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Robertson DS, Choodari-Oskooei B, Dimairo M, Flight L, Pallmann P, Jaki T. Point estimation for adaptive trial designs II: Practical considerations and guidance. Stat Med 2023; 42:2496-2520. [PMID: 37021359 PMCID: PMC7614609 DOI: 10.1002/sim.9734] [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: 06/25/2021] [Revised: 01/20/2023] [Accepted: 03/18/2023] [Indexed: 04/07/2023]
Abstract
In adaptive clinical trials, the conventional end-of-trial point estimate of a treatment effect is prone to bias, that is, a systematic tendency to deviate from its true value. As stated in recent FDA guidance on adaptive designs, it is desirable to report estimates of treatment effects that reduce or remove this bias. However, it may be unclear which of the available estimators are preferable, and their use remains rare in practice. This article is the second in a two-part series that studies the issue of bias in point estimation for adaptive trials. Part I provided a methodological review of approaches to remove or reduce the potential bias in point estimation for adaptive designs. In part II, we discuss how bias can affect standard estimators and assess the negative impact this can have. We review current practice for reporting point estimates and illustrate the computation of different estimators using a real adaptive trial example (including code), which we use as a basis for a simulation study. We show that while on average the values of these estimators can be similar, for a particular trial realization they can give noticeably different values for the estimated treatment effect. Finally, we propose guidelines for researchers around the choice of estimators and the reporting of estimates following an adaptive design. The issue of bias should be considered throughout the whole lifecycle of an adaptive design, with the estimation strategy prespecified in the statistical analysis plan. When available, unbiased or bias-reduced estimates are to be preferred.
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Affiliation(s)
| | - Babak Choodari-Oskooei
- MRC Clinical Trials Unit at UCL, Institute of Clinical Trials and Methodology, University College London, London, UK
| | - Munya Dimairo
- School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK
| | - Laura Flight
- School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK
| | | | - Thomas Jaki
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
- Faculty of Informatics and Data Science, University of Regensburg, Regensburg, Germany
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2
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Kaizer AM, Belli HM, Ma Z, Nicklawsky AG, Roberts SC, Wild J, Wogu AF, Xiao M, Sabo RT. Recent innovations in adaptive trial designs: A review of design opportunities in translational research. J Clin Transl Sci 2023; 7:e125. [PMID: 37313381 PMCID: PMC10260347 DOI: 10.1017/cts.2023.537] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 03/29/2023] [Accepted: 04/17/2023] [Indexed: 06/15/2023] Open
Abstract
Clinical trials are constantly evolving in the context of increasingly complex research questions and potentially limited resources. In this review article, we discuss the emergence of "adaptive" clinical trials that allow for the preplanned modification of an ongoing clinical trial based on the accumulating evidence with application across translational research. These modifications may include terminating a trial before completion due to futility or efficacy, re-estimating the needed sample size to ensure adequate power, enriching the target population enrolled in the study, selecting across multiple treatment arms, revising allocation ratios used for randomization, or selecting the most appropriate endpoint. Emerging topics related to borrowing information from historic or supplemental data sources, sequential multiple assignment randomized trials (SMART), master protocol and seamless designs, and phase I dose-finding studies are also presented. Each design element includes a brief overview with an accompanying case study to illustrate the design method in practice. We close with brief discussions relating to the statistical considerations for these contemporary designs.
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Affiliation(s)
- Alexander M. Kaizer
- Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Hayley M. Belli
- Department of Population Health, New York University Grossman School of Medicine, New York, NY, USA
| | - Zhongyang Ma
- Department of Population Health, New York University Grossman School of Medicine, New York, NY, USA
| | - Andrew G. Nicklawsky
- Department of Pediatrics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Samantha C. Roberts
- Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Jessica Wild
- Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Adane F. Wogu
- Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Mengli Xiao
- Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Roy T. Sabo
- Department of Biostatistics, Virginia Commonwealth University, Richmond, VA, USA
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3
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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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Chen X, Zhang J, Jiang L, Yan F. IBIS: identify biomarker-based subgroups with a Bayesian enrichment design for targeted combination therapy. BMC Med Res Methodol 2023; 23:66. [PMID: 36941537 PMCID: PMC10026491 DOI: 10.1186/s12874-023-01877-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Accepted: 02/24/2023] [Indexed: 03/23/2023] Open
Abstract
BACKGROUND Combination therapies directed at multiple targets have potentially improved treatment effects for cancer patients. Compared to monotherapy, targeted combination therapy leads to an increasing number of subgroups and complicated biomarker-based efficacy profiles, making it more difficult for efficacy evaluation in clinical trials. Therefore, it is necessary to develop innovative clinical trial designs to explore the efficacy of targeted combination therapy in different subgroups and identify patients who are more likely to benefit from the investigational combination therapy. METHODS We propose a statistical tool called 'IBIS' to Identify BIomarker-based Subgroups and apply it to the enrichment design framework. The IBIS contains three main elements: subgroup division, efficacy evaluation and subgroup identification. We first enumerate all possible subgroup divisions based on biomarker levels. Then, Jensen-Shannon divergence is used to distinguish high-efficacy and low-efficacy subgroups, and Bayesian hierarchical model (BHM) is employed to borrow information within these two subsets for efficacy evaluation. Regarding subgroup identification, a hypothesis testing framework based on Bayes factors is constructed. This framework also plays a key role in go/no-go decisions and enriching specific population. Simulation studies are conducted to evaluate the proposed method. RESULTS The accuracy and precision of IBIS could reach a desired level in terms of estimation performance. In regard to subgroup identification and population enrichment, the proposed IBIS has superior and robust characteristics compared with traditional methods. An example of how to obtain design parameters for an adaptive enrichment design under the IBIS framework is also provided. CONCLUSIONS IBIS has the potential to be a useful tool for biomarker-based subgroup identification and population enrichment in clinical trials of targeted combination therapy.
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Affiliation(s)
- Xin Chen
- Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Jingyi Zhang
- Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Liyun Jiang
- Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Fangrong Yan
- Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, China.
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5
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Robertson DS, Choodari‐Oskooei B, Dimairo M, Flight L, Pallmann P, Jaki T. Point estimation for adaptive trial designs I: A methodological review. Stat Med 2023; 42:122-145. [PMID: 36451173 PMCID: PMC7613995 DOI: 10.1002/sim.9605] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Revised: 10/21/2022] [Accepted: 11/01/2022] [Indexed: 12/02/2022]
Abstract
Recent FDA guidance on adaptive clinical trial designs defines bias as "a systematic tendency for the estimate of treatment effect to deviate from its true value," and states that it is desirable to obtain and report estimates of treatment effects that reduce or remove this bias. The conventional end-of-trial point estimates of the treatment effects are prone to bias in many adaptive designs, because they do not take into account the potential and realized trial adaptations. While much of the methodological developments on adaptive designs have tended to focus on control of type I error rates and power considerations, in contrast the question of biased estimation has received relatively less attention. This article is the first in a two-part series that studies the issue of potential bias in point estimation for adaptive trials. Part I provides a comprehensive review of the methods to remove or reduce the potential bias in point estimation of treatment effects for adaptive designs, while part II illustrates how to implement these in practice and proposes a set of guidelines for trial statisticians. The methods reviewed in this article can be broadly classified into unbiased and bias-reduced estimation, and we also provide a classification of estimators by the type of adaptive design. We compare the proposed methods, highlight available software and code, and discuss potential methodological gaps in the literature.
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Affiliation(s)
| | | | - Munya Dimairo
- School of Health and Related Research (ScHARR)University of SheffieldSheffieldUK
| | - Laura Flight
- School of Health and Related Research (ScHARR)University of SheffieldSheffieldUK
| | | | - Thomas Jaki
- MRC Biostatistics UnitUniversity of CambridgeCambridgeUK
- Faculty of Informatics and Data ScienceUniversity of RegensburgRegensburgGermany
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6
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Guo X, Wei W, Liu M, Cai T, Wu C, Wang J. Assessing the Most Vulnerable Subgroup to Type II Diabetes Associated with Statin Usage: Evidence from Electronic Health Record Data. J Am Stat Assoc 2023; 118:1488-1499. [PMID: 38223220 PMCID: PMC10786632 DOI: 10.1080/01621459.2022.2157727] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Accepted: 11/21/2022] [Indexed: 12/23/2022]
Abstract
There have been increased concerns that the use of statins, one of the most commonly prescribed drugs for treating coronary artery disease, is potentially associated with the increased risk of new-onset Type II diabetes (T2D). Nevertheless, to date, there is no robust evidence supporting as to whether and what kind of populations are indeed vulnerable for developing T2D after taking statins. In this case study, leveraging the biobank and electronic health record data in the Partner Health System, we introduce a new data analysis pipeline and a novel statistical methodology that address existing limitations by (i) designing a rigorous causal framework that systematically examines the causal effects of statin usage on T2D risk in observational data, (ii) uncovering which patient subgroup is most vulnerable for developing T2D after taking statins, and (iii) assessing the replicability and statistical significance of the most vulnerable subgroup via a bootstrap calibration procedure. Our proposed approach delivers asymptotically sharp confidence intervals and debiased estimate for the treatment effect of the most vulnerable subgroup in the presence of high-dimensional covariates. With our proposed approach, we find that females with high T2D genetic risk are at the highest risk of developing T2D due to statin usage.
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Affiliation(s)
- Xinzhou Guo
- Department of Mathematics, Hong Kong University of Science and Technology, Hong Kong, Hong Kong
| | - Waverly Wei
- Division of Biostatistics, UC Berkeley, Berkeley, CA
| | - Molei Liu
- Department of Biostatistics, Columbia Mailman School of Public Health, New York, NY
| | - Tianxi Cai
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA
| | - Chong Wu
- Department of Biostatistics, MD Anderson Cancer Center, Houston, TX
| | - Jingshen Wang
- Division of Biostatistics, UC Berkeley, Berkeley, CA
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7
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Sinha AK, Moye L, Piller LB, Yamal JM, Barcenas CH, Song J, Davis BR. Simultaneous population enrichment and endpoint selection in phase 3 randomized controlled trials: An adaptive group sequential design with two binary alternative primary endpoints. COMMUN STAT-THEOR M 2023. [DOI: 10.1080/03610926.2022.2163180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Affiliation(s)
- Arup K. Sinha
- Department of Biostatistics, The University of Texas School of Public Health, Houston, Texas, USA
| | - Lemuel Moye
- Department of Biostatistics, The University of Texas School of Public Health, Houston, Texas, USA
| | - Linda B. Piller
- Division of Epidemiology, Human Genetics and Environmental Sciences, The University of Texas School of Public Health, Houston, Texas, USA
| | - Jose-Miguel Yamal
- Department of Biostatistics, The University of Texas School of Public Health, Houston, Texas, USA
| | - Carlos H. Barcenas
- Department of Breast Medical Oncology, Division of Cancer Medicine, MD Anderson Cancer Center, Houston, Texas, USA
| | - Jaejoon Song
- Department of Biostatistics, MD Anderson Cancer Center, Houston, Texas, USA
| | - Barry R. Davis
- Department of Biostatistics, The University of Texas School of Public Health, Houston, Texas, USA
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8
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Zhang W, Ro S, Jiang Q, Li X, Liu R, Lu C'C, Marchenko O, Zhao J, Xu Z. Statistical and Operational Considerations for 2-Stage Adaptive Designs with Simultaneous Evaluation of Overall and Marker-Selected Populations in Oncology Confirmatory Trials. Ther Innov Regul Sci 2022; 56:552-560. [PMID: 35503503 DOI: 10.1007/s43441-022-00407-y] [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: 11/18/2021] [Accepted: 04/07/2022] [Indexed: 11/24/2022]
Abstract
In biomarker enrichment study designs that start with an all-comer population, simultaneous evaluation of the entire and the marker-selected populations can be more desirable than pre-specifying the testing order, when the degree of marker predictiveness is uncertain. While there has been substantial research on this approach, our goal is to provide a complete overview and guidance in all aspects of this approach, including the interim analysis potentially using different endpoints, combination tests with associated multiplicity control, and the final treatment effect estimation. Regulatory/operational aspects and actual cases demonstrating the potential advantage of this approach are also described.
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Affiliation(s)
| | - Sunhee Ro
- Sierra Oncology, Inc., San Mateo, CA, USA
| | | | | | - Rong Liu
- Bristol Myers Squibb, Co., New York, NY, USA
| | | | | | - Jing Zhao
- Merck & Co, Inc., Kenilworth, NJ, USA
| | - Zhenzhen Xu
- Food and Drug Administration, Silver Spring, MD, USA
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9
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Laska E, Siegel C, Lin Z. A likely responder approach for the analysis of randomized controlled trials. Contemp Clin Trials 2022; 114:106688. [PMID: 35085831 PMCID: PMC8934276 DOI: 10.1016/j.cct.2022.106688] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Revised: 12/03/2021] [Accepted: 01/19/2022] [Indexed: 11/16/2022]
Abstract
OBJECTIVE To further the precision medicine goal of tailoring medical treatment to individual patient characteristics by providing a method of analysis of the effect of test treatment, T, compared to a reference treatment, R, in participants in a RCT who are likely responders to T. METHODS Likely responders to T are individuals whose expected response at baseline exceeds a prespecified minimum. A prognostic score, the expected response predicted as a function of baseline covariates, is obtained at trial completion. It is a balancing score that can be used to match likely responders randomized to T with those randomized to R; the result is comparable treatment groups that have a common covariance distribution. Treatments are compared based on observed outcomes in this enriched sample. The approach is illustrated in a RCT comparing two treatments for opioid use disorder. RESULTS A standard statistical analysis of the opioid use disorder RCT found no treatment difference in the total sample. However, a subset of likely responders to T were identified and in this group, T was statistically superior to R. CONCLUSION The causal treatment effect of T relative to R among likely responders may be more important than the effect in the whole target population. The prognostic score function provides quantitative information to support patient specific treatment decisions regarding T furthering the goal of precision medicine.
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Affiliation(s)
- Eugene Laska
- Department of Psychiatry, New York University Grossman School of Medicine, One Park Avenue, New York, NY 10016, USA; Department of Population Health, Division of Biostatistics, NYU Grossman School of Medicine, 180 Madison Avenue, New York, NY 10016, USA.
| | - Carole Siegel
- Department of Psychiatry, New York University Grossman School of Medicine, One Park Avenue, New York, NY 10016, USA; Department of Population Health, Division of Biostatistics, NYU Grossman School of Medicine, 180 Madison Avenue, New York, NY 10016, USA.
| | - Ziqiang Lin
- Department of Psychiatry, New York University Grossman School of Medicine, One Park Avenue, New York, NY 10016, USA.
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10
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Vinnat V, Chevret S. Enrichment Bayesian design for randomized clinical trials using categorical biomarkers and a binary outcome. BMC Med Res Methodol 2022; 22:54. [PMID: 35220954 PMCID: PMC8882316 DOI: 10.1186/s12874-022-01513-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Accepted: 01/11/2022] [Indexed: 11/30/2022] Open
Abstract
Background Adaptive clinical trials have been increasingly commonly employed to select a potential target population for one trial without conducting trials separately. Such enrichment designs typically consist of two or three stages, where the first stage serves as a screening process for selecting a specific subpopulation. Methods We propose a Bayesian design for randomized clinical trials with a binary outcome that focuses on restricting the inclusion to a subset of patients who are likely to benefit the most from the treatment during trial accrual. Several Bayesian measures of efficacy and treatment-by-subset interactions were used to dictate the enrichment, either based on Gail and Simon’s or Millen’s criteria. A simulation study was used to assess the performance of our design. The method is exemplified in a real randomized clinical trial conducted in patients with respiratory failure that failed to show any benefit of high flow oxygen supply compared with standard oxygen. Results The use of the enrichment rules allowed the detection of the existence of a treatment-by-subset interaction more rapidly compared with Gail and Simon’s criteria, with decreasing proportions of enrollment in the whole sample, and the proportions of enrichment lower, in the presence of interaction based on Millen’s criteria. In the real dataset, this may have allowed the detection of the potential interest of high flow oxygen in patients with a SOFA neurological score ≥ 1. Conclusion Enrichment designs that handle the uncertainty in treatment efficacy by focusing on the target population offer a promising balance for trial efficiency and ease of interpretation. Supplementary Information The online version contains supplementary material available at (10.1186/s12874-022-01513-z).
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11
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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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12
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Abstract
Adaptive enrichment designs for clinical trials may include rules that use interim data to identify treatment-sensitive patient subgroups, select or compare treatments, or change entry criteria. A common setting is a trial to compare a new biologically targeted agent to standard therapy. An enrichment design's structure depends on its goals, how it accounts for patient heterogeneity and treatment effects, and practical constraints. This article first covers basic concepts, including treatment-biomarker interaction, precision medicine, selection bias, and sequentially adaptive decision making, and briefly describes some different types of enrichment. Numerical illustrations are provided for qualitatively different cases involving treatment-biomarker interactions. Reviews are given of adaptive signature designs; a Bayesian design that uses a random partition to identify treatment-sensitive biomarker subgroups and assign treatments; and designs that enrich superior treatment sample sizes overall or within subgroups, make subgroup-specific decisions, or include outcome-adaptive randomization.
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Affiliation(s)
- Peter F Thall
- Department of Biostatistics, M.D. Anderson Cancer Center, University of Texas, Houston, Texas 77030, USA
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13
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Zhang C, Mayo MS, Wick JA, Gajewski BJ. Designing and analyzing clinical trials for personalized medicine via Bayesian models. Pharm Stat 2021; 20:573-596. [PMID: 33463906 DOI: 10.1002/pst.2095] [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: 07/19/2019] [Revised: 09/21/2020] [Accepted: 12/31/2020] [Indexed: 11/11/2022]
Abstract
Patients with different characteristics (e.g., biomarkers, risk factors) may have different responses to the same medicine. Personalized medicine clinical studies that are designed to identify patient subgroup treatment efficacies can benefit patients and save medical resources. However, subgroup treatment effect identification complicates the study design in consideration of desired operating characteristics. We investigate three Bayesian adaptive models for subgroup treatment effect identification: pairwise independent, hierarchical, and cluster hierarchical achieved via Dirichlet Process (DP). The impact of interim analysis and longitudinal data modeling on the personalized medicine study design is also explored. Interim analysis is considered since they can accelerate personalized medicine studies in cases where early stopping rules for success or futility are met. We apply integrated two-component prediction method (ITP) for longitudinal data simulation, and simple linear regression for longitudinal data imputation to optimize the study design. The designs' performance in terms of power for the subgroup treatment effects and overall treatment effect, sample size, and study duration are investigated via simulation. We found the hierarchical model is an optimal approach to identifying subgroup treatment effects, and the cluster hierarchical model is an excellent alternative approach in cases where sufficient information is not available for specifying the priors. The interim analysis introduction to the study design lead to the trade-off between power and expected sample size via the adjustment of the early stopping criteria. The introduction of the longitudinal modeling slightly improves the power. These findings can be applied to future personalized medicine studies with discrete or time-to-event endpoints.
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Affiliation(s)
- Chuanwu Zhang
- Department of Biostatistics & Data Science, University of Kansas Medical Center, Kansas City, Kansas, USA.,Sanofi, Waltham, Massachusetts, USA
| | - Matthew S Mayo
- Department of Biostatistics & Data Science, University of Kansas Medical Center, Kansas City, Kansas, USA
| | - Jo A Wick
- Department of Biostatistics & Data Science, University of Kansas Medical Center, Kansas City, Kansas, USA
| | - Byron J Gajewski
- Department of Biostatistics & Data Science, University of Kansas Medical Center, Kansas City, Kansas, USA
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14
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Park Y, Liu S, Thall PF, Yuan Y. Bayesian group sequential enrichment designs based on adaptive regression of response and survival time on baseline biomarkers. Biometrics 2021; 78:60-71. [PMID: 33438761 DOI: 10.1111/biom.13421] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2020] [Revised: 12/08/2020] [Accepted: 12/22/2020] [Indexed: 11/29/2022]
Abstract
Precision medicine relies on the idea that, for a particular targeted agent, only a subpopulation of patients is sensitive to it and thus may benefit from it therapeutically. In practice, it is often assumed based on preclinical data that a treatment-sensitive subpopulation is known, and moreover that the agent is substantively efficacious in that subpopulation. Due to important differences between preclinical settings and human biology, however, data from patients treated with a new targeted agent often show that one or both of these assumptions are false. This paper provides a Bayesian randomized group sequential enrichment design that compares an experimental treatment to a control based on survival time and uses early response as an ancillary outcome to assist with adaptive variable selection and enrichment. Initially, the design enrolls patients under broad eligibility criteria. At each interim decision, submodels for regression of response and survival time on a baseline covariate vector and treatment are fit; variable selection is used to identify a covariate subvector that characterizes treatment-sensitive patients and determines a personalized benefit index, and comparative superiority and futility decisions are made. Enrollment of each cohort is restricted to the most recent adaptively identified treatment-sensitive patients. Group sequential decision cutoffs are calibrated to control overall type I error and account for the adaptive enrollment restriction. The design provides a basis for precision medicine by identifying a treatment-sensitive subpopulation, if it exists, and determining whether the experimental treatment is superior to the control in that subpopulation. A simulation study shows that the proposed design reliably identifies a sensitive subpopulation, yields much higher generalized power compared to several existing enrichment designs and a conventional all-comers group sequential design, and is robust.
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Affiliation(s)
- Yeonhee Park
- Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, Wisconsin, USA
| | - Suyu Liu
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Peter F Thall
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Ying Yuan
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
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15
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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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16
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Burnett T, Mozgunov P, Pallmann P, Villar SS, Wheeler GM, Jaki T. Adding flexibility to clinical trial designs: an example-based guide to the practical use of adaptive designs. BMC Med 2020; 18:352. [PMID: 33208155 PMCID: PMC7677786 DOI: 10.1186/s12916-020-01808-2] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Accepted: 10/07/2020] [Indexed: 12/18/2022] Open
Abstract
Adaptive designs for clinical trials permit alterations to a study in response to accumulating data in order to make trials more flexible, ethical, and efficient. These benefits are achieved while preserving the integrity and validity of the trial, through the pre-specification and proper adjustment for the possible alterations during the course of the trial. Despite much research in the statistical literature highlighting the potential advantages of adaptive designs over traditional fixed designs, the uptake of such methods in clinical research has been slow. One major reason for this is that different adaptations to trial designs, as well as their advantages and limitations, remain unfamiliar to large parts of the clinical community. The aim of this paper is to clarify where adaptive designs can be used to address specific questions of scientific interest; we introduce the main features of adaptive designs and commonly used terminology, highlighting their utility and pitfalls, and illustrate their use through case studies of adaptive trials ranging from early-phase dose escalation to confirmatory phase III studies.
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Affiliation(s)
- Thomas Burnett
- Department of Mathematics and Statistics, Lancaster University, Fylde College, Lancaster, LA1 4YF UK
| | - Pavel Mozgunov
- Department of Mathematics and Statistics, Lancaster University, Fylde College, Lancaster, LA1 4YF UK
| | - Philip Pallmann
- Centre for Trials Research, College of Biomedical & Life Sciences, Cardiff University, Cardiff, UK
| | - Sofia S. Villar
- MRC Biostatistics Unit, University of Cambridge School of Clinical Medicine, Cambridge Institute of Public Health, Forvie Site, Robinson Way, Cambridge Biomedical Campus, Cambridge, CB2 0SR UK
| | - Graham M. Wheeler
- Cancer Research UK & UCL Cancer Trials Centre, University College London, 90 Tottenham Court Road, London, W1T 4TJ UK
| | - Thomas Jaki
- Department of Mathematics and Statistics, Lancaster University, Fylde College, Lancaster, LA1 4YF UK
- MRC Biostatistics Unit, University of Cambridge School of Clinical Medicine, Cambridge Institute of Public Health, Forvie Site, Robinson Way, Cambridge Biomedical Campus, Cambridge, CB2 0SR UK
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17
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Jin J, Riviere MK, Luo X, Dong Y. Bayesian methods for the analysis of early-phase oncology basket trials with information borrowing across cancer types. Stat Med 2020; 39:3459-3475. [PMID: 32717103 DOI: 10.1002/sim.8675] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2019] [Revised: 06/04/2020] [Accepted: 06/05/2020] [Indexed: 12/18/2022]
Abstract
Research in oncology has changed the focus from histological properties of tumors in a specific organ to a specific genomic aberration potentially shared by multiple cancer types. This motivates the basket trial, which assesses the efficacy of treatment simultaneously on multiple cancer types that have a common aberration. Although the assumption of homogeneous treatment effects seems reasonable given the shared aberration, in reality, the treatment effect may vary by cancer type, and potentially only a subgroup of the cancer types respond to the treatment. Various approaches have been proposed to increase the trial power by borrowing information across cancer types, which, however, tend to inflate the type I error rate. In this article, we review some representative Bayesian information borrowing methods for the analysis of early-phase basket trials. We then propose a novel method called the Bayesian hierarchical model with a correlated prior (CBHM), which conducts more flexible borrowing across cancer types according to sample similarity. We did simulation studies to compare CBHM with independent analysis and three information borrowing approaches: the conventional Bayesian hierarchical model, the EXNEX approach, and Liu's two-stage approach. Simulation results show that all information borrowing approaches substantially improve the power of independent analysis if a large proportion of the cancer types truly respond to the treatment. Our proposed CBHM approach shows an advantage over the existing information borrowing approaches, with a power similar to that of EXNEX or Liu's approach, but the potential to provide substantially better control of type I error rate.
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Affiliation(s)
- Jin Jin
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA
| | - Marie-Karelle Riviere
- Department of Biostatistics and Programming, Research and Development, Sanofi, Chilly-Mazarin, France
| | - Xiaodong Luo
- Department of Biostatistics and Programming, Sanofi, Bridgewater, New Jersey, USA
| | - Yingwen Dong
- Department of Biostatistics and Programming Oncology, Sanofi, Cambridge, Massachusetts, USA
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18
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Affiliation(s)
- Xinzhou Guo
- Department of Statistics, University of Michigan , Ann Arbor , MI
| | - Xuming He
- Department of Statistics, University of Michigan , Ann Arbor , MI
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19
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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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20
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Simon R. Review of Statistical Methods for Biomarker-Driven Clinical Trials. JCO Precis Oncol 2019; 3:1-9. [PMID: 35100721 DOI: 10.1200/po.18.00407] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
The discovery of somatic driver mutations in kinases and receptors has stimulated the development of molecularly targeted treatments that require companion diagnostics and new approaches to clinical development. This article reviews some of the clinical trial designs that have been developed to address these opportunities, including phase II basket and platform trials as well as phase III enrichment and biomarker adaptive designs. It also re-examines some of the conventional wisdom that previously dominated clinical trial design and discusses development and internal validation of a predictive biomarker as a new paradigm for optimizing the intended-use subset for a treatment. Statistical methods now being used in adaptive biomarker-driven clinical trials are reviewed. Some previous paradigms for clinical trial design can limit the development of more effective methods on the basis of prospectively planned adaptive methods, but useful new methods have been developed for analysis of genome-wide data and for the design of adaptively enriched studies. In many cases, the heterogeneity of populations eligible for clinical trials as traditionally defined makes it unlikely that molecularly targeted treatments will be effective for a majority of the eligible patients. New methods for dealing with patient heterogeneity in therapeutic response should be used in the design of phase III clinical trials.
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21
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Nugent C, Guo W, Müller P, Ji Y. Bayesian Approaches to Subgroup Analysis and Related Adaptive Clinical Trial Designs. JCO Precis Oncol 2019; 3:PO.19.00003. [PMID: 32923858 PMCID: PMC7446414 DOI: 10.1200/po.19.00003] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/29/2019] [Indexed: 11/20/2022] Open
Abstract
We review Bayesian and Bayesian decision theoretic approaches to subgroup analysis and applications to subgroup-based adaptive clinical trial designs. Subgroup analysis refers to inference about subpopulations with significantly distinct treatment effects. The discussion mainly focuses on inference for a benefiting subpopulation, that is, a characterization of a group of patients who benefit from the treatment under consideration more than the overall population. We introduce alternative approaches and demonstrate them with a small simulation study. Then, we turn to clinical trial designs. When the selection of the interesting subpopulation is carried out as the trial proceeds, the design becomes an adaptive clinical trial design, using subgroup analysis to inform the randomization and assignment of treatments to patients. We briefly review some related designs. There are a variety of approaches to Bayesian subgroup analysis. Practitioners should consider the type of subpopulations in which they are interested and choose their methods accordingly. We demonstrate how subgroup analysis can be carried out by different Bayesian methods and discuss how they identify slightly different subpopulations.
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Affiliation(s)
| | | | | | - Yuan Ji
- University of Chicago, Chicago, IL
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22
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Li W, Zhao J, Li X, Chen C, Beckman RA. Multi‐stage enrichment and basket trial designs with population selection. Stat Med 2019; 38:5470-5485. [DOI: 10.1002/sim.8371] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2018] [Revised: 06/03/2019] [Accepted: 08/16/2019] [Indexed: 11/06/2022]
Affiliation(s)
- Wen Li
- Biostatistics and Research Decision Sciences, Merck Research LaboratoriesMerck & Co, Inc Kenilworth New Jersey
| | - Jing Zhao
- Biostatistics and Research Decision Sciences, Merck Research LaboratoriesMerck & Co, Inc Kenilworth New Jersey
| | - Xiaoyun Li
- Biostatistics and Research Decision Sciences, Merck Research LaboratoriesMerck & Co, Inc Kenilworth New Jersey
| | - Cong Chen
- Biostatistics and Research Decision Sciences, Merck Research LaboratoriesMerck & Co, Inc Kenilworth New Jersey
| | - Robert A. Beckman
- Departments of Oncology and of Biostatistics, Bioinformatics, and Biomathematics, Lombardi Comprehensive Cancer Center and Innovation Center for Biomedical InformaticsGeorgetown University Medical Center Washington District of Columbia
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23
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Wan F, Titman AC, Jaki TF. Subgroup analysis of treatment effects for misclassified biomarkers with time‐to‐event data. J R Stat Soc Ser C Appl Stat 2019. [DOI: 10.1111/rssc.12364] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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24
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Sinha AK, Moye L, Piller LB, Yamal J, Barcenas CH, Lin J, Davis BR. Adaptive group‐sequential design with population enrichment in phase 3 randomized controlled trials with two binary co‐primary endpoints. Stat Med 2019; 38:3985-3996. [DOI: 10.1002/sim.8216] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2018] [Revised: 04/28/2019] [Accepted: 05/09/2019] [Indexed: 11/11/2022]
Affiliation(s)
- Arup K. Sinha
- Department of Biostatistics, School of Public HealthYale University New Haven Connecticut
| | - Lemuel Moye
- Department of Biostatistics, School of Public HealthThe University of Texas Health Science Center at Houston Houston Texas
| | - Linda B. Piller
- Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public HealthThe University of Texas Health Science Center at Houston Houston Texas
| | - Jose‐Miguel Yamal
- Department of Biostatistics, School of Public HealthThe University of Texas Health Science Center at Houston Houston Texas
| | - Carlos H. Barcenas
- Department of Breast Medical Oncology, Division of Cancer MedicineThe University of Texas MD Anderson Cancer Center Houston Texas
| | | | - Barry R. Davis
- Department of Biostatistics, School of Public HealthThe University of Texas Health Science Center at Houston Houston Texas
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25
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Simon N, Simon R. Using Bayesian modeling in frequentist adaptive enrichment designs. Biostatistics 2019; 19:27-41. [PMID: 28520893 DOI: 10.1093/biostatistics/kxw054] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2015] [Accepted: 11/25/2016] [Indexed: 11/14/2022] Open
Abstract
Our increased understanding of the mechanistic heterogeneity of diseases has pushed the development of targeted therapeutics. We do not expect all patients with a given disease to benefit from a targeted drug; only those in the target population. That is, those with sufficient dysregulation in the biomolecular pathway targeted by treatment. However, due to complexity of the pathway, and/or technical issues with our characterizing assay, it is often hard to characterize the target population until well into large-scale clinical trials. This has stimulated the development of adaptive enrichment trials; clinical trials in which the target population is adaptively learned; and enrollment criteria are adaptively updated to reflect this growing understanding. This paper proposes a framework for group-sequential adaptive enrichment trials. Building on the work of Simon & Simon (2013). Adaptive enrichment designs for clinical trials. Biostatistics 14(4), 613-625), it includes a frequentist hypothesis test at the end of the trial. However, it uses Bayesian methods to optimize the decisions required during the trial (regarding how to restrict enrollment) and Bayesian methods to estimate effect size, and characterize the target population at the end of the trial. This joint frequentist/Bayesian design combines the power of Bayesian methods for decision making with the use of a formal hypothesis test at the end of the trial to preserve the studywise probability of a type I error.
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Affiliation(s)
- Noah Simon
- Department of Biostatistics, University of Washington, Box 357232, Seattle, WA 98195, USA
| | - Richard Simon
- Biometric Research Branch of the National Cancer Institute (at the National Institutes of Health), 9609 Medical Center Dr, Rockville, MD 20850, USA
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26
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Nonaka T, Igeta M, Matsui S. Statistical testing strategies for assessing treatment efficacy and marker accuracy in phase III trials. Pharm Stat 2019; 18:459-475. [PMID: 30838777 DOI: 10.1002/pst.1937] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2017] [Revised: 10/13/2018] [Accepted: 01/24/2019] [Indexed: 11/11/2022]
Abstract
When a candidate predictive marker is available, but evidence on its predictive ability is not sufficiently reliable, all-comers trials with marker stratification are frequently conducted. We propose a framework for planning and evaluating prospective testing strategies in confirmatory, phase III marker-stratified clinical trials based on a natural assumption on heterogeneity of treatment effects across marker-defined subpopulations, where weak rather than strong control is permitted for multiple population tests. For phase III marker-stratified trials, it is expected that treatment efficacy is established in a particular patient population, possibly in a marker-defined subpopulation, and that the marker accuracy is assessed when the marker is used to restrict the indication or labelling of the treatment to a marker-based subpopulation, ie, assessment of the clinical validity of the marker. In this paper, we develop statistical testing strategies based on criteria that are explicitly designated to the marker assessment, including those examining treatment effects in marker-negative patients. As existing and developed statistical testing strategies can assert treatment efficacy for either the overall patient population or the marker-positive subpopulation, we also develop criteria for evaluating the operating characteristics of the statistical testing strategies based on the probabilities of asserting treatment efficacy across marker subpopulations. Numerical evaluations to compare the statistical testing strategies based on the developed criteria are provided.
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Affiliation(s)
- Takahiro Nonaka
- Office of New Drug V, Pharmaceuticals and Medical Devices Agency, Tokyo, Japan
| | - Masataka Igeta
- Department of Biostatistics, Hyogo College of Medicine, Nishinomiya, Japan
| | - Shigeyuki Matsui
- Department of Biostatistics, Nagoya University Graduate School of Medicine, Nagoya, Japan
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27
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Wang T, Wang X, Zhou H, Cai J, George SL. Auxiliary variable-enriched biomarker-stratified design. Stat Med 2018; 37:4610-4635. [PMID: 30221368 DOI: 10.1002/sim.7938] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2018] [Revised: 06/04/2018] [Accepted: 07/15/2018] [Indexed: 12/18/2022]
Abstract
Clinical trials in the era of precision medicine require assessment of biomarkers to identify appropriate subgroups of patients for targeted therapy. In a biomarker-stratified design (BSD), biomarkers are measured on all patients and used as stratification variables. However, such a trial can be both inefficient and costly, especially when the prevalence of the subgroup of primary interest is low and the cost of assessing the biomarkers is high. Efficiency can be improved and costs reduced by using enriched biomarker-stratified designs, in which patients of primary interest, typically the biomarker-positive patients, are oversampled. We consider a special type of enrichment design, an auxiliary variable-enriched design (AEBSD), in which enrichment is based on some inexpensive auxiliary variable that is positively correlated with the true biomarker. The proposed AEBSD reduces the total cost of the trial compared with a standard BSD when the prevalence rate of true biomarker positivity is small and the positive predictive value (PPV) of the auxiliary biomarker is larger than the prevalence rate. In addition, for an AEBSD, we can immediately randomize the patients selected in the screening process without waiting for the result of the true biomarker test, reducing the treatment waiting time. We propose an adaptive Bayesian method to adjust the assumed PPV while the trial is ongoing. Numerical studies and an example illustrate the approach. An R package is available.
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Affiliation(s)
- Ting Wang
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Xiaofei Wang
- Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina
| | - Haibo Zhou
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Jianwen Cai
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Stephen L George
- Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina
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28
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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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29
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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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30
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Simon R, Simon N. Inference for multimarker adaptive enrichment trials. Stat Med 2017; 36:4083-4093. [PMID: 28795420 PMCID: PMC7780249 DOI: 10.1002/sim.7422] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2016] [Revised: 04/24/2017] [Accepted: 06/28/2017] [Indexed: 11/07/2022]
Abstract
Identification of treatment selection biomarkers has become very important in cancer drug development. Adaptive enrichment designs have been developed for situations where a unique treatment selection biomarker is not apparent based on the mechanism of action of the drug. With such designs, the eligibility rules may be adaptively modified at interim analysis times to exclude patients who are unlikely to benefit from the test treatment.We consider a recently proposed, particularly flexible approach that permits development of model-based multifeature predictive classifiers as well as optimized cut-points for continuous biomarkers. A single significance test, including all randomized patients, is performed at the end of the trial of the strong null hypothesis that the expected outcome on the test treatment is no better than control for any of the subset populations of patients accrued in the K stages of the clinical trial. In this paper, we address 2 issues involving inference following an adaptive enrichment design as described above. The first is specification of the intended use population and estimation of treatment effect for that population following rejection of the strong null hypothesis. The second issue is defining conditions in which rejection of the strong null hypothesis implies rejection of the null hypothesis for the intended use population.
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Affiliation(s)
- Richard Simon
- Biometric Research Program, National Cancer Institute, Rockville, MD 20850, USA
| | - Noah Simon
- Department of Biostatistics, University of Washington, Seattle, WA 98195, USA
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31
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Zhang Z, Chen R, Soon G, Zhang H. Treatment evaluation for a data-driven subgroup in adaptive enrichment designs of clinical trials. Stat Med 2017; 37:1-11. [PMID: 28948633 DOI: 10.1002/sim.7497] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2016] [Revised: 05/29/2017] [Accepted: 08/24/2017] [Indexed: 11/09/2022]
Abstract
Adaptive enrichment designs (AEDs) of clinical trials allow investigators to restrict enrollment to a promising subgroup based on an interim analysis. Most of the existing AEDs deal with a small number of predefined subgroups, which are often unknown at the design stage. The newly developed Simon design offers a great deal of flexibility in subgroup selection (without requiring pre-defined subgroups) but does not provide a procedure for estimating and testing treatment efficacy for the selected subgroup. This article proposes a 2-stage AED which does not require predefined subgroups but requires a prespecified algorithm for choosing a subgroup on the basis of baseline covariate information. Having a prespecified algorithm for subgroup selection makes it possible to use cross-validation and bootstrap methods to correct for the resubstitution bias in estimating treatment efficacy for the selected subgroup. The methods are evaluated and compared in a simulation study mimicking actual clinical trials of human immunodeficiency virus infection.
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Affiliation(s)
- Zhiwei Zhang
- Department of Statistics, University of California at Riverside, Riverside, California, USA
| | - Ruizhe Chen
- Division of Epidemiology and Biostatistics, School of Public Health, University of Illinois at Chicago, Chicago, Illinois, USA
| | - Guoxing Soon
- Division of Biometrics IV, Office of Biostatistics, Center for Drug Evaluation and Research, Food and Drug Administration, Silver Spring, Maryland, USA
| | - Hui Zhang
- Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
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32
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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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33
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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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34
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Li W, Chen C, Li X, Beckman RA. Estimation of treatment effect in two-stage confirmatory oncology trials of personalized medicines. Stat Med 2017; 36:1843-1861. [PMID: 28303586 DOI: 10.1002/sim.7272] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2016] [Accepted: 02/14/2017] [Indexed: 12/26/2022]
Abstract
A personalized medicine may benefit a subpopulation with certain predictive biomarker signatures or certain disease types. However, there is great uncertainty about drug activity in a subpopulation when designing a confirmatory trial in practice, and it is logical to take a two-stage approach with the study unless credible external information is available for decision-making purpose. The first stage deselects (or prunes) non-performing subpopulations at an interim analysis, and the second stage pools the remaining subpopulations in the final analysis. The endpoints used at the two stages can be different in general. A key issue of interest is the statistical property of the test statistics and point estimate at the final analysis. Previous research has focused on type I error control and power calculation for such two-stage designs. This manuscript will investigate estimation bias of the treatment effect, which is implicit in the adjustment of nominal type I error for multiplicity control in such two-stage designs. Previous work handles the treatment effect of an intermediate endpoint as a nuisance parameter to provide the most conservative type I error control. This manuscript takes the same approach to explore the bias. The methodology is applied to the two previously studied designs. In the first design, patients with different biomarker levels are enrolled in a study, and the treatment effect is assumed to be in an order. The goal of the interim analysis is to identify a biomarker cut-off point for the subpopulations. In the second design, patients with different tumour types but the same biomarker signature are included in a trial applying a basket design. The goal of the interim analysis is to identify a subset of tumour types in the absence of treatment effect ordering. Closed-form equations are provided for the estimation bias as well as the variance under the two designs. Simulations are conducted under various scenarios to validate the analytic results that demonstrated that the bias can be properly estimated in practice. Worked examples are presented. Extensions to general adaptive designs and operational considerations are discussed. Copyright © 2017 John Wiley & Sons, Ltd.
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Affiliation(s)
- Wen Li
- Biostatistics and Research Decision Sciences, Merck Research Laboratories (MRL), Merck & Co., Inc, Kenilworth, NJ, U.S.A
| | - Cong Chen
- Biostatistics and Research Decision Sciences, Merck Research Laboratories (MRL), Merck & Co., Inc, Kenilworth, NJ, U.S.A
| | - Xiaoyun Li
- Biostatistics and Research Decision Sciences, Merck Research Laboratories (MRL), Merck & Co., Inc, Kenilworth, NJ, U.S.A
| | - Robert A Beckman
- Departments of Oncology and of Biostatistics, Bioinformatics, and Biomathematics, Lombardi Comprehensive Cancer Center and Innovation Center for Biomedical Informatics, Georgetown University Medical Center, 2115 Wisconsin Avenue, Suite 110, Washington, DC, 20007, U.S.A
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Adaptive Biomarker Population Selection in Phase III Confirmatory Trials with Time-to-Event Endpoints. STATISTICS IN BIOSCIENCES 2016. [DOI: 10.1007/s12561-016-9178-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Chen C, Li X(N, Yuan S, Antonijevic Z, Kalamegham R, Beckman RA. Statistical Design and Considerations of a Phase 3 Basket Trial for Simultaneous Investigation of Multiple Tumor Types in One Study. Stat Biopharm Res 2016. [DOI: 10.1080/19466315.2016.1193044] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Affiliation(s)
- Cong Chen
- Biostatistics and Research Decision Sciences, Merck Research Laboratories, Upper Gwynedd, PA, USA
| | - Xiaoyun (Nicole) Li
- Biostatistics and Research Decision Sciences, Merck Research Laboratories, Upper Gwynedd, PA, USA
| | - Shuai Yuan
- Biostatistics and Research Decision Sciences, Merck Research Laboratories, Upper Gwynedd, PA, USA
| | | | - Rasika Kalamegham
- American Association for Cancer Research, Office of Science Policy and Government Affairs, Washington, DC, USA
| | - Robert A. Beckman
- Departments of Oncology and of Biostatistics, Bioinformatics, and Biomathematics, Lombardi Comprehensive Cancer Center and Innovation Center for Biomedical Informatics, Georgetown University Medical Center, Washington, DC, USA
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Chen C, Li N, Shentu Y, Pang L, Beckman RA. Adaptive Informational Design of Confirmatory Phase III Trials With an Uncertain Biomarker Effect to Improve the Probability of Success. Stat Biopharm Res 2016. [DOI: 10.1080/19466315.2016.1173582] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Affiliation(s)
- Cong Chen
- Biostatistics and Research Decision Sciences, Merck Research Laboratories, Upper Gwynedd, PA, USA
| | - Nicole Li
- Biostatistics and Research Decision Sciences, Merck Research Laboratories, Upper Gwynedd, PA, USA
| | - Yue Shentu
- Biostatistics and Research Decision Sciences, Merck Research Laboratories, Upper Gwynedd, PA, USA
| | - Lei Pang
- Departments of Oncology and Biostatistics, Bioinformatics, and Biomathematics, Lombardi Comprehensive Cancer Center and Innovation Center for Biomedical Informatics, Georgetown University Medical Center, Washington, DC, USA
| | - Robert A. Beckman
- Departments of Oncology and Biostatistics, Bioinformatics, and Biomathematics, Lombardi Comprehensive Cancer Center and Innovation Center for Biomedical Informatics, Georgetown University Medical Center, Washington, DC, USA
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Yuan SS, Chen A, He L, Chen C, Gause CK, Beckman RA. On Group Sequential Enrichment Design for Basket Trial. Stat Biopharm Res 2016. [DOI: 10.1080/19466315.2016.1200999] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Affiliation(s)
- Shuai S. Yuan
- Biostatistics and Research Decision Sciences, Merck Research Laboratories (MRL), Kenilworth, NJ, USA
| | - Aiying Chen
- Biostatistics, Sanofi Pasteur, Swiftwater, PA, USA
| | - Li He
- Biostatistics and Research Decision Sciences, Merck Research Laboratories (MRL), Kenilworth, NJ, USA
| | - Cong Chen
- Biostatistics and Research Decision Sciences, Merck Research Laboratories (MRL), Kenilworth, NJ, USA
| | - Christine K. Gause
- Biostatistics and Research Decision Sciences, Merck Research Laboratories (MRL), Kenilworth, NJ, USA
| | - Robert A. Beckman
- Departments of Oncology and of Biostatistics, Bioinformatics, and Biomathematics, Lombardi Comprehensive Cancer Center and Innovation Center for Biomedical Informatics, Georgetown University Medical Center, Washington, DC, USA
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Spencer AV, Harbron C, Mander A, Wason J, Peers I. An adaptive design for updating the threshold value of a continuous biomarker. Stat Med 2016; 35:4909-4923. [PMID: 27417407 PMCID: PMC5378309 DOI: 10.1002/sim.7042] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2015] [Revised: 02/25/2016] [Accepted: 06/22/2016] [Indexed: 12/24/2022]
Abstract
Potential predictive biomarkers are often measured on a continuous scale, but in practice, a threshold value to divide the patient population into biomarker ‘positive’ and ‘negative’ is desirable. Early phase clinical trials are increasingly using biomarkers for patient selection, but at this stage, it is likely that little will be known about the relationship between the biomarker and the treatment outcome. We describe a single-arm trial design with adaptive enrichment, which can increase power to demonstrate efficacy within a patient subpopulation, the parameters of which are also estimated. Our design enables us to learn about the biomarker and optimally adjust the threshold during the study, using a combination of generalised linear modelling and Bayesian prediction. At the final analysis, a binomial exact test is carried out, allowing the hypothesis that ‘no population subset exists in which the novel treatment has a desirable response rate’ to be tested. Through extensive simulations, we are able to show increased power over fixed threshold methods in many situations without increasing the type-I error rate. We also show that estimates of the threshold, which defines the population subset, are unbiased and often more precise than those from fixed threshold studies. We provide an example of the method applied (retrospectively) to publically available data from a study of the use of tamoxifen after mastectomy by the German Breast Study Group, where progesterone receptor is the biomarker of interest.
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Affiliation(s)
- Amy V Spencer
- AstraZeneca, Global Medicines Development, Biometrics and Information Sciences, Mereside, Alderley Park, Macclesfield, SK10 4TG, U.K..
| | - Chris Harbron
- Roche Pharmaceuticals, 6 Falcon Way, Welwyn Garden City, AL7 1TW, U.K
| | - Adrian Mander
- MRC Biostatistics Unit Hub for Trials Methodology Research, Cambridge Institute of Public Health, Cambridge Biomedical Campus, Forvie Site, Robinson Way, Cambridge, CB2 0SR, U.K
| | - James Wason
- MRC Biostatistics Unit Hub for Trials Methodology Research, Cambridge Institute of Public Health, Cambridge Biomedical Campus, Forvie Site, Robinson Way, Cambridge, CB2 0SR, U.K
| | - Ian Peers
- AstraZeneca, Global Medicines Development, Biometrics and Information Sciences, Mereside, Alderley Park, Macclesfield, SK10 4TG, U.K
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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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Rosenblum M, Qian T, Du Y, Qiu H, Fisher A. Multiple testing procedures for adaptive enrichment designs: combining group sequential and reallocation approaches. Biostatistics 2016; 17:650-62. [DOI: 10.1093/biostatistics/kxw014] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2015] [Accepted: 01/27/2016] [Indexed: 11/14/2022] Open
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Parashar D, Bowden J, Starr C, Wernisch L, Mander A. An optimal stratified Simon two-stage design. Pharm Stat 2016; 15:333-40. [PMID: 26932771 PMCID: PMC5405342 DOI: 10.1002/pst.1742] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2014] [Revised: 10/21/2015] [Accepted: 01/18/2016] [Indexed: 01/02/2023]
Abstract
In Phase II oncology trials, therapies are increasingly being evaluated for their effectiveness in specific populations of interest. Such targeted trials require designs that allow for stratification based on the participants' molecular characterisation. A targeted design proposed by Jones and Holmgren (JH) Jones CL, Holmgren E: ‘An adaptive Simon two‐stage design for phase 2 studies of targeted therapies’, Contemporary Clinical Trials 28 (2007) 654‐661.determines whether a drug only has activity in a disease sub‐population or in the wider disease population. Their adaptive design uses results from a single interim analysis to decide whether to enrich the study population with a subgroup or not; it is based on two parallel Simon two‐stage designs. We study the JH design in detail and extend it by providing a few alternative ways to control the familywise error rate, in the weak sense as well as the strong sense. We also introduce a novel optimal design by minimising the expected sample size. Our extended design contributes to the much needed framework for conducting Phase II trials in stratified medicine. © 2016 The Authors Pharmaceutical Statistics Published by John Wiley & Sons Ltd
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Affiliation(s)
- Deepak Parashar
- Statistics and Epidemiology Unit, and Cancer Research Centre, Division of Health Sciences, University of Warwick, Coventry, UK
| | - Jack Bowden
- MRC Biostatistics Unit Hub for Trials Methodology Research, Coventry, UK
| | - Colin Starr
- MRC Biostatistics Unit Hub for Trials Methodology Research, Coventry, UK
| | - Lorenz Wernisch
- MRC Biostatistics Unit Hub for Trials Methodology Research, Coventry, UK
| | - Adrian Mander
- MRC Biostatistics Unit Hub for Trials Methodology Research, Coventry, UK
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43
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Comparison of different clinical development plans for confirmatory subpopulation selection. Contemp Clin Trials 2016; 47:78-84. [DOI: 10.1016/j.cct.2015.12.014] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2015] [Revised: 12/15/2015] [Accepted: 12/19/2015] [Indexed: 01/13/2023]
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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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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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Sugitani T, Bretz F, Maurer W. A simple and flexible graphical approach for adaptive group-sequential clinical trials. J Biopharm Stat 2014; 26:202-16. [PMID: 25372071 DOI: 10.1080/10543406.2014.972509] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
In this article, we introduce a graphical approach to testing multiple hypotheses in group-sequential clinical trials allowing for midterm design modifications. It is intended for structured study objectives in adaptive clinical trials and extends the graphical group-sequential designs from Maurer and Bretz (Statistics in Biopharmaceutical Research 2013; 5: 311-320) to adaptive trial designs. The resulting test strategies can be visualized graphically and performed iteratively. We illustrate the methodology with two examples from our clinical trial practice. First, we consider a three-armed gold-standard trial with the option to reallocate patients to either the test drug or the active control group, while stopping the recruitment of patients to placebo, after having demonstrated superiority of the test drug over placebo at an interim analysis. Second, we consider a confirmatory two-stage adaptive design with treatment selection at interim.
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Affiliation(s)
- Toshifumi Sugitani
- a Section for Medical Statistics, Medical University of Vienna , Vienna , Austria
| | - Frank Bretz
- b Novartis Pharma AG , Basel , Switzerland.,c Shanghai University of Finance and Economics , Shanghai , Peoples Republic of China
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Stallard N, Hamborg T, Parsons N, Friede T. Adaptive designs for confirmatory clinical trials with subgroup selection. J Biopharm Stat 2014; 24:168-87. [PMID: 24392984 DOI: 10.1080/10543406.2013.857238] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2013] [Accepted: 08/23/2013] [Indexed: 10/25/2022]
Abstract
Growing interest in stratified medicine is leading to increasing importance of subgroup analyses in confirmatory clinical trials. Conventionally, confirmatory clinical trials either focus on a subgroup identified in advance or assess subgroup effects once the trial is completed. The focus of this article is methodology for adaptive clinical trials that both identify whether a treatment is particularly effective in a predefined subgroup, potentially enabling alteration of recruitment, and assess the effectiveness in the subgroup and/or whole population. Methods for such adaptive trials are described and compared, and the logistical and regulatory issues associated with such approaches are discussed.
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Affiliation(s)
- Nigel Stallard
- a Warwick Medical School , University of Warwick , Coventry , United Kingdom
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