1
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Lu X, Shan G. Two-stage response adaptive randomization designs for multi-arm trials with binary outcome. J Biopharm Stat 2024; 34:526-538. [PMID: 37452825 PMCID: PMC10788381 DOI: 10.1080/10543406.2023.2234028] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Accepted: 07/01/2023] [Indexed: 07/18/2023]
Abstract
In recent years, adaptive randomization methods have gained significant popularity in clinical research and trial design due to their ability to provide both efficiency and flexibility in adjusting the statistical procedures of ongoing clinical trials. For a study to compare multiple treatments, a multi-arm two-stage design could be utilized to select the best treatment from the first stage and further compare that treatment with control in the second stage. The traditional design used equal randomization in both stages. To better utilize the interim results from the first stage, we propose to develop response adaptive randomization two-stage designs for a multi-arm clinical trial with binary outcome. Two allocation methods are considered: (1) an optimal allocation based on a sequential design; (2) the play-the-winner rule. Optimal multi-arm two-stage designs are obtained under three criteria: minimizing the expected number of failures, minimizing the average expected sample size, and minimizing the expected sample size under the null hypothesis. Simulation studies show that the proposed adaptive design based on the play-the-winner rule has good performance. A phase II trial for patients with pancreas adenocarcinoma and a germline BRCA/ PALB2 mutation was used to illustrate the application of the proposed response adaptive randomization designs.
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Affiliation(s)
- Xinlin Lu
- Department of Biostatistics, University of Florida, Gainesville FL, 32611
| | - Guogen Shan
- Department of Biostatistics, University of Florida, Gainesville FL, 32611
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2
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Yang X, Cheng Y, Thall PF, Wahed AS. A generalized outcome-adaptive sequential multiple assignment randomized trial design. Biometrics 2024; 80:ujae073. [PMID: 39106124 DOI: 10.1093/biomtc/ujae073] [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: 10/17/2023] [Revised: 04/18/2024] [Accepted: 07/22/2024] [Indexed: 08/09/2024]
Abstract
A dynamic treatment regime (DTR) is a mathematical representation of a multistage decision process. When applied to sequential treatment selection in medical settings, DTRs are useful for identifying optimal therapies for chronic diseases such as AIDs, mental illnesses, substance abuse, and many cancers. Sequential multiple assignment randomized trials (SMARTs) provide a useful framework for constructing DTRs and providing unbiased between-DTR comparisons. A limitation of SMARTs is that they ignore data from past patients that may be useful for reducing the probability of exposing new patients to inferior treatments. In practice, this may result in decreased treatment adherence or dropouts. To address this problem, we propose a generalized outcome-adaptive (GO) SMART design that adaptively unbalances stage-specific randomization probabilities in favor of treatments observed to be more effective in previous patients. To correct for bias induced by outcome adaptive randomization, we propose G-estimators and inverse-probability-weighted estimators of DTR effects embedded in a GO-SMART and show analytically that they are consistent. We report simulation results showing that, compared to a SMART, Response-Adaptive SMART and SMART with adaptive randomization, a GO-SMART design treats significantly more patients with the optimal DTR and achieves a larger number of total responses while maintaining similar or better statistical power.
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Affiliation(s)
- Xue Yang
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA 15261, United States
| | - Yu Cheng
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA 15261, United States
- Department of Statistics, University of Pittsburgh, Pittsburgh, PA 15261, United States
| | - Peter F Thall
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States
| | - Abdus S Wahed
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA 15261, United States
- Department of Biostatistics and Computational Biology, University of Rochester, Rochester, NY 14642, United States
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3
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Fabbri M, Rascol O, Foltynie T, Carroll C, Postuma RB, Porcher R, Corvol JC. Advantages and Challenges of Platform Trials for Disease Modifying Therapies in Parkinson's Disease. Mov Disord 2024. [PMID: 38925541 DOI: 10.1002/mds.29899] [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: 04/19/2024] [Revised: 05/27/2024] [Accepted: 06/03/2024] [Indexed: 06/28/2024] Open
Abstract
Traditional drug development in Parkinson's disease (PD) faces significant challenges because of its protracted timeline and high costs. In response, innovative master protocols are emerging and designed to address multiple research questions within a single overarching protocol. These trials may offer advantages such as increased efficiency, agility in adding new treatment arms, and potential cost savings. However, they also present organizational, methodological, funding, regulatory, and sponsorship challenges. We review the potential of master protocols, focusing on platform trials, for disease modifying therapies in PD. These trials share a common control group and allow for the termination or addition of treatment arms during a trial with non-predetermined end. Specific issues exist for a platform trial in the PD field considering the heterogeneity of patients in terms of phenotype, genotype and staging, the confounding effects of symptomatic treatments, and the choice of outcome measures with no consensus on a non-clinical biomarker to serve as a surrogate and the slowness of PD progression. We illustrate these aspects using the examples of the main PD platform trials currently in development with each one targeting distinct goals, populations, and outcomes. Overall, platform trials hold promise in expediting the evaluation of potential therapies for PD. However, it remains to be proven whether these theoretical benefits will translate into increased production of high-quality trial data. Success also depends on the willingness of pharmaceutical companies to engage in such trials and whether this approach will ultimately hasten the identification and licensing of effective disease-modifying drugs. © 2024 International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Margherita Fabbri
- Department of Clinical Pharmacology and Neurosciences, Clinical Investigation Center CIC1436, Toulouse Parkinson Expert Center, Toulouse NeuroToul Center of Excellence in Neurodegeneration (COEN), French NS-Park/F-CRIN Network, University of Toulouse 3, CHU of Toulouse, INSERM, Toulouse, France
| | - Olivier Rascol
- Department of Clinical Pharmacology and Neurosciences, Clinical Investigation Center CIC1436, Toulouse Parkinson Expert Center, Toulouse NeuroToul Center of Excellence in Neurodegeneration (COEN), French NS-Park/F-CRIN Network, University of Toulouse 3, CHU of Toulouse, INSERM, Toulouse, France
| | - Tom Foltynie
- Department of Clinical and Movement Neurosciences, UCL Institute of Neurology, London, United Kingdom
| | - Camille Carroll
- Translational and Clinical Research Institute, Newcastle University, Newcastle, United Kingdom
| | - Ronald B Postuma
- Department of Neurology and Neurosurgery, McGill University, Montreal Neurological Institute, Montreal, Quebec, Canada
| | - Raphael Porcher
- Université Paris Cité and Université Sorbonne Paris Nord, INSERM, INRAE, Center for Research in Epidemiology and StatisticS (CRESS), Paris, France
- Center for Clinical Epidemiology, Assistance Publique-Hôpitaux de Paris, Hôtel-Dieu Hospital, Paris, France
| | - Jean Christophe Corvol
- Sorbonne Université, Institut du Cerveau-Paris Brain Institute - ICM, Assistance Publique Hôpitaux de Paris, Inserm, CNRS, Department of Neurology, CIC Neurosciences, Hôpital Pitié-Salpêtrière, French NS-Park/F-CRIN Network, Paris, France
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4
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Yuan Y, Zhou H, Liu S. Statistical and practical considerations in planning and conduct of dose-optimization trials. Clin Trials 2024; 21:273-286. [PMID: 38243399 PMCID: PMC11134987 DOI: 10.1177/17407745231207085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2024]
Abstract
The U.S. Food and Drug Administration launched Project Optimus with the aim of shifting the paradigm of dose-finding and selection toward identifying the optimal biological dose that offers the best balance between benefit and risk, rather than the maximum tolerated dose. However, achieving dose optimization is a challenging task that involves a variety of factors and is considerably more complicated than identifying the maximum tolerated dose, both in terms of design and implementation. This article provides a comprehensive review of various design strategies for dose-optimization trials, including phase 1/2 and 2/3 designs, and highlights their respective advantages and disadvantages. In addition, practical considerations for selecting an appropriate design and planning and executing the trial are discussed. The article also presents freely available software tools that can be utilized for designing and implementing dose-optimization trials. The approaches and their implementation are illustrated through real-world examples.
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Affiliation(s)
- Ying Yuan
- Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Heng Zhou
- Biostatistics and Research Decision Sciences, Merck and Co., Inc, Rahway, NJ, USA
| | - Suyu Liu
- Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, TX, USA
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5
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Granholm A, Lange T, Harhay MO, Jensen AKG, Perner A, Møller MH, Kaas-Hansen BS. Effects of duration of follow-up and lag in data collection on the performance of adaptive clinical trials. Pharm Stat 2024; 23:138-150. [PMID: 37837271 PMCID: PMC10935606 DOI: 10.1002/pst.2342] [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: 05/12/2023] [Revised: 08/07/2023] [Accepted: 10/03/2023] [Indexed: 10/15/2023]
Abstract
Different combined outcome-data lags (follow-up durations plus data-collection lags) may affect the performance of adaptive clinical trial designs. We assessed the influence of different outcome-data lags (0-105 days) on the performance of various multi-stage, adaptive trial designs (2/4 arms, with/without a common control, fixed/response-adaptive randomisation) with undesirable binary outcomes according to different inclusion rates (3.33/6.67/10 patients/day) under scenarios with no, small, and large differences. Simulations were conducted under a Bayesian framework, with constant stopping thresholds for superiority/inferiority calibrated to keep type-1 error rates at approximately 5%. We assessed multiple performance metrics, including mean sample sizes, event counts/probabilities, probabilities of conclusiveness, root mean squared errors (RMSEs) of the estimated effect in the selected arms, and RMSEs between the analyses at the time of stopping and the final analyses including data from all randomised patients. Performance metrics generally deteriorated when the proportions of randomised patients with available data were smaller due to longer outcome-data lags or faster inclusion, that is, mean sample sizes, event counts/probabilities, and RMSEs were larger, while the probabilities of conclusiveness were lower. Performance metric impairments with outcome-data lags ≤45 days were relatively smaller compared to those occurring with ≥60 days of lag. For most metrics, the effects of different outcome-data lags and lower proportions of randomised patients with available data were larger than those of different design choices, for example, the use of fixed versus response-adaptive randomisation. Increased outcome-data lag substantially affected the performance of adaptive trial designs. Trialists should consider the effects of outcome-data lags when planning adaptive trials.
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Affiliation(s)
- Anders Granholm
- Department of Intensive Care 4131, Copenhagen University
Hospital – Rigshospitalet, Copenhagen, Denmark
| | - Theis Lange
- Section of Biostatistics, Department of Public Health,
University of Copenhagen, Copenhagen, Denmark
| | - Michael O. Harhay
- Clinical Trials Methods and Outcomes Lab, PAIR (Palliative
and Advanced Illness Research) Center, Perelman School of Medicine, University of
Pennsylvania, Philadelphia, USA
- Department of Biostatistics, Epidemiology, and Informatics,
Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Aksel Karl Georg Jensen
- Section of Biostatistics, Department of Public Health,
University of Copenhagen, Copenhagen, Denmark
| | - Anders Perner
- Department of Intensive Care 4131, Copenhagen University
Hospital – Rigshospitalet, Copenhagen, Denmark
| | - Morten Hylander Møller
- Department of Intensive Care 4131, Copenhagen University
Hospital – Rigshospitalet, Copenhagen, Denmark
| | - Benjamin Skov Kaas-Hansen
- Department of Intensive Care 4131, Copenhagen University
Hospital – Rigshospitalet, Copenhagen, Denmark
- Section of Biostatistics, Department of Public Health,
University of Copenhagen, Copenhagen, Denmark
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6
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Su L, Chen X, Zhang J, Yan F. MIDAS-2: an enhanced Bayesian platform design for immunotherapy combinations with subgroup efficacy exploration. J Biopharm Stat 2023:1-21. [PMID: 38131109 DOI: 10.1080/10543406.2023.2292211] [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: 02/09/2023] [Accepted: 12/02/2023] [Indexed: 12/23/2023]
Abstract
Although immunotherapy combinations have revolutionised cancer treatment, the rapid screening of effective and optimal therapies from large numbers of candidate combinations, as well as exploring subgroup efficacy, remains challenging. This necessitates innovative, integrated, and efficient trial designs. In this study, we extend the MIDAS design to include subgroup exploration and propose an enhanced Bayesian information borrowing platform design called MIDAS-2. MIDAS-2 enables quick and continuous screening of promising combination strategies and exploration of their subgroup effects within a unified platform design framework. We use a regression model to characterize the efficacy pattern in subgroups. Information borrowing is applied through Bayesian hierarchical modelling to improve trial efficiency considering the limited sample size in subgroups. Time trend calibration is also employed to avoid potential baseline drifts. Simulation results demonstrate that MIDAS-2 yields high probabilities for identifying the effective drug combinations as well as promising subgroups, facilitating appropriate selection of the best treatments for each subgroup. The proposed design is robust against small time trend drifts, and the type I error is successfully controlled after calibration when a large drift is expected. Overall, MIDAS-2 provides an adaptive drug screening and subgroup exploring framework to accelerate immunotherapy development in an efficient, accurate, and integrated fashion.
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Affiliation(s)
- Liwen Su
- Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, China
| | - 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
| | - Fangrong Yan
- Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, China
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7
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VanBuren JM, Hall M, Zuppa AF, Mourani PM, Carcillo J, Dean JM, Watt K, Holubkov R. The Design of Nested Adaptive Clinical Trials of Multiple Organ Dysfunction Syndrome Children in a Single Study. Pediatr Crit Care Med 2023; 24:e635-e646. [PMID: 37498156 PMCID: PMC10817996 DOI: 10.1097/pcc.0000000000003332] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/28/2023]
Abstract
OBJECTIVES Describe the statistical design of the Personalized Immunomodulation in Sepsis-induced Multiple Organ Dysfunction Syndrome (MODS) (PRECISE) study. DESIGN Children with sepsis-induced MODS undergo real-time immune testing followed by assignment to an immunophenotype-specific study cohort. Interventional cohorts include the granulocyte macrophage-colony stimulating factor (GM-CSF) for the Reversal of Immunoparalysis in Pediatric Sepsis-induced MODS (GRACE)-2 trial, which uses the drug GM-CSF (or placebo) to reverse immunoparalysis; and the Targeted Reversal of Inflammation in Pediatric Sepsis-induced MODS (TRIPS) trial, which uses the drug anakinra (or placebo) to reverse systemic inflammation. Both trials have adaptive components and use a statistical framework in which frequent data monitoring assesses futility and efficacy, allowing potentially earlier stopping than traditional approaches. Prespecified simulation-based stopping boundaries are customized to each trial to preserve an overall one-sided type I error rate. The TRIPS trial also uses response-adaptive randomization, updating randomization allocation proportions to favor active arms that appear more efficacious based on accumulating data. SETTING Twenty-four U.S. academic PICUs. PATIENTS Septic children with specific immunologic derangements during ongoing dysfunction of at least two organs. INTERVENTIONS The GRACE-2 trial compares GM-CSF and placebo in children with immunoparalysis. The TRIPS trial compares four different doses of anakinra to placebo in children with moderate to severe systemic inflammation. MEASUREMENTS AND MAIN RESULTS Both trials assess primary efficacy using the sum of the daily pediatric logistic organ dysfunction-2 score over 28 days. Ranked summed scores, with mortality assigned the worst possible value, are compared between arms using the Wilcoxon Rank Sum test (GRACE-2) and a dose-response curve (TRIPS). We present simulation-based operating characteristics under several scenarios to demonstrate the behavior of the adaptive design. CONCLUSIONS The adaptive design incorporates innovative statistical features that allow for multiple active arms to be compared with placebo based on a child's personal immunophenotype. The design increases power and provides optimal operating characteristics compared with traditional conservative methods.
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Affiliation(s)
- John M VanBuren
- Department of Pediatrics, University of Utah, Salt Lake City, UT
| | - Mark Hall
- Department of Pediatrics, Division of Critical Care Medicine, Nationwide Children's Hospital, Columbus, OH
| | - Athena F Zuppa
- Department of Anesthesia and Critical Care, Division of Critical Care Medicine, Children's Hospital of Philadelphia, Philadelphia, PA
| | - Peter M Mourani
- Department of Pediatrics, Division of Critical Care Medicine, University of Arkansas for Medical Sciences and Arkansas Children's Research Institute, Little Rock, AR
| | - Joseph Carcillo
- Department of Critical Care Medicine and Pediatrics, University of Pittsburgh, Children's Hospital of Pittsburgh, UPMC Children's Hospital of Pittsburgh, Pittsburgh, PA
| | - J Michael Dean
- Department of Pediatrics, University of Utah, Salt Lake City, UT
| | - Kevin Watt
- Department of Pediatrics, University of Utah, Salt Lake City, UT
| | - Richard Holubkov
- Department of Pediatrics, University of Utah, Salt Lake City, UT
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8
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Lee J, Thall PF, Msaouel P. Bayesian treatment screening and selection using subgroup-specific utilities of response and toxicity. Biometrics 2023; 79:2458-2473. [PMID: 35974457 PMCID: PMC9931950 DOI: 10.1111/biom.13738] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2021] [Accepted: 08/04/2022] [Indexed: 11/26/2022]
Abstract
A Bayesian design is proposed for randomized phase II clinical trials that screen multiple experimental treatments compared to an active control based on ordinal categorical toxicity and response. The underlying model and design account for patient heterogeneity characterized by ordered prognostic subgroups. All decision criteria are subgroup specific, including interim rules for dropping unsafe or ineffective treatments, and criteria for selecting optimal treatments at the end of the trial. The design requires an elicited utility function of the two outcomes that varies with the subgroups. Final treatment selections are based on posterior mean utilities. The methodology is illustrated by a trial of targeted agents for metastatic renal cancer, which motivated the design methodology. In the context of this application, the design is evaluated by computer simulation, including comparison to three designs that conduct separate trials within subgroups, or conduct one trial while ignoring subgroups, or base treatment selection on estimated response rates while ignoring toxicity.
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Affiliation(s)
- Juhee Lee
- Department of Statistics, Baskin School of Engineering, University of California Santa Cruz, Santa Cruz, California, USA
| | - Peter F Thall
- Department of Biostatistics, M.D. Anderson Cancer Center, Houston, Texas, USA
| | - Pavlos Msaouel
- Departments of Genitourinary Medical Oncology and Translational Molecular Pathology, M.D. Anderson Cancer Center, Houston, Texas, USA
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9
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Vinnat V, Chiche JD, Demoule A, Chevret S. Simulation study for evaluating an adaptive-randomisation Bayesian hybrid trial design with enrichment. Contemp Clin Trials Commun 2023; 33:101141. [PMID: 37397429 PMCID: PMC10313856 DOI: 10.1016/j.conctc.2023.101141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Revised: 03/22/2023] [Accepted: 04/12/2023] [Indexed: 07/04/2023] Open
Abstract
Background As we enter the era of precision medicine, the role of adaptive designs, such as response-adaptive randomisation or enrichment designs in drug discovery and development, has become increasingly important to identify the treatment given to a patient based on one or more biomarkers. Tailoring the ventilation supply technique according to the responsiveness of patients to positive end-expiratory pressure is a suitable setting for such a design. Methods In the setting of marker-strategy design, we propose a Bayesian response-adaptive randomisation with enrichment design based on group sequential analyses. This design combines the elements of enrichment design and response-adaptive randomisation. Concerning the enrichment strategy, Bayesian treatment-by-subset interaction measures were used to adaptively enrich the patients most likely to benefit from an experimental treatment while controlling the false-positive rate.The operating characteristics of the design were assessed by simulation and compared to those of alternate designs. Results The results obtained allowed the detection of the superiority of one treatment over another and the presence of a treatment-by-subgroup interaction while keeping the false-positive rate at approximately 5\% and reducing the average number of included patients. In addition, simulation studies identified that the number of interim analyses and the burn-in period may have an impact on the performance of the scheme. Conclusion The proposed design highlights important objectives of precision medicine, such as determining whether the experimental treatment is superior to another and identifying wheter such an efficacy could depend on patient profile.
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Affiliation(s)
- Valentin Vinnat
- ECSTRRA team, INSERM U1153, Université Paris Cité, Paris, France
| | - Jean-Daniel Chiche
- Service de médecine intensive adulte, Centre Hospitalier Universitaire Vaudois, Lausanne, Switzerland
| | - Alexandre Demoule
- Sorbonne Université, INSERM, UMRS1158 Neurophysiologie Respiratoire Expérimentale et Clinique, France
- AP-HP, Groupe Hospitalier Universitaire APHP-Sorbonne Université, site Pitié-Salpêtrière, Service de Médecine Intensive et Réanimation (Département R3S), Paris, France
| | - Sylvie Chevret
- ECSTRRA team, INSERM U1153, Université Paris Cité, Paris, France
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10
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Robertson DS, Lee KM, López-Kolkovska BC, Villar SS. Response-adaptive randomization in clinical trials: from myths to practical considerations. Stat Sci 2023; 38:185-208. [PMID: 37324576 PMCID: PMC7614644 DOI: 10.1214/22-sts865] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Response-Adaptive Randomization (RAR) is part of a wider class of data-dependent sampling algorithms, for which clinical trials are typically used as a motivating application. In that context, patient allocation to treatments is determined by randomization probabilities that change based on the accrued response data in order to achieve experimental goals. RAR has received abundant theoretical attention from the biostatistical literature since the 1930's and has been the subject of numerous debates. In the last decade, it has received renewed consideration from the applied and methodological communities, driven by well-known practical examples and its widespread use in machine learning. Papers on the subject present different views on its usefulness, and these are not easy to reconcile. This work aims to address this gap by providing a unified, broad and fresh review of methodological and practical issues to consider when debating the use of RAR in clinical trials.
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Affiliation(s)
- David S. Robertson
- MRC Biostatistics Unit, University of Cambridge, Forvie Site, Robinson Way, Cambridge CB2 0SR, United Kingdom
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11
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Giovagnoli A, Verdinelli I. Bayesian Adaptive Randomization with Compound Utility Functions. Stat Sci 2023. [DOI: 10.1214/21-sts848] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Affiliation(s)
- Alessandra Giovagnoli
- Alessandra Giovagnoli is retired Professor, Department of Statistical Sciences, Alma Mater Studiorum, Università di Bologna, Bologna, Italy
| | - Isabella Verdinelli
- Isabella Verdinelli is Professor in Residence, Department of Statistics, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA
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12
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Granholm A, Kaas-Hansen BS, Lange T, Schjørring OL, Andersen LW, Perner A, Jensen AKG, Møller MH. An overview of methodological considerations regarding adaptive stopping, arm dropping, and randomization in clinical trials. J Clin Epidemiol 2023; 153:45-54. [PMID: 36400262 DOI: 10.1016/j.jclinepi.2022.11.002] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 10/17/2022] [Accepted: 11/02/2022] [Indexed: 11/18/2022]
Abstract
BACKGROUND AND OBJECTIVES Adaptive features may increase flexibility and efficiency of clinical trials, and improve participants' chances of being allocated to better interventions. Our objective is to provide thorough guidance on key methodological considerations for adaptive clinical trials. METHODS We provide an overview of key methodological considerations for clinical trials employing adaptive stopping, adaptive arm dropping, and response-adaptive randomization. We cover pros and cons of different decisions and provide guidance on using simulation to compare different adaptive trial designs. We focus on Bayesian multi-arm adaptive trials, although the same general considerations apply to frequentist adaptive trials. RESULTS We provide guidance on 1) interventions and possible common control, 2) outcome selection, follow-up duration and model choice, 3) timing of adaptive analyses, 4) decision rules for adaptive stopping and arm dropping, 5) randomization strategies, 6) performance metrics, their prioritization, and arm selection strategies, and 7) simulations, assessment of performance under different scenarios, and reporting. Finally, we provide an example using a newly developed R simulation engine that may be used to evaluate and compare different adaptive trial designs. CONCLUSION This overview may help trialists design better and more transparent adaptive clinical trials and to adequately compare them before initiation.
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Affiliation(s)
- Anders Granholm
- Department of Intensive Care, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark.
| | - Benjamin Skov Kaas-Hansen
- Department of Intensive Care, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark; Section of Biostatistics, Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Theis Lange
- Section of Biostatistics, Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Olav Lilleholt Schjørring
- Department of Anaesthesia and Intensive Care, Aalborg University Hospital, Aalborg, Denmark; Department of Clinical Medicine, Aalborg University, Aalborg, Denmark
| | - Lars W Andersen
- Research Center for Emergency Medicine, Department of Clinical Medicine, Aarhus University and Aarhus University Hospital, Aarhus, Denmark; Department of Anesthesiology and Intensive Care, Aarhus University Hospital, Aarhus, Denmark; Prehospital Emergency Medical Services, Central Denmark Region, Aarhus, Denmark
| | - Anders Perner
- Department of Intensive Care, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark
| | - Aksel Karl Georg Jensen
- Section of Biostatistics, Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Morten Hylander Møller
- Department of Intensive Care, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark
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13
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Mavrogonatou L, Sun Y, Robertson DS, Villar SS. A comparison of allocation strategies for optimising clinical trial designs under variance heterogeneity. Comput Stat Data Anal 2022. [DOI: 10.1016/j.csda.2022.107559] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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14
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Ryan EG, Couturier DL, Heritier S. Bayesian adaptive clinical trial designs for respiratory medicine. Respirology 2022; 27:834-843. [PMID: 35918280 PMCID: PMC9544135 DOI: 10.1111/resp.14337] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Accepted: 07/19/2022] [Indexed: 01/20/2023]
Abstract
The use of Bayesian adaptive designs for clinical trials has increased in recent years, particularly during the COVID‐19 pandemic. Bayesian adaptive designs offer a flexible and efficient framework for conducting clinical trials and may provide results that are more useful and natural to interpret for clinicians, compared to traditional approaches. In this review, we provide an introduction to Bayesian adaptive designs and discuss its use in recent clinical trials conducted in respiratory medicine. We illustrate this approach by constructing a Bayesian adaptive design for a multi‐arm trial that compares two non‐invasive ventilation treatments to standard oxygen therapy for patients with acute cardiogenic pulmonary oedema. We highlight the benefits and some of the challenges involved in designing and implementing Bayesian adaptive trials.
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Affiliation(s)
- Elizabeth G Ryan
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Dominique-Laurent Couturier
- Cancer Research UK - Cambridge Institute, University of Cambridge, Cambridge, UK.,Medical Research Council Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - Stephane Heritier
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
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Racine-Poon A, D’Amelio A, Sverdlov O, Haas T. OPTIM-ARTS—An Adaptive Phase II Open Platform Trial Design With Application to a Metastatic Melanoma Study. Stat Biopharm Res 2022. [DOI: 10.1080/19466315.2020.1749722] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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16
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Proper J, Murray TA. An alternative metric for evaluating the potential patient benefit of response-adaptive randomization procedures. Biometrics 2022. [PMID: 35394063 DOI: 10.1111/biom.13673] [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: 03/12/2021] [Accepted: 03/31/2022] [Indexed: 11/27/2022]
Abstract
When planning a two-arm group sequential clinical trial with a binary primary outcome that has severe implications for quality of life (e.g., mortality), investigators may strive to find the design that maximizes in-trial patient benefit. In such cases, Bayesian response-adaptive randomization (BRAR) is often considered because it can alter the allocation ratio throughout the trial in favor of the treatment that is currently performing better. Although previous studies have recommended using fixed randomization over BRAR based on patient benefit metrics calculated from the realized trial sample size, these previous comparisons have been limited by failures to hold type I and II error rates constant across designs or consider the impacts on all individuals directly affected by the design choice. In this paper, we propose a metric for comparing designs with the same type I and II error rates that reflects expected outcomes among individuals who would participate in the trial if enrollment is open when they become eligible. We demonstrate how to use the proposed metric to guide the choice of design in the context of two recent trials in persons suffering out of hospital cardiac arrest. Using computer simulation, we demonstrate that various implementations of group sequential BRAR offer modest improvements with respect to the proposed metric relative to conventional group sequential monitoring alone. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Jennifer Proper
- Department of Biostatistics, University of Minnesota Twin Cities, Minneapolis, Minnesota, U.S.A
| | - Thomas A Murray
- Department of Biostatistics, University of Minnesota Twin Cities, Minneapolis, Minnesota, U.S.A
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17
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Park Y. Personalized Risk-Based Screening Design for Comparative Two-Arm Group Sequential Clinical Trials. J Pers Med 2022; 12:448. [PMID: 35330448 PMCID: PMC8953575 DOI: 10.3390/jpm12030448] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2022] [Revised: 03/07/2022] [Accepted: 03/10/2022] [Indexed: 11/16/2022] Open
Abstract
Personalized medicine has been emerging to take into account individual variability in genes and environment. In the era of personalized medicine, it is critical to incorporate the patients' characteristics and improve the clinical benefit for patients. The patients' characteristics are incorporated in adaptive randomization to identify patients who are expected to get more benefit from the treatment and optimize the treatment allocation. However, it is challenging to control potential selection bias from using observed efficacy data and the effect of prognostic covariates in adaptive randomization. This paper proposes a personalized risk-based screening design using Bayesian covariate-adjusted response-adaptive randomization that compares the experimental screening method to a standard screening method based on indicators of having a disease. Personalized risk-based allocation probability is built for adaptive randomization, and Bayesian adaptive decision rules are calibrated to preserve error rates. A simulation study shows that the proposed design controls error rates and yields a much smaller number of failures and a larger number of patients allocated to a better intervention compared to existing randomized controlled trial designs. Therefore, the proposed design performs well for randomized controlled clinical trials under personalized medicine.
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Affiliation(s)
- Yeonhee Park
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53705, USA
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18
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Arjas E, Gasbarra D. Adaptive treatment allocation and selection in multi-arm clinical trials: a Bayesian perspective. BMC Med Res Methodol 2022; 22:50. [PMID: 35184731 PMCID: PMC8858379 DOI: 10.1186/s12874-022-01526-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Accepted: 01/19/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Adaptive designs offer added flexibility in the execution of clinical trials, including the possibilities of allocating more patients to the treatments that turned out more successful, and early stopping due to either declared success or futility. Commonly applied adaptive designs, such as group sequential methods, are based on the frequentist paradigm and on ideas from statistical significance testing. Interim checks during the trial will have the effect of inflating the Type 1 error rate, or, if this rate is controlled and kept fixed, lowering the power. RESULTS The purpose of the paper is to demonstrate the usefulness of the Bayesian approach in the design and in the actual running of randomized clinical trials during phase II and III. This approach is based on comparing the performance of the different treatment arms in terms of the respective joint posterior probabilities evaluated sequentially from the accruing outcome data, and then taking a control action if such posterior probabilities fall below a pre-specified critical threshold value. Two types of actions are considered: treatment allocation, putting on hold at least temporarily further accrual of patients to a treatment arm, and treatment selection, removing an arm from the trial permanently. The main development in the paper is in terms of binary outcomes, but extensions for handling time-to-event data, including data from vaccine trials, are also discussed. The performance of the proposed methodology is tested in extensive simulation experiments, with numerical results and graphical illustrations documented in a Supplement to the main text. As a companion to this paper, an implementation of the methods is provided in the form of a freely available R package 'barts'. CONCLUSION The proposed methods for trial design provide an attractive alternative to their frequentist counterparts.
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Affiliation(s)
- Elja Arjas
- University of Helsinki, Helsinki, Finland.
- University of Oslo, Oslo, Norway.
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19
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Randomised clinical trials in critical care: past, present and future. Intensive Care Med 2021; 48:164-178. [PMID: 34853905 PMCID: PMC8636283 DOI: 10.1007/s00134-021-06587-9] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 11/17/2021] [Indexed: 12/15/2022]
Abstract
Randomised clinical trials (RCTs) are the gold standard for providing unbiased evidence of intervention effects. Here, we provide an overview of the history of RCTs and discuss the major challenges and limitations of current critical care RCTs, including overly optimistic effect sizes; unnuanced conclusions based on dichotomization of results; limited focus on patient-centred outcomes other than mortality; lack of flexibility and ability to adapt, increasing the risk of inconclusive results and limiting knowledge gains before trial completion; and inefficiency due to lack of re-use of trial infrastructure. We discuss recent developments in critical care RCTs and novel methods that may provide solutions to some of these challenges, including a research programme approach (consecutive, complementary studies of multiple types rather than individual, independent studies), and novel design and analysis methods. These include standardization of trial protocols; alternative outcome choices and use of core outcome sets; increased acceptance of uncertainty, probabilistic interpretations and use of Bayesian statistics; novel approaches to assessing heterogeneity of treatment effects; adaptation and platform trials; and increased integration between clinical trials and clinical practice. We outline the advantages and discuss the potential methodological and practical disadvantages with these approaches. With this review, we aim to inform clinicians and researchers about conventional and novel RCTs, including the rationale for choosing one or the other methodological approach based on a thorough discussion of pros and cons. Importantly, the most central feature remains the randomisation, which provides unparalleled restriction of confounding compared to non-randomised designs by reducing confounding to chance.
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20
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VanBuren JM, Casper TC, Nishijima DK, Kuppermann N, Lewis RJ, Dean JM, McGlothlin A. The design of a Bayesian adaptive clinical trial of tranexamic acid in severely injured children. Trials 2021; 22:769. [PMID: 34736498 PMCID: PMC8567588 DOI: 10.1186/s13063-021-05737-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Accepted: 10/20/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Trauma is the leading cause of death and disability in children in the USA. Tranexamic acid (TXA) reduces the blood transfusion requirements in adults and children during surgery. Several studies have evaluated TXA in adults with hemorrhagic trauma, but no randomized controlled trials have occurred in children with trauma. We propose a Bayesian adaptive clinical trial to investigate TXA in children with brain and/or torso hemorrhagic trauma. METHODS/DESIGN We designed a double-blind, Bayesian adaptive clinical trial that will enroll up to 2000 patients. We extend the traditional Emax dose-response model to incorporate a hierarchical structure so multiple doses of TXA can be evaluated in different injury populations (isolated head injury, isolated torso injury, or both head and torso injury). Up to 3 doses of TXA (15 mg/kg, 30 mg/kg, and 45 mg/kg bolus doses) will be compared to placebo. Equal allocation between placebo, 15 mg/kg, and 30 mg/kg will be used for an initial period within each injury group. Depending on the dose-response curve, the 45 mg/kg arm may open in an injury group if there is a trend towards increasing efficacy based on the observed relationship using the data from the lower doses. Response-adaptive randomization allows each injury group to differ in allocation proportions of TXA so an optimal dose can be identified for each injury group. Frequent interim stopping periods are included to evaluate efficacy and futility. The statistical design is evaluated through extensive simulations to determine the operating characteristics in several plausible scenarios. This trial achieves adequate power in each injury group. DISCUSSION This trial design evaluating TXA in pediatric hemorrhagic trauma allows for three separate injury populations to be analyzed and compared within a single study framework. Individual conclusions regarding optimal dosing of TXA can be made within each injury group. Identifying the optimal dose of TXA, if any, for various injury types in childhood may reduce death and disability.
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Affiliation(s)
- John M. VanBuren
- Department of Pediatrics, University of Utah School of Medicine, 295 Chipeta Way, Salt Lake City, UT 84108 USA
| | - T. Charles Casper
- Department of Pediatrics, University of Utah School of Medicine, 295 Chipeta Way, Salt Lake City, UT 84108 USA
| | - Daniel K. Nishijima
- Department of Emergency Medicine, University of California, Davis School of Medicine, Sacramento, CA 95817 USA
| | - Nathan Kuppermann
- Department of Emergency Medicine, University of California, Davis School of Medicine, Sacramento, CA 95817 USA
- Department of Pediatrics, University of California, Davis School of Medicine, Sacramento, CA 95817 USA
| | - Roger J. Lewis
- Department of Emergency Medicine, Harbor-UCLA Medical Center, Torrance, CA 90509 USA
- Berry Consultants, LLC, Austin, TX 78746 USA
| | - J. Michael Dean
- Department of Pediatrics, University of Utah School of Medicine, 295 Chipeta Way, Salt Lake City, UT 84108 USA
| | | | - For the TIC-TOC Collaborators of the Pediatric Emergency Care Applied Research Network (PECARN)
- Department of Pediatrics, University of Utah School of Medicine, 295 Chipeta Way, Salt Lake City, UT 84108 USA
- Department of Emergency Medicine, University of California, Davis School of Medicine, Sacramento, CA 95817 USA
- Department of Pediatrics, University of California, Davis School of Medicine, Sacramento, CA 95817 USA
- Department of Emergency Medicine, Harbor-UCLA Medical Center, Torrance, CA 90509 USA
- Berry Consultants, LLC, Austin, TX 78746 USA
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21
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Practical Considerations and Recommendations for Master Protocol Framework: Basket, Umbrella and Platform Trials. Ther Innov Regul Sci 2021; 55:1145-1154. [PMID: 34160785 PMCID: PMC8220876 DOI: 10.1007/s43441-021-00315-7] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2021] [Accepted: 06/07/2021] [Indexed: 11/05/2022]
Abstract
Master protocol, categorized as basket trial, umbrella trial or platform trial, is an innovative clinical trial framework that aims to expedite clinical drug development, enhance trial efficiency, and eventually bring medicines to patients faster. Despite a clear uptake on the advantages in the concepts and designs, master protocols are still yet to be widely used. Part of that may be due to the fact that the master protocol framework comes with the need for new statistical designs and considerations for analyses and operational challenges. In this article, we provide an overview of the master protocol framework, unify the definitions with some examples, review the statistical methods for the designs and analyses, and focus our discussions on some practical considerations and recommendations of master protocols to help practitioners better design and implement such studies.
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22
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Wang J, Wu L, Wahed AS. Adaptive randomization in a two-stage sequential multiple assignment randomized trial. Biostatistics 2021; 23:1182-1199. [PMID: 34052847 DOI: 10.1093/biostatistics/kxab020] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Revised: 04/22/2021] [Accepted: 04/27/2021] [Indexed: 11/13/2022] Open
Abstract
Sequential multiple assignment randomized trials (SMARTs) are systematic and efficient media for comparing dynamic treatment regimes (DTRs), where each patient is involved in multiple stages of treatment with the randomization at each stage depending on the patient's previous treatment history and interim outcomes. Generally, patients enrolled in SMARTs are randomized equally to ethically acceptable treatment options regardless of how effective those treatments were during the previous stages, which results in some undesirable consequences in practice, such as low recruitment, less retention, and lower treatment adherence. In this article, we propose a response-adaptive SMART (RA-SMART) design where the allocation probabilities are imbalanced in favor of more promising treatments based on the accumulated information on treatment efficacy from previous patients and stages. The operating characteristics of the RA-SMART design relative to SMART design, including the consistency and efficiency of estimated response rate under each DTR, the power of identifying the optimal DTR, and the number of patients treated with the optimal and the worst DTRs, are assessed through extensive simulation studies. Some practical suggestions are discussed in the conclusion.
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Affiliation(s)
- Junyao Wang
- Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, 130 De Soto Street, Pittsburgh, PA 15213, USA
| | - Liwen Wu
- Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, 130 De Soto Street, Pittsburgh, PA 15213, USA
| | - Abdus S Wahed
- Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, 130 De Soto Street, Pittsburgh, PA 15213, USA
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23
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Pohl M, Krisam J, Kieser M. Categories, components, and techniques in a modular construction of basket trials for application and further research. Biom J 2021; 63:1159-1184. [PMID: 33942894 DOI: 10.1002/bimj.202000314] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Revised: 02/15/2021] [Accepted: 04/30/2021] [Indexed: 12/24/2022]
Abstract
Basket trials have become a virulent topic in medical and statistical research during the last decade. The core idea of them is to treat patients, who express the same genetic predisposition-either personally or their disease-with the same treatment irrespective of the location of the disease. The location of the disease defines each basket and the pathway of the treatment uses the common genetic predisposition among the baskets. This opens the opportunity to share information among baskets, which can consequently increase the information of the basket-wise response with respect to the investigated treatment. This further allows dynamic decisions regarding futility and efficacy of individual baskets during the ongoing trial. Several statistical designs have been proposed on how a basket trial can be conducted and this has left an unclear situation with many options. The different designs propose different mathematical and statistical techniques, different decision rules, and also different trial purposes. This paper presents a broad overview of existing designs, categorizes them, and elaborates their similarities and differences. A uniform and consistent notation facilitates the first contact, introduction, and understanding of the statistical methodologies and techniques used in basket trials. Finally, this paper presents a modular approach for the construction of basket trials in applied medical science and forms a base for further research of basket trial designs and their techniques.
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Affiliation(s)
- Moritz Pohl
- Institute of Medical Biometry and Informatics, Medical Biometry, University of Heidelberg, Heidelberg, Germany
| | - Johannes Krisam
- Institute of Medical Biometry and Informatics, Medical Biometry, University of Heidelberg, Heidelberg, Germany
| | - Meinhard Kieser
- Institute of Medical Biometry and Informatics, Medical Biometry, University of Heidelberg, Heidelberg, Germany
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24
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May Lee K, Jack Lee J. Evaluating Bayesian adaptive randomization procedures with adaptive clip methods for multi-arm trials. Stat Methods Med Res 2021; 30:1273-1287. [PMID: 33689524 PMCID: PMC7613973 DOI: 10.1177/0962280221995961] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/10/2024]
Abstract
Bayesian adaptive randomization is a heuristic approach that aims to randomize more patients to the putatively superior arms based on the trend of the accrued data in a trial. Many statistical aspects of this approach have been explored and compared with other approaches; yet only a limited number of works has focused on improving its performance and providing guidance on its application to real trials. An undesirable property of this approach is that the procedure would randomize patients to an inferior arm in some circumstances, which has raised concerns in its application. Here, we propose an adaptive clip method to rectify the problem by incorporating a data-driven function to be used in conjunction with Bayesian adaptive randomization procedure. This function aims to minimize the chance of assigning patients to inferior arms during the early time of the trial. Moreover, we propose a utility approach to facilitate the selection of a randomization procedure. A cost that reflects the penalty of assigning patients to the inferior arm(s) in the trial is incorporated into our utility function along with all patients benefited from the trial, both within and beyond the trial. We illustrate the selection strategy for a wide range of scenarios.
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Affiliation(s)
- Kim May Lee
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - J Jack Lee
- University of Texas MD Anderson Cancer Center, Houston, TX, USA
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25
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Proper J, Connett J, Murray T. Alternative models and randomization techniques for Bayesian response-adaptive randomization with binary outcomes. Clin Trials 2021; 18:417-426. [PMID: 33926267 DOI: 10.1177/17407745211010139] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
BACKGROUND Bayesian response-adaptive designs, which data adaptively alter the allocation ratio in favor of the better performing treatment, are often criticized for engendering a non-trivial probability of a subject imbalance in favor of the inferior treatment, inflating type I error rate, and increasing sample size requirements. The implementation of these designs using the Thompson sampling methods has generally assumed a simple beta-binomial probability model in the literature; however, the effect of these choices on the resulting design operating characteristics relative to other reasonable alternatives has not been fully examined. Motivated by the Advanced R2 Eperfusion STrategies for Refractory Cardiac Arrest trial, we posit that a logistic probability model coupled with an urn or permuted block randomization method will alleviate some of the practical limitations engendered by the conventional implementation of a two-arm Bayesian response-adaptive design with binary outcomes. In this article, we discuss up to what extent this solution works and when it does not. METHODS A computer simulation study was performed to evaluate the relative merits of a Bayesian response-adaptive design for the Advanced R2 Eperfusion STrategies for Refractory Cardiac Arrest trial using the Thompson sampling methods based on a logistic regression probability model coupled with either an urn or permuted block randomization method that limits deviations from the evolving target allocation ratio. The different implementations of the response-adaptive design were evaluated for type I error rate control across various null response rates and power, among other performance metrics. RESULTS The logistic regression probability model engenders smaller average sample sizes with similar power, better control over type I error rate, and more favorable treatment arm sample size distributions than the conventional beta-binomial probability model, and designs using the alternative randomization methods have a negligible chance of a sample size imbalance in the wrong direction. CONCLUSION Pairing the logistic regression probability model with either of the alternative randomization methods results in a much improved response-adaptive design in regard to important operating characteristics, including type I error rate control and the risk of a sample size imbalance in favor of the inferior treatment.
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Affiliation(s)
- Jennifer Proper
- Division of Biostatistics, University of Minnesota Twin Cities, Minneapolis, MN, USA
| | - John Connett
- Division of Biostatistics, University of Minnesota Twin Cities, Minneapolis, MN, USA
| | - Thomas Murray
- Division of Biostatistics, University of Minnesota Twin Cities, Minneapolis, MN, USA
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26
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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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27
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Bayesian Approaches for Confirmatory Trials in Rare Diseases: Opportunities and Challenges. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18031022. [PMID: 33498915 PMCID: PMC7908592 DOI: 10.3390/ijerph18031022] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 11/29/2020] [Revised: 01/15/2021] [Accepted: 01/20/2021] [Indexed: 11/30/2022]
Abstract
The aim of this narrative review is to introduce the reader to Bayesian methods that, in our opinion, appear to be the most important in the context of rare diseases. A disease is defined as rare depending on the prevalence of the affected patients in the considered population, for example, about 1 in 1500 people in U.S.; about 1 in 2500 people in Japan; and fewer than 1 in 2000 people in Europe. There are between 6000 and 8000 rare diseases and the main issue in drug development is linked to the challenge of achieving robust evidence from clinical trials in small populations. A better use of all available information can help the development process and Bayesian statistics can provide a solid framework at the design stage, during the conduct of the trial, and at the analysis stage. The focus of this manuscript is to provide a review of Bayesian methods for sample size computation or reassessment during phase II or phase III trial, for response adaptive randomization and of for meta-analysis in rare disease. Challenges regarding prior distribution choice, computational burden and dissemination are also discussed.
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28
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The Bayesian Design of Adaptive Clinical Trials. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18020530. [PMID: 33435249 PMCID: PMC7826635 DOI: 10.3390/ijerph18020530] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Revised: 12/31/2020] [Accepted: 01/06/2021] [Indexed: 01/13/2023]
Abstract
This paper presents a brief overview of the recent literature on adaptive design of clinical trials from a Bayesian perspective for statistically not so sophisticated readers. Adaptive designs are attracting a keen interest in several disciplines, from a theoretical viewpoint and also—potentially—from a practical one, and Bayesian adaptive designs, in particular, have raised high expectations in clinical trials. The main conceptual tools are highlighted here, with a mention of several trial designs proposed in the literature that use these methods, including some of the registered Bayesian adaptive trials to this date. This review aims at complementing the existing ones on this topic, pointing at further interesting reading material.
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29
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Bassi A, Berkhof J, de Jong D, van de Ven PM. Bayesian adaptive decision-theoretic designs for multi-arm multi-stage clinical trials. Stat Methods Med Res 2020; 30:717-730. [PMID: 33243087 PMCID: PMC8008394 DOI: 10.1177/0962280220973697] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Multi-arm multi-stage clinical trials in which more than two drugs are simultaneously investigated provide gains over separate single- or two-arm trials. In this paper we propose a generic Bayesian adaptive decision-theoretic design for multi-arm multi-stage clinical trials with K (K≥2) arms. The basic idea is that after each stage a decision about continuation of the trial and accrual of patients for an additional stage is made on the basis of the expected reduction in loss. For this purpose, we define a loss function that incorporates the patient accrual costs as well as costs associated with an incorrect decision at the end of the trial. An attractive feature of our loss function is that its estimation is computationally undemanding, also when K > 2. We evaluate the frequentist operating characteristics for settings with a binary outcome and multiple experimental arms. We consider both the situation with and without a control arm. In a simulation study, we show that our design increases the probability of making a correct decision at the end of the trial as compared to nonadaptive designs and adaptive two-stage designs.
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Affiliation(s)
- Andrea Bassi
- Department of Epidemiology and Data Science, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Johannes Berkhof
- Department of Epidemiology and Data Science, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Daphne de Jong
- Department of Pathology, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Peter M van de Ven
- Department of Epidemiology and Data Science, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
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30
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Heath A, Yaskina M, Pechlivanoglou P, Rios D, Offringa M, Klassen TP, Poonai N, Pullenayegum E. A Bayesian response-adaptive dose-finding and comparative effectiveness trial. Clin Trials 2020; 18:61-70. [PMID: 33231105 DOI: 10.1177/1740774520965173] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
BACKGROUND/AIMS Combinations of treatments that have already received regulatory approval can offer additional benefit over Each of the treatments individually. However, trials of these combinations are lower priority than those that develop novel therapies, which can restrict funding, timelines and patient availability. This article develops a novel trial design to facilitate the evaluation of New combination therapies. This trial design combines elements of phase II and phase III trials to reduce the burden of evaluating combination therapies, while also maintaining a feasible sample size. This design was developed for a randomised trial that compares the properties of three combination doses of ketamine and dexmedetomidine, given intranasally, to ketamine delivered intravenously for children undergoing a closed reduction for a fracture or dislocation. METHODS This trial design uses response-adaptive randomisation to evaluate different dose combinations and increase the information collected for successful novel drug combinations. The design then uses Bayesian dose-response modelling to undertake a comparative effectiveness analysis for the most successful dose combination against a relevant comparator. We used simulation methods determine the thresholds for adapting the trial and making conclusions. We also used simulations to evaluate the probability of selecting the dose combination with the highest true effectiveness the operating characteristics of the design and its Bayesian predictive power. RESULTS With 410 participants, five interim updates of the randomisation ratio and a probability of effectiveness of 0.93, 0.88 and 0.83 for the three dose combinations, we have an 83% chance of randomising the largest number of patients to the drug with the highest probability of effectiveness. Based on this adaptive randomisation procedure, the comparative effectiveness analysis has a type I error of less than 5% and a 93% chance of correcting concluding non-inferiority, when the probability of effectiveness for the optimal combination therapy is 0.9. In this case, the trial has a greater than 77% chance of meeting its dual aims of dose-finding and comparative effectiveness. Finally, the Bayesian predictive power of the trial is over 90%. CONCLUSIONS By simultaneously determining the optimal dose and collecting data on the relative effectiveness of an intervention, we can minimise administrative burden and recruitment time for a trial. This will minimise the time required to get effective, safe combination therapies to patients quickly. The proposed trial has high potential to meet the dual study objectives within a feasible overall sample size.
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Affiliation(s)
- Anna Heath
- Child Health Evaluative Sciences, Peter Gilgan Centre for Research and Learning, The Hospital for Sick Children, Toronto, ON, Canada.,Division of Biostatistics, University of Toronto, Toronto, ON, Canada.,Department of Statistical Science, University College London, London, United Kingdom
| | - Maryna Yaskina
- Women & Children's Health Research Institute, University of Alberta, Edmonton, AB, Canada
| | - Petros Pechlivanoglou
- Child Health Evaluative Sciences, Peter Gilgan Centre for Research and Learning, The Hospital for Sick Children, Toronto, ON, Canada.,Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada
| | - David Rios
- Child Health Evaluative Sciences, Peter Gilgan Centre for Research and Learning, The Hospital for Sick Children, Toronto, ON, Canada
| | - Martin Offringa
- Child Health Evaluative Sciences, Peter Gilgan Centre for Research and Learning, The Hospital for Sick Children, Toronto, ON, Canada.,Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada
| | - Terry P Klassen
- University of Manitoba, Winnipeg, MB, Canada.,Children's Hospital Research Institute of Manitoba, Winnipeg, MB, Canada
| | - Naveen Poonai
- Schulich School of Medicine and Dentistry, London, ON, Canada.,Children's Health Research Institute, London Health Sciences Centre, London, ON, Canada
| | - Eleanor Pullenayegum
- Child Health Evaluative Sciences, Peter Gilgan Centre for Research and Learning, The Hospital for Sick Children, Toronto, ON, Canada.,Division of Biostatistics, University of Toronto, Toronto, ON, Canada
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31
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Mozgunov P, Jaki T. An information theoretic approach for selecting arms in clinical trials. J R Stat Soc Series B Stat Methodol 2020. [DOI: 10.1111/rssb.12391] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
| | - Thomas Jaki
- Lancaster University and University of Cambridge UK
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32
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The Evolution of Master Protocol Clinical Trial Designs: A Systematic Literature Review. Clin Ther 2020; 42:1330-1360. [DOI: 10.1016/j.clinthera.2020.05.010] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2020] [Revised: 04/10/2020] [Accepted: 05/11/2020] [Indexed: 02/07/2023]
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33
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Crippa A, De Laere B, Discacciati A, Larsson B, Connor JT, Gabriel EE, Thellenberg C, Jänes E, Enblad G, Ullen A, Hjälm-Eriksson M, Oldenburg J, Ost P, Lindberg J, Eklund M, Grönberg H. The ProBio trial: molecular biomarkers for advancing personalized treatment decision in patients with metastatic castration-resistant prostate cancer. Trials 2020; 21:579. [PMID: 32586393 PMCID: PMC7318749 DOI: 10.1186/s13063-020-04515-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2020] [Accepted: 06/15/2020] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Multiple therapies exist for patients with metastatic castration-resistant prostate cancer (mCRPC). However, their improvement on progression-free survival (PFS) remains modest, potentially explained by tumor molecular heterogeneity. Several prognostic molecular biomarkers have been identified for mCRPC that may have predictive potential to guide treatment selection and prolong PFS. We designed a platform trial to test this hypothesis. METHODS The Prostate-Biomarker (ProBio) study is a multi-center, outcome-adaptive, multi-arm, biomarker-driven platform trial for tailoring treatment decisions for men with mCRPC. Treatment decisions in the experimental arms are based on biomarker signatures defined as mutations in certain genes/pathways suggested in the scientific literature to be important for treatment response in mCRPC. The biomarker signatures are determined by targeted sequencing of circulating tumor and germline DNA using a panel specifically designed for mCRPC. DISCUSSION Patients are stratified based on the sequencing results and randomized to either current clinical practice (control), where the treating physician decides treatment, or to molecularly driven treatment selection based on the biomarker profile. Outcome-adaptive randomization is implemented to early identify promising treatments for a biomarker signature. Biomarker signature-treatment combinations graduate from the platform when they demonstrate 85% probability of improving PFS compared to the control arm. Graduated combinations are further evaluated in a seamless confirmatory trial with fixed randomization. The platform design allows for new drugs and biomarkers to be introduced in the study. CONCLUSIONS The ProBio design allows promising treatment-biomarker combinations to quickly graduate from the platform and be confirmed for rapid implementation in clinical care. TRIAL REGISTRATION ClinicalTrials.gov Identifier NCT03903835. Date of registration: April 4, 2019. Status: Recruiting.
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Affiliation(s)
- Alessio Crippa
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
| | - Bram De Laere
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department of Human Structure and Repair, Ghent University, Ghent, Belgium
| | - Andrea Discacciati
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Berit Larsson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Jason T Connor
- University of Central Florida College of Medicine, Orlando, FL, USA
- Confluence Stat LLC, Orlando, FL, USA
| | - Erin E Gabriel
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Camilla Thellenberg
- Department of Radiation Sciences and Oncology, Umeå University, Umeå, Sweden
| | - Elin Jänes
- Länssjukhuset Sundsvall Härnösand, Sundsvall, Sweden
| | - Gunilla Enblad
- Department of Immunology, Genetics and Pathology, Uppsala Universitet, Uppsala, Sweden
| | - Anders Ullen
- Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden
| | | | - Jan Oldenburg
- Division of Medicine, University of Oslo, Oslo, Norway
| | - Piet Ost
- Department of Radiotherapy and Experimental Cancer Research, Ghent University, Ghent, Belgium
| | - Johan Lindberg
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Martin Eklund
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Henrik Grönberg
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
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34
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Xia F, George SL, Ning J, Li L, Huang X. A Signature Enrichment Design with Bayesian Adaptive Randomization. J Appl Stat 2020; 48:1091-1110. [PMID: 34024982 DOI: 10.1080/02664763.2020.1757048] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
Clinical trials in the era of precision cancer medicine aim to identify and validate biomarker signatures which can guide the assignment of individually optimal treatments to patients. In this article, we propose a group sequential randomized phase II design, which updates the biomarker signature as the trial goes on, utilizes enrichment strategies for patient selection, and uses Bayesian response-adaptive randomization for treatment assignment. To evaluate the performance of the new design, in addition to the commonly considered criteria of type I error and power, we propose four new criteria measuring the benefits and losses for individuals both inside and outside of the clinical trial. Compared with designs with equal randomization, the proposed design gives trial participants a better chance to receive their personalized optimal treatments and thus results in a higher response rate on the trial. This design increases the chance to discover a successful new drug by an adaptive enrichment strategy, i.e., identification and selective enrollment of a subset of patients who are sensitive to the experimental therapies. Simulation studies demonstrate these advantages of the proposed design. It is illustrated by an example based on an actual clinical trial in non-small-cell lung cancer.
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Affiliation(s)
- Fang Xia
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center
| | - Stephen L George
- Department of Biostatistics and Bioinformatics, Duke University School of Medicine
| | - Jing Ning
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center
| | - Liang Li
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center
| | - Xuelin Huang
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center
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35
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Viele K, Saville BR, McGlothlin A, Broglio K. Comparison of response adaptive randomization features in multiarm clinical trials with control. Pharm Stat 2020; 19:602-612. [DOI: 10.1002/pst.2015] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2018] [Revised: 01/27/2020] [Accepted: 03/02/2020] [Indexed: 12/27/2022]
Affiliation(s)
- Kert Viele
- Berry Consultants Austin Texas USA
- Department of Biostatistics University of Kentucky Lexington Kentucky USA
| | - Benjamin R. Saville
- Berry Consultants Austin Texas USA
- Department of Biostatistics Vanderbilt University Nashville Tennessee USA
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36
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Ryan EG, Lamb SE, Williamson E, Gates S. Bayesian adaptive designs for multi-arm trials: an orthopaedic case study. Trials 2020; 21:83. [PMID: 31937341 PMCID: PMC6961269 DOI: 10.1186/s13063-019-4021-0] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2019] [Accepted: 12/20/2019] [Indexed: 12/26/2022] Open
Abstract
BACKGROUND Bayesian adaptive designs can be more efficient than traditional methods for multi-arm randomised controlled trials. The aim of this work was to demonstrate how Bayesian adaptive designs can be constructed for multi-arm phase III clinical trials and assess potential benefits that these designs offer. METHODS We constructed several alternative Bayesian adaptive designs for the Collaborative Ankle Support Trial (CAST), which was a randomised controlled trial that compared four treatments for severe ankle sprain. These designs incorporated response adaptive randomisation (RAR), arm dropping, and early stopping for efficacy or futility. We studied the operating characteristics of the Bayesian designs via simulation. We then virtually re-executed the trial by implementing the Bayesian adaptive designs using patient data sampled from the CAST study to demonstrate the practical applicability of the designs. RESULTS We constructed five Bayesian adaptive designs, each of which had high power and recruited fewer patients on average than the original designs target sample size. The virtual executions showed that most of the Bayesian designs would have led to trials that declared superiority of one of the interventions over the control. Bayesian adaptive designs with RAR or arm dropping were more likely to allocate patients to better performing arms at each interim analysis. Similar estimates and conclusions were obtained from the Bayesian adaptive designs as from the original trial. CONCLUSIONS Using CAST as an example, this case study shows how Bayesian adaptive designs can be constructed for phase III multi-arm trials using clinically relevant decision criteria. These designs demonstrated that they can potentially generate earlier results and allocate more patients to better performing arms. We recommend the wider use of Bayesian adaptive approaches in phase III clinical trials. TRIAL REGISTRATION CAST study registration ISRCTN, ISRCTN37807450. Retrospectively registered on 25 April 2003.
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Affiliation(s)
- Elizabeth G Ryan
- Cancer Research UK Clinical Trials Unit, Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, B15 2TT, UK.
| | - Sarah E Lamb
- Centre for Rehabilitation Research, Nuffield Department of Orthopaedics, Rheumatology & Musculoskeletal Sciences (NDORMS), Botnar Research Centre, University of Oxford, Oxford, OX3 7LD, UK
- College of Medicine and Health, University of Exeter, Exeter, EX1 2LU, UK
| | - Esther Williamson
- Centre for Rehabilitation Research, Nuffield Department of Orthopaedics, Rheumatology & Musculoskeletal Sciences (NDORMS), Botnar Research Centre, University of Oxford, Oxford, OX3 7LD, UK
| | - Simon Gates
- Cancer Research UK Clinical Trials Unit, Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, B15 2TT, UK
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37
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Viele K, Broglio K, McGlothlin A, Saville BR. Comparison of methods for control allocation in multiple arm studies using response adaptive randomization. Clin Trials 2019; 17:52-60. [PMID: 31630567 DOI: 10.1177/1740774519877836] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND/AIMS Response adaptive randomization has many polarizing properties in two-arm settings comparing control to a single treatment. The generalization of these features to the multiple arm setting has been less explored, and existing comparisons in the literature reach disparate conclusions. We investigate several generalizations of two-arm response adaptive randomization methods relating to control allocation in multiple arm trials, exploring how critiques of response adaptive randomization generalize to the multiple arm setting. METHODS We perform a simulation study to investigate multiple control allocation schemes within response adaptive randomization, comparing the designs on metrics such as power, arm selection, mean square error, and the treatment of patients within the trial. RESULTS The results indicate that the generalization of two-arm response adaptive randomization concerns is variable and depends on the form of control allocation employed. The concerns are amplified when control allocation may be reduced over the course of the trial but are mitigated in the methods considered when control allocation is maintained or increased during the trial. In our chosen example, we find minimal advantage to increasing, as opposed to maintaining, control allocation; however, this result reflects an extremely limited exploration of methods for increasing control allocation. CONCLUSION Selection of control allocation in multiple arm response adaptive randomization has a large effect on the performance of the design. Some disparate comparisons of response adaptive randomization to alternative paradigms may be partially explained by these results. In future comparisons, control allocation for multiple arm response adaptive randomization should be chosen to keep in mind the appropriate match between control allocation in response adaptive randomization and the metric or metrics of interest.
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Affiliation(s)
| | | | | | - Benjamin R Saville
- Berry Consultants LLC, Austin, TX, USA.,Department of Biostatistics, Vanderbilt University School of Medicine, Nashville, TN, USA
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38
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Tidwell RSS, Peng SA, Chen M, Liu DD, Yuan Y, Lee JJ. Bayesian clinical trials at The University of Texas MD Anderson Cancer Center: An update. Clin Trials 2019; 16:645-656. [PMID: 31450957 DOI: 10.1177/1740774519871471] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
BACKGROUND/AIMS In our 2009 article, we showed that Bayesian methods had established a foothold in developing therapies in our institutional oncology trials. In this article, we will document what has happened since that time. In addition, we will describe barriers to implementing Bayesian clinical trials, as well as our experience overcoming them. METHODS We reviewed MD Anderson Cancer Center clinical trials submitted to the institutional protocol office for scientific and ethical review between January 2009 and December 2013, the same length time period as the previous article. We tabulated Bayesian methods implemented for design or analyses for each trial and then compared these to our previous findings. RESULTS Overall, we identified 1020 trials and found that 283 (28%) had Bayesian components so we designated them as Bayesian trials. Among MD Anderson-only and multicenter trials, 56% and 14%, respectively, were Bayesian, higher rates than our previous study. Bayesian trials were more common in phase I/II trials (34%) than in phase III/IV (6%) trials. Among Bayesian trials, the most commonly used features were for toxicity monitoring (65%), efficacy monitoring (36%), and dose finding (22%). The majority (86%) of Bayesian trials used non-informative priors. A total of 75 (27%) trials applied Bayesian methods for trial design and primary endpoint analysis. Among this latter group, the most commonly used methods were the Bayesian logistic regression model (N = 22), the continual reassessment method (N = 20), and adaptive randomization (N = 16). Median institutional review board approval time from protocol submission was the same 1.4 months for Bayesian and non-Bayesian trials. Since the previous publication, the Biomarker-Integrated Approaches of Targeted Therapy for Lung Cancer Elimination (BATTLE) trial was the first large-scale decision trial combining multiple treatments in a single trial. Since then, two regimens in breast cancer therapy have been identified and published from the cooperative Investigation of Serial Studies to Predict Your Therapeutic Response with Imaging and Molecular Analysis (I-SPY 2), enhancing cooperation among investigators and drug developers across the nation, as well as advancing information needed for personalized medicine. Many software programs and Shiny applications for Bayesian trial design and calculations are available from our website which has had more than 21,000 downloads worldwide since 2004. CONCLUSION Bayesian trials have the increased flexibility in trial design needed for personalized medicine, resulting in more cooperation among researchers working to fight against cancer. Some disadvantages of Bayesian trials remain, but new methods and software are available to improve their function and incorporation into cancer clinical research.
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Affiliation(s)
- Rebecca S Slack Tidwell
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - S Andrew Peng
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Minxing Chen
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Diane D Liu
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Ying Yuan
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - J Jack Lee
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
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39
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Gajewski BJ, Statland J, Barohn R. Using Adaptive Designs to Avoid Selecting the Wrong Arms in Multiarm Comparative Effectiveness Trials. Stat Biopharm Res 2019; 11:375-386. [PMID: 31839873 DOI: 10.1080/19466315.2019.1610044] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Abstract
Limited resources are a challenge when planning comparative effectiveness studies of multiple promising treatments, often prompting study planners to reduce the sample size to meet the financial constraints. The practical solution is often to increase the efficiency of this sample size by selecting a pair of treatments among the pool of promising treatments before the clinical trial begins. The problem with this approach is that the investigator may inadvertently leave out the most beneficial treatment. This paper demonstrates a possible solution to this problem by using Bayesian adaptive designs. We use a planned comparative effectiveness clinical trial of treatments for sialorrhea in amyotrophic lateral sclerosis as an example of the approach. Rather than having to guess at the two best treatments to compare based on limited data, we suggest putting more arms in the trial and letting response adaptive randomization (RAR) determine better arms. To ground this study relative to previous literature we first compare RAR, adaptive equal randomization (ER), arm(s) dropping, and a fixed design. Given the goals of this trial we demonstrate that we may avoid 'type III errors' - inadvertently leaving out the best treatment - with little loss in power compared to a two-arm design, even when choosing the correct two arms for the two-armed design. There are appreciable gains in power when the two arms are prescreened at random.
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Affiliation(s)
- Byron J Gajewski
- Department of Biostatistics & Data Science, University of Kansas Medical Center, Mail Stop 1026, 3901 Rainbow Blvd., Kansas City, KS 66160, USA
| | - Jeffrey Statland
- Department of Neurology, University of Kansas Medical Center, Mail Stop 2012, 3901 Rainbow Blvd., Kansas City, KS 66160, USA
| | - Richard Barohn
- Department of Neurology, University of Kansas Medical Center, Mail Stop 2012, 3901 Rainbow Blvd., Kansas City, KS 66160, USA
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40
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Sim J. Outcome-adaptive randomization in clinical trials: issues of participant welfare and autonomy. THEORETICAL MEDICINE AND BIOETHICS 2019; 40:83-101. [PMID: 30778720 PMCID: PMC6478640 DOI: 10.1007/s11017-019-09481-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Outcome-adaptive randomization (OAR) has been proposed as a corrective to certain ethical difficulties inherent in the traditional randomized clinical trial (RCT) using fixed-ratio randomization. In particular, it has been suggested that OAR redresses the balance between individual and collective ethics in favour of the former. In this paper, I examine issues of welfare and autonomy arising in relation to OAR. A central issue in discussions of welfare in OAR is equipoise, and the moral status of OAR is crucially influenced by the way in which this concept is construed. If OAR is based on a model of equipoise that demands strict indifference between competing interventions throughout the trial, such equipoise is disturbed by accruing data favouring one treatment over another; OAR seeks to redress this by weighting randomization to the seemingly superior treatment. However, this is a partial response, as patients continue to be allocated to the inferior therapy. Moreover, it rests upon considerations of aggregate harms and benefits, and does not therefore uphold individual ethics. Issues of fairness also arise, as early and late enrollees are randomized on a different basis. Fixed-ratio randomization represents a fuller and more consistent response to a loss of equipoise, as so construed. With regard to consent, the complexity of OAR poses challenges to adequate disclosure and comprehension. Additionally, OAR does not offer a remedy to the therapeutic misconception-participants' tendency to attribute treatment allocation in an RCT to individual clinical judgments, rather than to scientific considerations-and, if anything, accentuates rather than alleviates this misconception. In relation to these issues, OAR fails to offer ethical advantages over fixed-ratio randomization. More broadly, the ethical basis of OAR can be seen to lie more in collective than in individual ethics, and overall it fares worse in this territory than fixed-ratio randomization.
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Affiliation(s)
- Julius Sim
- Institute for Primary Care and Health Sciences, Keele University, Staffordshire, ST5 5BG, UK.
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41
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Proschan MA, Dodd LE. Re-randomization tests in clinical trials. Stat Med 2019; 38:2292-2302. [PMID: 30672002 DOI: 10.1002/sim.8093] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2018] [Revised: 12/17/2018] [Accepted: 12/17/2018] [Indexed: 11/12/2022]
Abstract
As randomization methods use more information in more complex ways to assign patients to treatments, analysis of the resulting data becomes challenging. The treatment assignment vector and outcome vector become correlated whenever randomization probabilities depend on data correlated with outcomes. One straightforward analysis method is a re-randomization test that fixes outcome data and creates a reference distribution for the test statistic by repeatedly re-randomizing according to the same randomization method used in the trial. This article reviews re-randomization tests, especially in nonstandard settings like covariate-adaptive and response-adaptive randomization. We show that re-randomization tests provide valid inference in a wide range of settings. Nonetheless, there are simple examples demonstrating limitations.
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Affiliation(s)
- Michael A Proschan
- Biostatistics Research Branch, National Institute of Allergy and Infectious Diseases, Rockville, MD
| | - Lori E Dodd
- National Institutes of Health, National Institute of Allergy and Infectious Diseases, Rockville, MD
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42
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Qiao W, Ning J, Huang X. A clinical trial design with covariate-adjusted response-adaptive randomization using superiority confidence of treatments. Stat Biopharm Res 2019; 11:336-347. [PMID: 34113421 DOI: 10.1080/19466315.2019.1647279] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Adaptive randomization using response outcome or covariate are commonly used in the literature. However, the performance of these designs has not been thoroughly studied, especially when there are various interactions between the covariate and treatment. We have conducted simulations to evaluate the performance of commonly used designs under two-arm and multiple-arm situations. When a predictive factor exists, in the phase II trial conduction using adaptive designs, such as the BATTLE-1, BATTLE-2 trial and ISPY-2 trials, researchers evaluate the operating characteristics using the traditional power assessment. In this article, new criteria are used in a general modeling frame work to incorporate the complicated interaction. Based on our evaluation, the covariate-adjusted and response-adaptive randomization (Sc-ca) results in a greater total number of responders. Additionally, the design can detect the treatment effect difference in subgroups, and consistently assign patients to the most beneficial treatment according to their covariate profiles. This translates into a higher proportion of individuals receiving optimized treatments compared with other commonly used designs. This adaptive design is a step toward personalized therapy to benefit each patient enrolled in a prospective clinical trial, when there is the strong evidence that predictive factors exist.
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Affiliation(s)
- Wei Qiao
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Jing Ning
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Xuelin Huang
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX
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43
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Li Y, Wang M, Cheung YK. Treatment and dose prioritization in early phase platform trials of targeted cancer therapies. J R Stat Soc Ser C Appl Stat 2018. [DOI: 10.1111/rssc.12324] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Yimei Li
- University of Pennsylvania, Philadelphia, and Children's Hospital of Philadelphia USA
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44
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Xu Y, Müller P, Tsimberidou AM, Berry D. A nonparametric Bayesian basket trial design. Biom J 2018; 61:1160-1174. [PMID: 29808479 DOI: 10.1002/bimj.201700162] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2017] [Revised: 04/09/2018] [Accepted: 04/13/2018] [Indexed: 12/13/2022]
Abstract
Targeted therapies on the basis of genomic aberrations analysis of the tumor have shown promising results in cancer prognosis and treatment. Regardless of tumor type, trials that match patients to targeted therapies for their particular genomic aberrations have become a mainstream direction of therapeutic management of patients with cancer. Therefore, finding the subpopulation of patients who can most benefit from an aberration-specific targeted therapy across multiple cancer types is important. We propose an adaptive Bayesian clinical trial design for patient allocation and subpopulation identification. We start with a decision theoretic approach, including a utility function and a probability model across all possible subpopulation models. The main features of the proposed design and population finding methods are the use of a flexible nonparametric Bayesian survival regression based on a random covariate-dependent partition of patients, and decisions based on a flexible utility function that reflects the requirement of the clinicians appropriately and realistically, and the adaptive allocation of patients to their superior treatments. Through extensive simulation studies, the new method is demonstrated to achieve desirable operating characteristics and compares favorably against the alternatives.
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Affiliation(s)
- Yanxun Xu
- Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Peter Müller
- Department of Mathematics, University of Texas at Austin, Austin, TX, 78705, USA
| | - Apostolia M Tsimberidou
- Department of Investigational Cancer Therapeutics, The University of Texas M.D. Anderson Cancer Center, Houston, TX, 77005, USA
| | - Donald Berry
- Department of Biostatistics, The University of Texas M.D. Anderson Cancer Center, Houston, TX, 77005, USA
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45
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Chappell R, Durkalski V, Joffe S. University of Pennsylvania ninth annual conference on statistical issues in clinical trials: Where are we with adaptive clinical trial designs? (morning panel discussion). Clin Trials 2017; 14:441-450. [PMID: 28825324 DOI: 10.1177/1740774517723590] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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