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Burnett T, König F, Jaki T. Adding experimental treatment arms to multi-arm multi-stage platform trials in progress. Stat Med 2024. [PMID: 38852991 DOI: 10.1002/sim.10090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Revised: 01/16/2024] [Accepted: 04/15/2024] [Indexed: 06/11/2024]
Abstract
Multi-arm multi-stage (MAMS) platform trials efficiently compare several treatments with a common control arm. Crucially MAMS designs allow for adjustment for multiplicity if required. If for example, the active treatment arms in a clinical trial relate to different dose levels or different routes of administration of a drug, the strict control of the family-wise error rate (FWER) is paramount. Suppose a further treatment becomes available, it is desirable to add this to the trial already in progress; to access both the practical and statistical benefits of the MAMS design. In any setting where control of the error rate is required, we must add corresponding hypotheses without compromising the validity of the testing procedure.To strongly control the FWER, MAMS designs use pre-planned decision rules that determine the recruitment of the next stage of the trial based on the available data. The addition of a treatment arm presents an unplanned change to the design that we must account for in the testing procedure. We demonstrate the use of the conditional error approach to add hypotheses to any testing procedure that strongly controls the FWER. We use this framework to add treatments to a MAMS trial in progress. Simulations illustrate the possible characteristics of such procedures.
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Affiliation(s)
- Thomas Burnett
- Department of Mathematical Sciences, University of Bath, Bath, UK
| | - Franz König
- Center for Medical Data Science, Medical University of Vienna, Vienna, Austria
| | - Thomas Jaki
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
- Faculty of Computer Science and Data Science, University of Regensburg, Regensburg, Germany
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Wang Y, Yao M, Liu J, Liu Y, Ma Y, Luo X, Mei F, Xiang H, Zou K, Li L, Sun X. Adaptive designs were primarily used but inadequately reported in early phase drug trials. BMC Med Res Methodol 2024; 24:130. [PMID: 38840047 PMCID: PMC11151552 DOI: 10.1186/s12874-024-02256-9] [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: 05/26/2023] [Accepted: 05/27/2024] [Indexed: 06/07/2024] Open
Abstract
BACKGROUND Faced with the high cost and limited efficiency of classical randomized controlled trials, researchers are increasingly applying adaptive designs to speed up the development of new drugs. However, the application of adaptive design to drug randomized controlled trials (RCTs) and whether the reporting is adequate are unclear. Thus, this study aimed to summarize the epidemiological characteristics of the relevant trials and assess their reporting quality by the Adaptive designs CONSORT Extension (ACE) checklist. METHODS We searched MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials (CENTRAL) and ClinicalTrials.gov from inception to January 2020. We included drug RCTs that explicitly claimed to be adaptive trials or used any type of adaptative design. We extracted the epidemiological characteristics of included studies to summarize their adaptive design application. We assessed the reporting quality of the trials by Adaptive designs CONSORT Extension (ACE) checklist. Univariable and multivariable linear regression models were used to the association of four prespecified factors with the quality of reporting. RESULTS Our survey included 108 adaptive trials. We found that adaptive design has been increasingly applied over the years, and was commonly used in phase II trials (n = 45, 41.7%). The primary reasons for using adaptive design were to speed the trial and facilitate decision-making (n = 24, 22.2%), maximize the benefit of participants (n = 21, 19.4%), and reduce the total sample size (n = 15, 13.9%). Group sequential design (n = 63, 58.3%) was the most frequently applied method, followed by adaptive randomization design (n = 26, 24.1%), and adaptive dose-finding design (n = 24, 22.2%). The proportion of adherence to the ACE checklist of 26 topics ranged from 7.4 to 99.1%, with eight topics being adequately reported (i.e., level of adherence ≥ 80%), and eight others being poorly reported (i.e., level of adherence ≤ 30%). In addition, among the seven items specific for adaptive trials, three were poorly reported: accessibility to statistical analysis plan (n = 8, 7.4%), measures for confidentiality (n = 14, 13.0%), and assessments of similarity between interim stages (n = 25, 23.1%). The mean score of the ACE checklist was 13.9 (standard deviation [SD], 3.5) out of 26. According to our multivariable regression analysis, later published trials (estimated β = 0.14, p < 0.01) and the multicenter trials (estimated β = 2.22, p < 0.01) were associated with better reporting. CONCLUSION Adaptive design has shown an increasing use over the years, and was primarily applied to early phase drug trials. However, the reporting quality of adaptive trials is suboptimal, and substantial efforts are needed to improve the reporting.
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Affiliation(s)
- Yuning Wang
- Institute of Integrated Traditional Chinese and Western Medicine, West China Hospital, Chinese Evidence-based Medicine Center and Chinese Cochrane Center, Sichuan University, Chengdu, 610041, China
- NMPA Key Laboratory for Real World Data Research and Evaluation in Hainan, Chengdu, 610041, China
- China Sichuan Center of Technology Innovation for Real World Data, Chengdu, 610041, China
| | - Minghong Yao
- Institute of Integrated Traditional Chinese and Western Medicine, West China Hospital, Chinese Evidence-based Medicine Center and Chinese Cochrane Center, Sichuan University, Chengdu, 610041, China
- NMPA Key Laboratory for Real World Data Research and Evaluation in Hainan, Chengdu, 610041, China
- China Sichuan Center of Technology Innovation for Real World Data, Chengdu, 610041, China
| | - Jiali Liu
- Institute of Integrated Traditional Chinese and Western Medicine, West China Hospital, Chinese Evidence-based Medicine Center and Chinese Cochrane Center, Sichuan University, Chengdu, 610041, China
- NMPA Key Laboratory for Real World Data Research and Evaluation in Hainan, Chengdu, 610041, China
- China Sichuan Center of Technology Innovation for Real World Data, Chengdu, 610041, China
| | - Yanmei Liu
- Institute of Integrated Traditional Chinese and Western Medicine, West China Hospital, Chinese Evidence-based Medicine Center and Chinese Cochrane Center, Sichuan University, Chengdu, 610041, China
- NMPA Key Laboratory for Real World Data Research and Evaluation in Hainan, Chengdu, 610041, China
- China Sichuan Center of Technology Innovation for Real World Data, Chengdu, 610041, China
| | - Yu Ma
- Institute of Integrated Traditional Chinese and Western Medicine, West China Hospital, Chinese Evidence-based Medicine Center and Chinese Cochrane Center, Sichuan University, Chengdu, 610041, China
- NMPA Key Laboratory for Real World Data Research and Evaluation in Hainan, Chengdu, 610041, China
- China Sichuan Center of Technology Innovation for Real World Data, Chengdu, 610041, China
| | - Xiaochao Luo
- Institute of Integrated Traditional Chinese and Western Medicine, West China Hospital, Chinese Evidence-based Medicine Center and Chinese Cochrane Center, Sichuan University, Chengdu, 610041, China
- NMPA Key Laboratory for Real World Data Research and Evaluation in Hainan, Chengdu, 610041, China
- China Sichuan Center of Technology Innovation for Real World Data, Chengdu, 610041, China
| | - Fan Mei
- Institute of Integrated Traditional Chinese and Western Medicine, West China Hospital, Chinese Evidence-based Medicine Center and Chinese Cochrane Center, Sichuan University, Chengdu, 610041, China
- NMPA Key Laboratory for Real World Data Research and Evaluation in Hainan, Chengdu, 610041, China
- China Sichuan Center of Technology Innovation for Real World Data, Chengdu, 610041, China
| | - Hunong Xiang
- Institute of Integrated Traditional Chinese and Western Medicine, West China Hospital, Chinese Evidence-based Medicine Center and Chinese Cochrane Center, Sichuan University, Chengdu, 610041, China
- NMPA Key Laboratory for Real World Data Research and Evaluation in Hainan, Chengdu, 610041, China
- China Sichuan Center of Technology Innovation for Real World Data, Chengdu, 610041, China
| | - Kang Zou
- Institute of Integrated Traditional Chinese and Western Medicine, West China Hospital, Chinese Evidence-based Medicine Center and Chinese Cochrane Center, Sichuan University, Chengdu, 610041, China
- NMPA Key Laboratory for Real World Data Research and Evaluation in Hainan, Chengdu, 610041, China
- China Sichuan Center of Technology Innovation for Real World Data, Chengdu, 610041, China
| | - Ling Li
- Institute of Integrated Traditional Chinese and Western Medicine, West China Hospital, Chinese Evidence-based Medicine Center and Chinese Cochrane Center, Sichuan University, Chengdu, 610041, China.
- NMPA Key Laboratory for Real World Data Research and Evaluation in Hainan, Chengdu, 610041, China.
- China Sichuan Center of Technology Innovation for Real World Data, Chengdu, 610041, China.
| | - Xin Sun
- Institute of Integrated Traditional Chinese and Western Medicine, West China Hospital, Chinese Evidence-based Medicine Center and Chinese Cochrane Center, Sichuan University, Chengdu, 610041, China.
- NMPA Key Laboratory for Real World Data Research and Evaluation in Hainan, Chengdu, 610041, China.
- China Sichuan Center of Technology Innovation for Real World Data, Chengdu, 610041, China.
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Serra A, Mozgunov P, Jaki T. A Bayesian multi-arm multi-stage clinical trial design incorporating information about treatment ordering. Stat Med 2023; 42:2841-2854. [PMID: 37158302 PMCID: PMC10962588 DOI: 10.1002/sim.9752] [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: 04/22/2022] [Revised: 01/27/2023] [Accepted: 04/13/2023] [Indexed: 05/10/2023]
Abstract
Multi-Arm Multi-Stage (MAMS) designs can notably improve efficiency in later stages of drug development, but they can be suboptimal when an order in the effects of the arms can be assumed. In this work, we propose a Bayesian multi-arm multi-stage trial design that selects all promising treatments with high probability and can efficiently incorporate information about the order in the treatment effects as well as incorporate prior knowledge on the treatments. A distinguishing feature of the proposed design is that it allows taking into account the uncertainty of the treatment effect order assumption and does not assume any parametric arm-response model. The design can provide control of the family-wise error rate under specific values of the control mean and we illustrate its operating characteristics in a study of symptomatic asthma. Via simulations, we compare the novel Bayesian design with frequentist multi-arm multi-stage designs and a frequentist order restricted design that does not account for the order uncertainty and demonstrate the gains in the sample sizes the proposed design can provide. We also find that the proposed design is robust to violations of the assumptions on the order.
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Affiliation(s)
| | - Pavel Mozgunov
- MRC Biostatistics UnitUniversity of CambridgeCambridgeUK
| | - Thomas Jaki
- MRC Biostatistics UnitUniversity of CambridgeCambridgeUK
- Faculty for Informatics and Data ScienceUniversity of RegensburgRegensburgGermany
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4
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Wu L, Wang J, Wahed AS. Interim monitoring in sequential multiple assignment randomized trials. Biometrics 2023; 79:368-380. [PMID: 34571583 DOI: 10.1111/biom.13562] [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: 02/15/2021] [Revised: 06/10/2021] [Accepted: 09/03/2021] [Indexed: 11/29/2022]
Abstract
A sequential multiple assignment randomized trial (SMART) facilitates the comparison of multiple adaptive treatment strategies (ATSs) simultaneously. Previous studies have established a framework to test the homogeneity of multiple ATSs by a global Wald test through inverse probability weighting. SMARTs are generally lengthier than classical clinical trials due to the sequential nature of treatment randomization in multiple stages. Thus, it would be beneficial to add interim analyses allowing for an early stop if overwhelming efficacy is observed. We introduce group sequential methods to SMARTs to facilitate interim monitoring based on the multivariate chi-square distribution. Simulation studies demonstrate that the proposed interim monitoring in SMART (IM-SMART) maintains the desired type I error and power with reduced expected sample size compared to the classical SMART. Finally, we illustrate our method by reanalyzing a SMART assessing the effects of cognitive behavioral and physical therapies in patients with knee osteoarthritis and comorbid subsyndromal depressive symptoms.
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Affiliation(s)
- Liwen Wu
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Junyao Wang
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Abdus S Wahed
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
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5
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Lee KM, Robertson DS, Jaki T, Emsley R. The benefits of covariate adjustment for adaptive multi-arm designs. Stat Methods Med Res 2022; 31:2104-2121. [PMID: 35876412 PMCID: PMC7613816 DOI: 10.1177/09622802221114544] [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] [Indexed: 11/20/2022]
Abstract
Covariate adjustment via a regression approach is known to increase the precision
of statistical inference when fixed trial designs are employed in randomized
controlled studies. When an adaptive multi-arm design is employed with the
ability to select treatments, it is unclear how covariate adjustment affects
various aspects of the study. Consider the design framework that relies on
pre-specified treatment selection rule(s) and a combination test approach for
hypothesis testing. It is our primary goal to evaluate the impact of covariate
adjustment on adaptive multi-arm designs with treatment selection. Our secondary
goal is to show how the Uniformly Minimum Variance Conditionally Unbiased
Estimator can be extended to account for covariate adjustment analytically. We
find that adjustment with different sets of covariates can lead to different
treatment selection outcomes and hence probabilities of rejecting hypotheses.
Nevertheless, we do not see any negative impact on the control of the familywise
error rate when covariates are included in the analysis model. When adjusting
for covariates that are moderately or highly correlated with the outcome, we see
various benefits to the analysis of the design. Conversely, there is negligible
impact when including covariates that are uncorrelated with the outcome.
Overall, pre-specification of covariate adjustment is recommended for the
analysis of adaptive multi-arm design with treatment selection. Having the
statistical analysis plan in place prior to the interim and final analyses is
crucial, especially when a non-collapsible measure of treatment effect is
considered in the trial.
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Affiliation(s)
- Kim May Lee
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | | | - Thomas Jaki
- 47959MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
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Optimal Weighted Multiple-Testing Procedure for Clinical Trials. MATHEMATICS 2022. [DOI: 10.3390/math10121996] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
This paper describes a new method for testing randomized clinical trials with binary outcomes, which combines the O’Brien and Fleming (1979) multiple-testing procedure with optimal allocations and unequal weighted samples simultaneously. The O’Brien and Fleming method of group sequential testing is a simple and effective method with the same Type I error and power as a fixed one-stage chi-square test, with the option to terminate early if one treatment is clearly superior to another. This study modified the O’Brien and Fleming procedure, resulting in a more flexible new procedure, where the optimal allocation assists in allocating more subjects to the winning treatment without compromising the integrity of the study, while unequal weighting allows for different samples to be chosen for different stages of a trial. The new optimal weighted multiple-testing procedure (OWMP), based on simulation studies, is relatively robust to the added features because it showed a high preference for decreasing the Type I error and maintaining the power. In addition, the procedure was illustrated using simulated and real-life examples. The outcomes of the current study suggest that the new procedure is as effective as the original. However, it is more flexible.
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7
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Kyr M, Svobodnik A, Stepanova R, Hejnova R. N-of-1 Trials in Pediatric Oncology: From a Population-Based Approach to Personalized Medicine-A Review. Cancers (Basel) 2021; 13:5428. [PMID: 34771590 PMCID: PMC8582573 DOI: 10.3390/cancers13215428] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Revised: 10/11/2021] [Accepted: 10/27/2021] [Indexed: 12/02/2022] Open
Abstract
Pediatric oncology is a critical area where the more efficient development of new treatments is urgently needed. The speed of approval of new drugs is still limited by regulatory requirements and a lack of innovative designs appropriate for trials in children. Childhood cancers meet the criteria of rare diseases. Personalized medicine brings it even closer to the horizon of individual cases. Thus, not all the traditional research tools, such as large-scale RCTs, are always suitable or even applicable, mainly due to limited sample sizes. Small samples and traditional versus subject-specific evidence are both distinctive issues in personalized pediatric oncology. Modern analytical approaches and adaptations of the paradigms of evidence are warranted. We have reviewed innovative trial designs and analytical methods developed for small populations, together with individualized approaches, given their applicability to pediatric oncology. We discuss traditional population-based and individualized perspectives of inferences and evidence, and explain the possibilities of using various methods in pediatric personalized oncology. We find that specific derivatives of the original N-of-1 trial design adapted for pediatric personalized oncology may represent an optimal analytical tool for this area of medicine. We conclude that no particular N-of-1 strategy can provide a solution. Rather, a whole range of approaches is needed to satisfy the new inferential and analytical paradigms of modern medicine. We reveal a new view of cancer as continuum model and discuss the "evidence puzzle".
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Affiliation(s)
- Michal Kyr
- Department of Paediatric Oncology, University Hospital Brno and School of Medicine, Masaryk University, Cernopolni 9, 613 00 Brno, Czech Republic
- International Clinical Research Centre, St. Anne’s University Hospital Brno, Pekarska 53, 656 91 Brno, Czech Republic
| | - Adam Svobodnik
- Department of Pharmacology, Faculty of Medicine, Masaryk University, Kamenice 5, 625 00 Brno, Czech Republic; (A.S.); (R.S.) (R.H.)
| | - Radka Stepanova
- Department of Pharmacology, Faculty of Medicine, Masaryk University, Kamenice 5, 625 00 Brno, Czech Republic; (A.S.); (R.S.) (R.H.)
| | - Renata Hejnova
- Department of Pharmacology, Faculty of Medicine, Masaryk University, Kamenice 5, 625 00 Brno, Czech Republic; (A.S.); (R.S.) (R.H.)
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8
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Lin R, Thall PF, Yuan Y. BAGS: A Bayesian Adaptive Group Sequential Trial Design With Subgroup-Specific Survival Comparisons. J Am Stat Assoc 2020; 116:322-334. [DOI: 10.1080/01621459.2020.1837142] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Affiliation(s)
- Ruitao Lin
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Peter F. Thall
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Ying Yuan
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX
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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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Steingrimsson JA, Betz J, Qian T, Rosenblum M. Optimized adaptive enrichment designs for three-arm trials: learning which subpopulations benefit from different treatments. Biostatistics 2019; 22:283-297. [PMID: 31420983 DOI: 10.1093/biostatistics/kxz030] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2018] [Revised: 06/13/2019] [Accepted: 07/15/2019] [Indexed: 11/13/2022] Open
Abstract
We consider the problem of designing a confirmatory randomized trial for comparing two treatments versus a common control in two disjoint subpopulations. The subpopulations could be defined in terms of a biomarker or disease severity measured at baseline. The goal is to determine which treatments benefit which subpopulations. We develop a new class of adaptive enrichment designs tailored to solving this problem. Adaptive enrichment designs involve a preplanned rule for modifying enrollment based on accruing data in an ongoing trial. At the interim analysis after each stage, for each subpopulation, the preplanned rule may decide to stop enrollment or to stop randomizing participants to one or more study arms. The motivation for this adaptive feature is that interim data may indicate that a subpopulation, such as those with lower disease severity at baseline, is unlikely to benefit from a particular treatment while uncertainty remains for the other treatment and/or subpopulation. We optimize these adaptive designs to have the minimum expected sample size under power and Type I error constraints. We compare the performance of the optimized adaptive design versus an optimized nonadaptive (single stage) design. Our approach is demonstrated in simulation studies that mimic features of a completed trial of a medical device for treating heart failure. The optimized adaptive design has $25\%$ smaller expected sample size compared to the optimized nonadaptive design; however, the cost is that the optimized adaptive design has $8\%$ greater maximum sample size. Open-source software that implements the trial design optimization is provided, allowing users to investigate the tradeoffs in using the proposed adaptive versus standard designs.
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Affiliation(s)
- Jon Arni Steingrimsson
- Department of Biostatistics, Brown University, 121 South Main Street, Providence, RI 02903, USA
| | - Joshua Betz
- Department of Biostatistics, Johns Hopkins University, 615 North Wolfe Street, Baltimore, MD 21205, USA
| | - Tianchen Qian
- Department of Statistics, Harvard University, 1 Oxford St, Cambridge, MA 02138, USA
| | - Michael Rosenblum
- Department of Biostatistics, Johns Hopkins University, 615 North Wolfe Street, Baltimore, MD 21205, USA
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11
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Blenkinsop A, Parmar MK, Choodari-Oskooei B. Assessing the impact of efficacy stopping rules on the error rates under the multi-arm multi-stage framework. Clin Trials 2019; 16:132-141. [PMID: 30648428 PMCID: PMC6442021 DOI: 10.1177/1740774518823551] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
BACKGROUND The multi-arm multi-stage framework uses intermediate outcomes to assess lack-of-benefit of research arms at interim stages in randomised trials with time-to-event outcomes. However, the design lacks formal methods to evaluate early evidence of overwhelming efficacy on the definitive outcome measure. We explore the operating characteristics of this extension to the multi-arm multi-stage design and how to control the pairwise and familywise type I error rate. Using real examples and the updated nstage program, we demonstrate how such a design can be developed in practice. METHODS We used the Dunnett approach for assessing treatment arms when conducting comprehensive simulation studies to evaluate the familywise error rate, with and without interim efficacy looks on the definitive outcome measure, at the same time as the planned lack-of-benefit interim analyses on the intermediate outcome measure. We studied the effect of the timing of interim analyses, allocation ratio, lack-of-benefit boundaries, efficacy rule, number of stages and research arms on the operating characteristics of the design when efficacy stopping boundaries are incorporated. Methods for controlling the familywise error rate with efficacy looks were also addressed. RESULTS Incorporating Haybittle-Peto stopping boundaries on the definitive outcome at the interim analyses will not inflate the familywise error rate in a multi-arm design with two stages. However, this rule is conservative; in general, more liberal stopping boundaries can be used with minimal impact on the familywise error rate. Efficacy bounds in trials with three or more stages using an intermediate outcome may inflate the familywise error rate, but we show how to maintain strong control. CONCLUSION The multi-arm multi-stage design allows stopping for both lack-of-benefit on the intermediate outcome and efficacy on the definitive outcome at the interim stages. We provide guidelines on how to control the familywise error rate when efficacy boundaries are implemented in practice.
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12
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Mitroiu M, Rengerink KO, Pontes C, Sancho A, Vives R, Pesiou S, Fontanet JM, Torres F, Nikolakopoulos S, Pateras K, Rosenkranz G, Posch M, Urach S, Ristl R, Koch A, Loukia S, van der Lee JH, Roes KCB. Applicability and added value of novel methods to improve drug development in rare diseases. Orphanet J Rare Dis 2018; 13:200. [PMID: 30419965 PMCID: PMC6233569 DOI: 10.1186/s13023-018-0925-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2018] [Accepted: 10/02/2018] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND The ASTERIX project developed a number of novel methods suited to study small populations. The objective of this exercise was to evaluate the applicability and added value of novel methods to improve drug development in small populations, using real world drug development programmes as reported in European Public Assessment Reports. METHODS The applicability and added value of thirteen novel methods developed within ASTERIX were evaluated using data from 26 European Public Assessment Reports (EPARs) for orphan medicinal products, representative of rare medical conditions as predefined through six clusters. The novel methods included were 'innovative trial designs' (six methods), 'level of evidence' (one method), 'study endpoints and statistical analysis' (four methods), and 'meta-analysis' (two methods) and they were selected from the methods developed within ASTERIX based on their novelty; methods that discussed already available and applied strategies were not included for the purpose of this validation exercise. Pre-requisites for application in a study were systematized for each method, and for each main study in the selected EPARs it was assessed if all pre-requisites were met. This direct applicability using the actual study design was firstly assessed. Secondary, applicability and added value were explored allowing changes to study objectives and design, but without deviating from the context of the drug development plan. We evaluated whether differences in applicability and added value could be observed between the six predefined condition clusters. RESULTS AND DISCUSSION Direct applicability of novel methods appeared to be limited to specific selected cases. The applicability and added value of novel methods increased substantially when changes to the study setting within the context of drug development were allowed. In this setting, novel methods for extrapolation, sample size re-assessment, multi-armed trials, optimal sequential design for small sample sizes, Bayesian sample size re-estimation, dynamic borrowing through power priors and fall-back tests for co-primary endpoints showed most promise - applicable in more than 40% of evaluated EPARs in all clusters. Most of the novel methods were applicable to conditions in the cluster of chronic and progressive conditions, involving multiple systems/organs. Relatively fewer methods were applicable to acute conditions with single episodes. For the chronic clusters, Goal Attainment Scaling was found to be particularly applicable as opposed to other (non-chronic) clusters. CONCLUSION Novel methods as developed in ASTERIX can improve drug development programs. Achieving optimal added value of these novel methods often requires consideration of the entire drug development program, rather than reconsideration of methods for a specific trial. The novel methods tested were mostly applicable in chronic conditions, and acute conditions with recurrent episodes.
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Affiliation(s)
- Marian Mitroiu
- Clinical Trial Methodology, Julius Center for Health Sciences and Primary Care, Biostatistics and Research Support, University Medical Center Utrecht, University of Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
| | - Katrien Oude Rengerink
- Clinical Trial Methodology, Julius Center for Health Sciences and Primary Care, Biostatistics and Research Support, University Medical Center Utrecht, University of Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
| | - Caridad Pontes
- Departament de Farmacologia, de Terapèutica i de Toxicologia, Universitat Autònoma de Barcelona, Unitat Docent Parc Taulí, c/ Parc Taulí 1, 08208 Sabadell, Spain
- Unitat de Farmacologia Clínica, Hospital de Sabadell, Institut d’Investigació i Innovació Parc Taulí I3PT - Universitat Autònoma de Barcelona, c/ Parc Taulí 1, 08208 Sabadell, Spain
| | - Aranzazu Sancho
- Departament de Farmacologia, de Terapèutica i de Toxicologia, Universitat Autònoma de Barcelona, Unitat Docent Parc Taulí, c/ Parc Taulí 1, 08208 Sabadell, Spain
- Clinical Pharmacology Department, Research Institute Puerta de Hierro, C/Manuel de Falla, 1, 28222 Majadahonda, Madrid, Spain
| | - Roser Vives
- Departament de Farmacologia, de Terapèutica i de Toxicologia, Universitat Autònoma de Barcelona, Unitat Docent Parc Taulí, c/ Parc Taulí 1, 08208 Sabadell, Spain
- Unitat de Farmacologia Clínica, Hospital de Sabadell, Institut d’Investigació i Innovació Parc Taulí I3PT - Universitat Autònoma de Barcelona, c/ Parc Taulí 1, 08208 Sabadell, Spain
| | - Stella Pesiou
- Departament de Farmacologia, de Terapèutica i de Toxicologia, Universitat Autònoma de Barcelona, Unitat Docent Parc Taulí, c/ Parc Taulí 1, 08208 Sabadell, Spain
| | - Juan Manuel Fontanet
- Departament de Farmacologia, de Terapèutica i de Toxicologia, Universitat Autònoma de Barcelona, Hospital de Sant Pau, C/St Antoni Maria Claret 167, 08025 Barcelona, Spain
| | - Ferran Torres
- Biostatistics Unit, Faculty of Medicine, Universitat Autònoma de Barcelona, 08193 Bellaterra, Barcelona, Spain
- Medical Statistics Core Facility, IDIBAPS - Hospital Clinic Barcelona, C/Mallorca 183, Floor -1, 08036 Barcelona, Spain
| | - Stavros Nikolakopoulos
- Clinical Trial Methodology, Julius Center for Health Sciences and Primary Care, Biostatistics and Research Support, University Medical Center Utrecht, University of Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
| | - Konstantinos Pateras
- Clinical Trial Methodology, Julius Center for Health Sciences and Primary Care, Biostatistics and Research Support, University Medical Center Utrecht, University of Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
| | - Gerd Rosenkranz
- Section for Medical Statistics, Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Spitalgasse 23, 1090 Vienna, Austria
| | - Martin Posch
- Section for Medical Statistics, Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Spitalgasse 23, 1090 Vienna, Austria
| | - Susanne Urach
- Section for Medical Statistics, Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Spitalgasse 23, 1090 Vienna, Austria
| | - Robin Ristl
- Section for Medical Statistics, Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Spitalgasse 23, 1090 Vienna, Austria
| | - Armin Koch
- Hannover Medical School, Carl-Neuberg-Str. 1, 30625 Hannover, Germany
| | - Spineli Loukia
- Hannover Medical School, Carl-Neuberg-Str. 1, 30625 Hannover, Germany
| | - Johanna H. van der Lee
- Paediatric Clinical Research Office, Woman-Child Center, Academic Medical Center, University of Amsterdam, Amsterdam, the Netherlands
| | - Kit C. B. Roes
- Clinical Trial Methodology, Julius Center for Health Sciences and Primary Care, Biostatistics and Research Support, University Medical Center Utrecht, University of Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
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13
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An optimised multi-arm multi-stage clinical trial design for unknown variance. Contemp Clin Trials 2018; 67:116-120. [PMID: 29474933 PMCID: PMC5886309 DOI: 10.1016/j.cct.2018.02.011] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2017] [Revised: 01/23/2018] [Accepted: 02/19/2018] [Indexed: 11/24/2022]
Abstract
Multi-arm multi-stage trial designs can bring notable gains in efficiency to the drug development process. However, for normally distributed endpoints, the determination of a design typically depends on the assumption that the patient variance in response is known. In practice, this will not usually be the case. To allow for unknown variance, previous research explored the performance of t-test statistics, coupled with a quantile substitution procedure for modifying the stopping boundaries, at controlling the familywise error-rate to the nominal level. Here, we discuss an alternative method based on Monte Carlo simulation that allows the group size and stopping boundaries of a multi-arm multi-stage t-test to be optimised, according to some nominated optimality criteria. We consider several examples, provide R code for general implementation, and show that our designs confer a familywise error-rate and power close to the desired level. Consequently, this methodology will provide utility in future multi-arm multi-stage trials.
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14
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Li X, Wulfsohn MS, Koch GG. Considerations on Testing Secondary Endpoints in Group Sequential Design. Stat Biopharm Res 2018. [DOI: 10.1080/19466315.2017.1375976] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
- Xiaoming Li
- Biostatistics and Programming, Atara Biotherapeutics, Westlake Village, CA
| | | | - Gary G. Koch
- Biostatistics, University of North Carolina, Chapel Hill, NC
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15
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Urach S, Posch M. Multi-arm group sequential designs with a simultaneous stopping rule. Stat Med 2016; 35:5536-5550. [PMID: 27550822 PMCID: PMC5157767 DOI: 10.1002/sim.7077] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2016] [Revised: 07/01/2016] [Accepted: 07/28/2016] [Indexed: 11/08/2022]
Abstract
Multi‐arm group sequential clinical trials are efficient designs to compare multiple treatments to a control. They allow one to test for treatment effects already in interim analyses and can have a lower average sample number than fixed sample designs. Their operating characteristics depend on the stopping rule: We consider simultaneous stopping, where the whole trial is stopped as soon as for any of the arms the null hypothesis of no treatment effect can be rejected, and separate stopping, where only recruitment to arms for which a significant treatment effect could be demonstrated is stopped, but the other arms are continued. For both stopping rules, the family‐wise error rate can be controlled by the closed testing procedure applied to group sequential tests of intersection and elementary hypotheses. The group sequential boundaries for the separate stopping rule also control the family‐wise error rate if the simultaneous stopping rule is applied. However, we show that for the simultaneous stopping rule, one can apply improved, less conservative stopping boundaries for local tests of elementary hypotheses. We derive corresponding improved Pocock and O'Brien type boundaries as well as optimized boundaries to maximize the power or average sample number and investigate the operating characteristics and small sample properties of the resulting designs. To control the power to reject at least one null hypothesis, the simultaneous stopping rule requires a lower average sample number than the separate stopping rule. This comes at the cost of a lower power to reject all null hypotheses. Some of this loss in power can be regained by applying the improved stopping boundaries for the simultaneous stopping rule. The procedures are illustrated with clinical trials in systemic sclerosis and narcolepsy. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.
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Affiliation(s)
- S Urach
- Section for Medical Statistics, Center for Medical Statistics, Informatics, and Intelligent Systems (CEMSIIS), Medical University of Vienna, Spitalgasse 23, A-1090, Wien, Austria
| | - M Posch
- Section for Medical Statistics, Center for Medical Statistics, Informatics, and Intelligent Systems (CEMSIIS), Medical University of Vienna, Spitalgasse 23, A-1090, Wien, Austria
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