non-enriched (i.e. the
proportion of patients in the subgroup corresponds to the prevalence in the
underlying population). We show that partial enrichment designs can
substantially improve the expected utilities. Furthermore, adaptive partial
enrichment designs are more robust than single-stage designs and retain high
expected utilities even if the expected utilities are evaluated under a
different prior than the one used in the optimization. In addition, we find that
trials optimized for the sponsor utility function have smaller sample sizes
compared to trials optimized under the societal view and may include the overall
population (with patients from the complement of the subgroup) even if there is
substantial evidence that the therapy is only effective in the subgroup.
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Affiliation(s)
- Thomas Ondra
- 1 Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria
| | | | - Robert A Beckman
- 3 Departments of Oncology and of Biostatistics, Bioinformatics, and Biomathematics, Georgetown University Medical Center, Washington, DC, USA
| | - Carl-Fredrik Burman
- 2 Department of Mathematics, Chalmers University, Gothenburg, Sweden.,4 Statistical Innovation, AstraZeneca R&D, Molndal, Sweden
| | - Franz König
- 1 Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria
| | - Nigel Stallard
- 5 Warwick Medical School, The University of Warwick, Coventry, UK
| | - Martin Posch
- 1 Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria
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26
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Sensitivity of adaptive enrichment trial designs to accrual rates, time to outcome measurement, and prognostic variables. Contemp Clin Trials Commun 2017; 8:39-48. [PMID: 29696195 PMCID: PMC5898543 DOI: 10.1016/j.conctc.2017.08.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2016] [Revised: 04/19/2017] [Accepted: 08/11/2017] [Indexed: 11/21/2022] Open
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27
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Abstract
Subgroup analyses in clinical trials are becoming increasingly important. In cancer research, more and more targeted therapies are explored from which probably only a portion of the whole population will benefit. An adaptive design for subgroup selection with identification of a subgroup, the adaptive signature design, was proposed in the literature. Unfortunately, measuring and validating the variables defining the subgroup (i.e. biomarkers) can be extremely expensive. For this reason, we propose an extension of this design where subgroup analysis is not performed when the overall results suggest that it is unlikely to achieve statistical significance in the subgroup. Avoiding measuring and validating expensive biomarkers in this case can save resources that could be used on more promising research.
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28
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Hamasaki T, Evans SR, Asakura K. Design, data monitoring, and analysis of clinical trials with co-primary endpoints: A review. J Biopharm Stat 2017; 28:28-51. [PMID: 29083951 DOI: 10.1080/10543406.2017.1378668] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
We review the design, data monitoring, and analyses of clinical trials with co-primary endpoints. Recently developed methods for fixed-sample and group-sequential settings are described. Practical considerations are discussed, and guidance for the application of these methods is provided.
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Affiliation(s)
- Toshimitsu Hamasaki
- a Department of Data Science , National Cerebral and Cardiovascular Center , Osaka , Japan.,b Department of Innovative Clinical Trials and Data Science , Osaka University Graduate School of Medicine , Osaka , Japan
| | - Scott R Evans
- c Department of Biostatistics and the Center for Biostatistics in AIDS Research , Harvard T.H. Chan School of Public Heath , MA , USA
| | - Koko Asakura
- a Department of Data Science , National Cerebral and Cardiovascular Center , Osaka , Japan.,b Department of Innovative Clinical Trials and Data Science , Osaka University Graduate School of Medicine , Osaka , Japan
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29
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Su SC, Li X, Zhao Y, Chan ISF. Population-Enrichment Adaptive Design Strategy for an Event-Driven Vaccine Efficacy Trial. STATISTICS IN BIOSCIENCES 2017. [DOI: 10.1007/s12561-017-9202-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
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30
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Matsui S, Crowley J. Biomarker-Stratified Phase III Clinical Trials: Enhancement with a Subgroup-Focused Sequential Design. Clin Cancer Res 2017; 24:994-1001. [PMID: 28887317 DOI: 10.1158/1078-0432.ccr-17-1552] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2017] [Revised: 07/24/2017] [Accepted: 09/05/2017] [Indexed: 11/16/2022]
Abstract
Among various design approaches to phase III clinical trials with a predictive biomarker, the marker-stratified all-comers design is advantageous because it allows for establishing the utility of both treatment and biomarker, but it is often criticized for requiring large sample sizes, as the design includes both marker-positive and marker-negative patients. In this article, we propose a simple but flexible subgroup-focused design for marker-stratified trials that allow both sequential assessment across marker-defined subgroups and adaptive subgroup selection while retaining an assessment using the entire patient cohort at the final analysis stage, possibly using established marker-based multiple testing procedures. Numerical evaluations indicate that the proposed marker-stratified design has a robustness property in preserving statistical power for detecting various profiles of treatment effects across the subgroups while effectively reducing the number of randomized patients in the marker-negative subgroup with presumably limited treatment efficacy. In contrast, the traditional all-comers and sequential enrichment designs could suffer from low statistical power for some possible profiles of treatment effects. The latter also needs long study durations and a large number of marker-screened patients. We also provide an application to SWOG S0819, a trial to assess the role of cetuximab in treating non-small cell lung cancers. These evaluations indicate that the proposed subgroup-focused approach can enhance the efficiency of the marker-stratified design for definitive evaluation of treatment and biomarker in phase III clinical trials. Clin Cancer Res; 24(5); 994-1001. ©2017 AACR.
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Affiliation(s)
- Shigeyuki Matsui
- Department of Biostatistics, Nagoya University Graduate School of Medicine, Nagoya, Japan.
| | - John Crowley
- Cancer Research And Biostatistics, Seattle, Washington
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31
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Sun H, Bretz F, Gerke O, Vach W. Comparing a stratified treatment strategy with the standard treatment in randomized clinical trials. Stat Med 2016; 35:5325-5337. [PMID: 27666738 DOI: 10.1002/sim.7091] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2016] [Revised: 08/08/2016] [Accepted: 08/10/2016] [Indexed: 11/07/2022]
Abstract
The increasing emergence of predictive markers for different treatments in the same patient population allows us to define stratified treatment strategies. We consider randomized clinical trials that compare a standard treatment with a new stratified treatment strategy that divides the study population into subgroups receiving different treatments. Because the new strategy may not be beneficial in all subgroups, we consider in this paper an intermediate approach that establishes a treatment effect in a subset of patients built by joining several subgroups. The approach is based on the simple idea of selecting the subset with minimal p-value when testing the subset-specific treatment effects. We present a framework to compare this approach with other approaches to select subsets by introducing three performance measures. The results of a comprehensive simulation study are presented, and the relative merits of the various approaches are discussed. Copyright © 2016 John Wiley & Sons, Ltd.
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Affiliation(s)
- Hong Sun
- Clinical Epidemiology, Institute for Medical Biometry and Statistics, Faculty of Medicine, Medical Center - University of Freiburg, Germany
| | | | - Oke Gerke
- Nuclear Medicine, Odense University Hospital, Denmark
| | - Werner Vach
- Clinical Epidemiology, Institute for Medical Biometry and Statistics, Faculty of Medicine, Medical Center - University of Freiburg, Germany
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32
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Dmitrienko A, Muysers C, Fritsch A, Lipkovich I. General guidance on exploratory and confirmatory subgroup analysis in late-stage clinical trials. J Biopharm Stat 2016; 26:71-98. [PMID: 26366479 DOI: 10.1080/10543406.2015.1092033] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
This article focuses on a broad class of statistical and clinical considerations related to the assessment of treatment effects across patient subgroups in late-stage clinical trials. This article begins with a comprehensive review of clinical trial literature and regulatory guidelines to help define scientifically sound approaches to evaluating subgroup effects in clinical trials. All commonly used types of subgroup analysis are considered in the article, including different variations of prospectively defined and post-hoc subgroup investigations. In the context of confirmatory subgroup analysis, key design and analysis options are presented, which includes conventional and innovative trial designs that support multi-population tailoring approaches. A detailed summary of exploratory subgroup analysis (with the purpose of either consistency assessment or subgroup identification) is also provided. The article promotes a more disciplined approach to post-hoc subgroup identification and formulates key principles that support reliable evaluation of subgroup effects in this setting.
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Affiliation(s)
- Alex Dmitrienko
- a Center for Statistics in Drug Development, Quintiles , Overland Park , Kansas , USA
| | | | - Arno Fritsch
- c Clinical Statistics , Bayer HealthCare , Wuppertal , Germany
| | - Ilya Lipkovich
- a Center for Statistics in Drug Development, Quintiles , Overland Park , Kansas , USA
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33
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Zaïr ZM, Singer DR. Influx transporter variants as predictors of cancer chemotherapy-induced toxicity: systematic review and meta-analysis. Pharmacogenomics 2016; 17:1189-1205. [PMID: 27380948 DOI: 10.2217/pgs-2015-0005] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
AIM Chemotherapeutic agents have been shown to increase lung patient survival, however their use may be limited by their serious adverse effects. We aimed to assess int impact of pharmacogenetic variation of influx transporters on inter-individual patient variation in adverse drug reactions. PATIENTS & METHODS We conducted a meta-analysis and systemic review and identified 16 publications, totaling 1510 patients, to be eligible for review. RESULTS Meta-analysis showed east-Asian patients expressing SLCO1B1 521T>C or 1118G>A to have a two- to fourfold increased risk of irinotecan-induced neutropenia but not diarrhea. American patients, expressing SLC19A1 IVS2(4935) G>A, were further associated with pemetrexed/gemcitabine-induced grade 3+ leukopenia. CONCLUSION Future studies should look to robust validation of SLCO1B1 and SLC19A1 as prognostic markers in the management of lung cancer patients.
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Affiliation(s)
| | - Donald Rj Singer
- Yale University School of Medicine, New Haven, CT, USA.,Fellowship of Postgraduate Medicine 11 Chandos Street, London, UK
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34
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Hébert JR, Frongillo EA, Adams SA, Turner-McGrievy GM, Hurley TG, Miller DR, Ockene IS. Perspective: Randomized Controlled Trials Are Not a Panacea for Diet-Related Research. Adv Nutr 2016; 7:423-32. [PMID: 27184269 PMCID: PMC4863268 DOI: 10.3945/an.115.011023] [Citation(s) in RCA: 71] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
Research into the role of diet in health faces a number of methodologic challenges in the choice of study design, measurement methods, and analytic options. Heavier reliance on randomized controlled trial (RCT) designs is suggested as a way to solve these challenges. We present and discuss 7 inherent and practical considerations with special relevance to RCTs designed to study diet: 1) the need for narrow focus; 2) the choice of subjects and exposures; 3) blinding of the intervention; 4) perceived asymmetry of treatment in relation to need; 5) temporal relations between dietary exposures and putative outcomes; 6) strict adherence to the intervention protocol, despite potential clinical counter-indications; and 7) the need to maintain methodologic rigor, including measuring diet carefully and frequently. Alternatives, including observational studies and adaptive intervention designs, are presented and discussed. Given high noise-to-signal ratios interjected by using inaccurate assessment methods in studies with weak or inappropriate study designs (including RCTs), it is conceivable and indeed likely that effects of diet are underestimated. No matter which designs are used, studies will require continued improvement in the assessment of dietary intake. As technology continues to improve, there is potential for enhanced accuracy and reduced user burden of dietary assessments that are applicable to a wide variety of study designs, including RCTs.
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Affiliation(s)
- James R Hébert
- Cancer Prevention and Control Program, Departments of Epidemiology and Biostatistics, and
| | - Edward A Frongillo
- Health Promotion, Education and Behavior, Arnold School of Public Health
| | - Swann A Adams
- Cancer Prevention and Control Program, Departments of Epidemiology and Biostatistics, and College of Nursing, University of South Carolina, Columbia, SC
| | | | | | - Donald R Miller
- Department of Health Policy and Management, Boston University School of Public Health, Boston, MA; Center for Healthcare Organization and Implementation Research, Bedford Veterans Administration Medical Center, Bedford, MA; and
| | - Ira S Ockene
- Division of Cardiovascular Medicine, Department of Medicine, University of Massachusetts Medical School, Worcester, MA
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Rosenblum M, Luber B, Thompson RE, Hanley D. Group sequential designs with prospectively planned rules for subpopulation enrichment. Stat Med 2016; 35:3776-91. [PMID: 27076411 DOI: 10.1002/sim.6957] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2015] [Revised: 02/23/2016] [Accepted: 03/06/2016] [Indexed: 11/11/2022]
Abstract
We propose a class of randomized trial designs aimed at gaining the advantages of wider generalizability and faster recruitment while mitigating the risks of including a population for which there is greater a priori uncertainty. We focus on testing null hypotheses for the overall population and a predefined subpopulation. Our designs have preplanned rules for modifying enrollment criteria based on data accrued at interim analyses. For example, enrollment can be restricted if the participants from a predefined subpopulation are not benefiting from the new treatment. Our designs have the following features: the multiple testing procedure fully leverages the correlation among statistics for different populations; the asymptotic familywise Type I error rate is strongly controlled; for outcomes that are binary or normally distributed, the decision rule and multiple testing procedure are functions of the data only through minimal sufficient statistics. Our designs incorporate standard group sequential boundaries for each population of interest; this may be helpful in communicating the designs, because many clinical investigators are familiar with such boundaries, which can be summarized succinctly in a single table or graph. We demonstrate these designs through simulations of a Phase III trial of a new treatment for stroke. User-friendly, free software implementing these designs is described. Copyright © 2016 John Wiley & Sons, Ltd.
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Affiliation(s)
- Michael Rosenblum
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, U.S.A
| | - Brandon Luber
- Division of Biostatistics and Bioinformatics, Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD, U.S.A
| | - Richard E Thompson
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, U.S.A
| | - Daniel Hanley
- Division of Biostatistics and Bioinformatics, Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD, U.S.A
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Wassmer G, Dragalin V. Designing Issues in Confirmatory Adaptive Population Enrichment Trials. J Biopharm Stat 2016; 25:651-69. [PMID: 24905739 DOI: 10.1080/10543406.2014.920869] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Abstract
Adaptive population enrichment designs enable the data-driven selection of one or more pre-specified subpopulations in an interim analysis, and the confirmatory proof of efficacy in the selected subset at the end of the trial. Sample size reassessment and other adaptive design changes can be performed as well. Strong control of the experimentwise Type I error rate is guaranteed by use of the combination testing principle together with the closed testing argument. In this paper the general methodology and designing issues when planning such a design are reviewed. It is shown how to derive overall confidence intervals and p-values. Criteria for assessing the operating characteristics of these designs are given, and the application is illustrated by examples.
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Uozumi R, Hamada C. Interim decision-making strategies in adaptive designs for population selection using time-to-event endpoints. J Biopharm Stat 2016; 27:84-100. [PMID: 26881477 DOI: 10.1080/10543406.2016.1148714] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
Adaptive designs in oncology clinical trials with interim analyses for population selection could be used in the development of targeted therapies if a predefined biomarker hypothesis exists. In this article, we consider an interim analysis using overall survival (OS), progression-free survival (PFS), and both OS and PFS, to determine whether the whole population or only the biomarker-positive population should continue into the subsequent stage of the trial, whereas the final decision is made based on OS data only. In order to increase the probability of selecting the most appropriate population at the interim analysis, we propose an interim decision-making strategy in adaptive designs with correlated endpoints considering the post-progression survival (PPS) magnitudes. In our approach, the interim decision is made on the basis of predictive power by incorporating information on OS as well as PFS to supplement the incomplete OS data. Simulation studies assuming a targeted therapy demonstrated that our interim decision-making procedure performs well in terms of selecting the proper population, especially under a scenario in which PPS affects the correlation between OS and PFS.
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Affiliation(s)
- Ryuji Uozumi
- a Department of Biomedicai Statistics and Bioinformatics , Kyoto University Graduate School of Medicine , Kyoto , Japan
| | - Chikuma Hamada
- b Department of Management Science, Graduate School of Engineering , Tokyo University of Science , Tokyo , Japan
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Bauer P, Bretz F, Dragalin V, König F, Wassmer G. Twenty-five years of confirmatory adaptive designs: opportunities and pitfalls. Stat Med 2016; 35:325-47. [PMID: 25778935 PMCID: PMC6680191 DOI: 10.1002/sim.6472] [Citation(s) in RCA: 130] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2014] [Revised: 02/03/2015] [Accepted: 02/19/2015] [Indexed: 12/26/2022]
Abstract
'Multistage testing with adaptive designs' was the title of an article by Peter Bauer that appeared 1989 in the German journal Biometrie und Informatik in Medizin und Biologie. The journal does not exist anymore but the methodology found widespread interest in the scientific community over the past 25 years. The use of such multistage adaptive designs raised many controversial discussions from the beginning on, especially after the publication by Bauer and Köhne 1994 in Biometrics: Broad enthusiasm about potential applications of such designs faced critical positions regarding their statistical efficiency. Despite, or possibly because of, this controversy, the methodology and its areas of applications grew steadily over the years, with significant contributions from statisticians working in academia, industry and agencies around the world. In the meantime, such type of adaptive designs have become the subject of two major regulatory guidance documents in the US and Europe and the field is still evolving. Developments are particularly noteworthy in the most important applications of adaptive designs, including sample size reassessment, treatment selection procedures, and population enrichment designs. In this article, we summarize the developments over the past 25 years from different perspectives. We provide a historical overview of the early days, review the key methodological concepts and summarize regulatory and industry perspectives on such designs. Then, we illustrate the application of adaptive designs with three case studies, including unblinded sample size reassessment, adaptive treatment selection, and adaptive endpoint selection. We also discuss the availability of software for evaluating and performing such designs. We conclude with a critical review of how expectations from the beginning were fulfilled, and - if not - discuss potential reasons why this did not happen.
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Affiliation(s)
- Peter Bauer
- Section of Medical StatisticsMedical University of ViennaSpitalgasse 231090 WienAustria
| | - Frank Bretz
- Novartis Pharma AGLichtstrasse 354002BaselSwitzerland
- Shanghai University of Finance and EconomicsChina
| | | | - Franz König
- Section of Medical StatisticsMedical University of ViennaSpitalgasse 231090 WienAustria
| | - Gernot Wassmer
- Aptiv Solutions, an ICON plc companyRobert‐Perthel‐Str. 77a50739KölnGermany
- Institute for Medical Statistics, Informatics and EpidemiologyUniversity of Cologne50924KölnGermany
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Renfro LA, Mallick H, An MW, Sargent DJ, Mandrekar SJ. Clinical trial designs incorporating predictive biomarkers. Cancer Treat Rev 2016; 43:74-82. [PMID: 26827695 DOI: 10.1016/j.ctrv.2015.12.008] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2015] [Revised: 12/26/2015] [Accepted: 12/29/2015] [Indexed: 01/13/2023]
Abstract
Development of oncologic therapies has traditionally been performed in a sequence of clinical trials intended to assess safety (phase I), preliminary efficacy (phase II), and improvement over the standard of care (phase III) in homogeneous (in terms of tumor type and disease stage) patient populations. As cancer has become increasingly understood on the molecular level, newer "targeted" drugs that inhibit specific cancer cell growth and survival mechanisms have increased the need for new clinical trial designs, wherein pertinent questions on the relationship between patient biomarkers and response to treatment can be answered. Herein, we review the clinical trial design literature from initial to more recently proposed designs for targeted agents or those treatments hypothesized to have enhanced effectiveness within patient subgroups (e.g., those with a certain biomarker value or who harbor a certain genetic tumor mutation). We also describe a number of real clinical trials where biomarker-based designs have been utilized, including a discussion of their respective advantages and challenges. As cancers become further categorized and/or reclassified according to individual patient and tumor features, we anticipate a continued need for novel trial designs to keep pace with the changing frontier of clinical cancer research.
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Affiliation(s)
- Lindsay A Renfro
- Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, USA.
| | - Himel Mallick
- Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA; The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Ming-Wen An
- Department of Mathematics and Statistics, Vassar College, Poughkeepsie, NY, USA
| | - Daniel J Sargent
- Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, USA
| | - Sumithra J Mandrekar
- Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, USA
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Ondra T, Dmitrienko A, Friede T, Graf A, Miller F, Stallard N, Posch M. Methods for identification and confirmation of targeted subgroups in clinical trials: A systematic review. J Biopharm Stat 2016; 26:99-119. [PMID: 26378339 PMCID: PMC4732423 DOI: 10.1080/10543406.2015.1092034] [Citation(s) in RCA: 73] [Impact Index Per Article: 9.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2015] [Accepted: 08/14/2015] [Indexed: 12/30/2022]
Abstract
Important objectives in the development of stratified medicines include the identification and confirmation of subgroups of patients with a beneficial treatment effect and a positive benefit-risk balance. We report the results of a literature review on methodological approaches to the design and analysis of clinical trials investigating a potential heterogeneity of treatment effects across subgroups. The identified approaches are classified based on certain characteristics of the proposed trial designs and analysis methods. We distinguish between exploratory and confirmatory subgroup analysis, frequentist, Bayesian and decision-theoretic approaches and, last, fixed-sample, group-sequential, and adaptive designs and illustrate the available trial designs and analysis strategies with published case studies.
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Affiliation(s)
- Thomas Ondra
- Center for Medical Statistics and Informatics, Medizinische Universität Wien, Vienna, Austria
| | - Alex Dmitrienko
- Center for Statistics in Drug Development, Quintiles, Overland Park, Kansas, USA
| | - Tim Friede
- Department of Medical Statistics, Universitaetsmedizin, Göttingen, Göttingen, Germany
| | - Alexandra Graf
- Center for Medical Statistics and Informatics, Medizinische Universität Wien, Vienna, Austria
| | - Frank Miller
- Statistiska institutionen, Stockholms Universitet, Stockholm, Sweden
| | - Nigel Stallard
- Department of Statistics and Epidemiology, University of Warwick, Coventry, UK
| | - Martin Posch
- Center for Medical Statistics and Informatics, Medizinische Universität Wien, Vienna, Austria
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Singer DRJ, Zaïr ZM. Clinical Perspectives on Targeting Therapies for Personalized Medicine. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2015; 102:79-114. [PMID: 26827603 PMCID: PMC7102676 DOI: 10.1016/bs.apcsb.2015.11.003] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Expected benefits from new technology include more efficient patient selection for clinical trials, more cost-effective treatment pathways for patients and health services and a more profitable accelerated approach for drug developers. Regulatory authorities expect the pharmaceutical and biotechnology industries to accelerate their development of companion diagnostics and companion therapeutics toward the goal of safer and more effective personalized medicine, and expect health services to fund and prescribers to adopt these new therapeutic technologies. This review discusses the importance of a range of new approaches to developing new and reprofiled medicines to treat common and serious diseases, and rare diseases: new network pharmacology approaches, adaptive trial designs with enriched populations more likely to respond safely to treatment, as assessed by companion diagnostics for response and toxicity risk and use of “real world” data. Case studies are described of single and multiple protein drug targets in several important therapeutic areas. These case studies also illustrate the value and complexity of use of selective biomarkers of clinical response and risk of adverse drug effects, either singly or in combination.
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Affiliation(s)
| | - Zoulikha M Zaïr
- Warwick Medical School, University of Warwick, Coventry, United Kingdom
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42
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Lu TP, Chen JJ. Subgroup identification for treatment selection in biomarker adaptive design. BMC Med Res Methodol 2015; 15:105. [PMID: 26646831 PMCID: PMC4673750 DOI: 10.1186/s12874-015-0098-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2015] [Accepted: 12/01/2015] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND Advances in molecular technology have shifted new drug development toward targeted therapy for treatments expected to benefit subpopulations of patients. Adaptive signature design (ASD) has been proposed to identify the most suitable target patient subgroup to enhance efficacy of treatment effect. There are two essential aspects in the development of biomarker adaptive designs: 1) an accurate classifier to identify the most appropriate treatment for patients, and 2) statistical tests to detect treatment effect in the relevant population and subpopulations. We propose utilization of classification methods to identity patient subgroups and present a statistical testing strategy to detect treatment effects. METHODS The diagonal linear discriminant analysis (DLDA) is used to identify targeted and non-targeted subgroups. For binary endpoints, DLDA is directly applied to classify patient into two subgroups; for continuous endpoints, a two-step procedure involving model fitting and determination of a cutoff-point is used for subgroup classification. The proposed strategy includes tests for treatment effect in all patients and in a marker-positive subgroup, with a possible follow-up estimation of treatment effect in the marker-negative subgroup. The proposed method is compared to the ASD classification method using simulated datasets and two publically available cancer datasets. RESULTS The DLDA-based classifier performs well in terms of sensitivity, specificity, positive and negative predictive values, and accuracy in the simulation data and the two cancer datasets, with superior accuracy compared to the ASD method. The subgroup testing strategy is shown to be useful in detecting treatment effect in terms of power and control of study-wise error. CONCLUSION Accuracy of a classifier is essential for adaptive designs. A poor classifier not only assigns patients to inappropriate treatments, but also reduces the power of the test, resulting in incorrect conclusions. The proposed procedure provides an effective approach for subgroup identification and subgroup analysis.
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Affiliation(s)
- Tzu-Pin Lu
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, Food and Drug Administration, 3900 NCTR Road, HFT-20, Jefferson, AR, 72079, USA. .,Department of Public Health, Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei, Taiwan.
| | - James J Chen
- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, Food and Drug Administration, 3900 NCTR Road, HFT-20, Jefferson, AR, 72079, USA. .,Graduate Institute of Biostatistics, China Medical University, Taichung, Taiwan.
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Dimairo M, Boote J, Julious SA, Nicholl JP, Todd S. Missing steps in a staircase: a qualitative study of the perspectives of key stakeholders on the use of adaptive designs in confirmatory trials. Trials 2015; 16:430. [PMID: 26416387 PMCID: PMC4587783 DOI: 10.1186/s13063-015-0958-9] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2014] [Accepted: 09/14/2015] [Indexed: 11/30/2022] Open
Abstract
Background Despite the promising benefits of adaptive designs (ADs), their routine use, especially in confirmatory trials, is lagging behind the prominence given to them in the statistical literature. Much of the previous research to understand barriers and potential facilitators to the use of ADs has been driven from a pharmaceutical drug development perspective, with little focus on trials in the public sector. In this paper, we explore key stakeholders’ experiences, perceptions and views on barriers and facilitators to the use of ADs in publicly funded confirmatory trials. Methods Semi-structured, in-depth interviews of key stakeholders in clinical trials research (CTU directors, funding board and panel members, statisticians, regulators, chief investigators, data monitoring committee members and health economists) were conducted through telephone or face-to-face sessions, predominantly in the UK. We purposively selected participants sequentially to optimise maximum variation in views and experiences. We employed the framework approach to analyse the qualitative data. Results We interviewed 27 participants. We found some of the perceived barriers to be: lack of knowledge and experience coupled with paucity of case studies, lack of applied training, degree of reluctance to use ADs, lack of bridge funding and time to support design work, lack of statistical expertise, some anxiety about the impact of early trial stopping on researchers’ employment contracts, lack of understanding of acceptable scope of ADs and when ADs are appropriate, and statistical and practical complexities. Reluctance to use ADs seemed to be influenced by: therapeutic area, unfamiliarity, concerns about their robustness in decision-making and acceptability of findings to change practice, perceived complexities and proposed type of AD, among others. Conclusions There are still considerable multifaceted, individual and organisational obstacles to be addressed to improve uptake, and successful implementation of ADs when appropriate. Nevertheless, inferred positive change in attitudes and receptiveness towards the appropriate use of ADs by public funders are supportive and are a stepping stone for the future utilisation of ADs by researchers. Electronic supplementary material The online version of this article (doi:10.1186/s13063-015-0958-9) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Munyaradzi Dimairo
- School of Health and Related Research, Regent Court, University of Sheffield, 30 Regent Street, S1 4DA, Sheffield, UK.
| | - Jonathan Boote
- School of Health and Related Research, Regent Court, University of Sheffield, 30 Regent Street, S1 4DA, Sheffield, UK. .,Centre for Research in Primary and Community Care, University of Hertfordshire, Hatfield, AL109AB, Hertfordshire, UK.
| | - Steven A Julious
- School of Health and Related Research, Regent Court, University of Sheffield, 30 Regent Street, S1 4DA, Sheffield, UK.
| | - Jonathan P Nicholl
- School of Health and Related Research, Regent Court, University of Sheffield, 30 Regent Street, S1 4DA, Sheffield, UK.
| | - Susan Todd
- Department of Mathematics and Statistics, University of Reading, Whiteknights, Reading, RG6 6AX, UK.
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Kimani PK, Todd S, Stallard N. Estimation after subpopulation selection in adaptive seamless trials. Stat Med 2015; 34:2581-601. [PMID: 25903293 PMCID: PMC4973856 DOI: 10.1002/sim.6506] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2013] [Revised: 12/24/2014] [Accepted: 03/22/2015] [Indexed: 12/30/2022]
Abstract
During the development of new therapies, it is not uncommon to test whether a new treatment works better than the existing treatment for all patients who suffer from a condition (full population) or for a subset of the full population (subpopulation). One approach that may be used for this objective is to have two separate trials, where in the first trial, data are collected to determine if the new treatment benefits the full population or the subpopulation. The second trial is a confirmatory trial to test the new treatment in the population selected in the first trial. In this paper, we consider the more efficient two-stage adaptive seamless designs (ASDs), where in stage 1, data are collected to select the population to test in stage 2. In stage 2, additional data are collected to perform confirmatory analysis for the selected population. Unlike the approach that uses two separate trials, for ASDs, stage 1 data are also used in the confirmatory analysis. Although ASDs are efficient, using stage 1 data both for selection and confirmatory analysis introduces selection bias and consequently statistical challenges in making inference. We will focus on point estimation for such trials. In this paper, we describe the extent of bias for estimators that ignore multiple hypotheses and selecting the population that is most likely to give positive trial results based on observed stage 1 data. We then derive conditionally unbiased estimators and examine their mean squared errors for different scenarios.
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Affiliation(s)
- Peter K. Kimani
- Warwick Medical SchoolThe University of WarwickCoventryCV4 7ALU.K.
| | - Susan Todd
- Department of Mathematics and StatisticsThe University of ReadingRG6 6AXReadingU.K.
| | - Nigel Stallard
- Warwick Medical SchoolThe University of WarwickCoventryCV4 7ALU.K.
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Optimal decision rules for biomarker-based subgroup selection for a targeted therapy in oncology. Int J Mol Sci 2015; 16:10354-75. [PMID: 25961947 PMCID: PMC4463650 DOI: 10.3390/ijms160510354] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2015] [Revised: 04/16/2015] [Accepted: 04/29/2015] [Indexed: 01/14/2023] Open
Abstract
Throughout recent years, there has been a rapidly increasing interest regarding the evaluation of so-called targeted therapies. These therapies are assumed to show a greater benefit in a pre-specified subgroup of patients—commonly identified by a predictive biomarker—as compared to the total patient population of interest. This situation has led to the necessity to develop biostatistical methods allowing an efficient evaluation of such treatments. Among others, adaptive enrichment designs have been proposed as a solution. These designs allow the selection of the most promising patient population based on an efficacy analysis at interim and restricting recruitment to these patients afterwards. As has recently been shown, the performance of the applied interim decision rule in such a design plays a crucial role in ensuring a successful trial. In this work, we investigate the situation when the primary outcome of the trial is a binary variable. Optimal decision rules are derived which incorporate the uncertainty about the treatment effects. These optimal decision rules are evaluated with respect to their performance in an adaptive enrichment design in terms of correct selection probability and power, and are compared to proposed ad hoc decision rules. Our methods are illustrated by means of a clinical trial example.
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Graf AC, Posch M, Koenig F. Adaptive designs for subpopulation analysis optimizing utility functions. Biom J 2015; 57:76-89. [PMID: 25399844 PMCID: PMC4314682 DOI: 10.1002/bimj.201300257] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2013] [Revised: 08/19/2014] [Accepted: 08/24/2014] [Indexed: 01/01/2023]
Abstract
If the response to treatment depends on genetic biomarkers, it is important to identify predictive biomarkers that define (sub-)populations where the treatment has a positive benefit risk balance. One approach to determine relevant subpopulations are subgroup analyses where the treatment effect is estimated in biomarker positive and biomarker negative groups. Subgroup analyses are challenging because several types of risks are associated with inference on subgroups. On the one hand, by disregarding a relevant subpopulation a treatment option may be missed due to a dilution of the treatment effect in the full population. Furthermore, even if the diluted treatment effect can be demonstrated in an overall population, it is not ethical to treat patients that do not benefit from the treatment when they can be identified in advance. On the other hand, selecting a spurious subpopulation increases the risk to restrict an efficacious treatment to a too narrow fraction of a potential benefiting population. We propose to quantify these risks with utility functions and investigate nonadaptive study designs that allow for inference on subgroups using multiple testing procedures as well as adaptive designs, where subgroups may be selected in an interim analysis. The characteristics of such adaptive and nonadaptive designs are compared for a range of scenarios.
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Affiliation(s)
- Alexandra C Graf
- Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of ViennaSpitalgasse 23, 1090, Vienna, Austria
- Competence Center for Clinical Trials, University of BremenLinzer Strasse 4, 28359, Bremen, Germany
| | - Martin Posch
- Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of ViennaSpitalgasse 23, 1090, Vienna, Austria
| | - Franz Koenig
- Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of ViennaSpitalgasse 23, 1090, Vienna, Austria
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Götte H, Donica M, Mordenti G. Improving Probabilities of Correct Interim Decision in Population Enrichment Designs. J Biopharm Stat 2014; 25:1020-38. [PMID: 24914474 DOI: 10.1080/10543406.2014.929583] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Abstract
Choice of target population is an essential part at the design stage of clinical trials. Data from earlier clinical development might suggest that the treatment is more effective in a subpopulation, but there might not be enough evidence to restrict the target population upfront. Adaptive designs allow modification of the target population based on interim data. Decision for modification should be based on objective decision rules. The presented decision rules maximize the weighted probability of correct interim decisions based on prior assumptions. Evaluation of decision rules in the planning phase can improve probabilities of correct interim decision and power.
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Affiliation(s)
- Heiko Götte
- a Global Biostatistics, Merck KGaA , Darmstadt , Germany
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