1
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Garcia Barrado L, Burzykowski T. Using an early outcome as the sole source of information of interim decisions regarding treatment effect on a long-term endpoint: The non-Gaussian case. Pharm Stat 2024. [PMID: 38837876 DOI: 10.1002/pst.2398] [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: 04/26/2023] [Revised: 04/10/2024] [Accepted: 05/03/2024] [Indexed: 06/07/2024]
Abstract
In randomized clinical trials that use a long-term efficacy endpoint, the follow-up time necessary to observe the endpoint may be substantial. In such trials, an attractive option is to consider an interim analysis based solely on an early outcome that could be used to expedite the evaluation of treatment's efficacy. Garcia Barrado et al. (Pharm Stat. 2022; 21: 209-219) developed a methodology that allows introducing such an early interim analysis for the case when both the early outcome and the long-term endpoint are normally-distributed, continuous variables. We extend the methodology to any combination of the early-outcome and long-term-endpoint types. As an example, we consider the case of a binary outcome and a time-to-event endpoint. We further evaluate the potential gain in operating characteristics (power, expected trial duration, and expected sample size) of a trial with such an interim analysis in function of the properties of the early outcome as a surrogate for the long-term endpoint.
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Affiliation(s)
- Leandro Garcia Barrado
- International Drug Development Institute (IDDI), Louvain-la-Neuve, Belgium
- Institute of Statistics, Biostatistics, and Actuarial Sciences (ISBA), Louvain Institute for Data Analysis and Modeling, Louvain-la-Neuve, Belgium
| | - Tomasz Burzykowski
- International Drug Development Institute (IDDI), Louvain-la-Neuve, Belgium
- Data Science Institute, I-BioStat, Hasselt University, Hasselt, Belgium
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2
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Wu L, Lin J. An adaptive seamless 2-in-1 design with biomarker-driven subgroup enrichment. J Biopharm Stat 2024:1-15. [PMID: 38651758 DOI: 10.1080/10543406.2024.2341683] [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: 10/02/2023] [Accepted: 04/05/2024] [Indexed: 04/25/2024]
Abstract
Adaptive seamless phase 2/3 subgroup enrichment design plays a pivotal role in streamlining efficient drug development within a competitive landscape, while also enhancing patient access to promising treatments. This design approach identifies biomarker subgroups with the highest potential to benefit from investigational regimens. The seamless integration of Phase 2 and Phase 3 ensures a timely confirmation of clinical benefits. One significant challenge in adaptive enrichment decisions is determining the optimal timing and maturity of the primary endpoint. In this paper, we propose an adaptive seamless 2-in-1 biomarker-driven subgroup enrichment design that addresses this challenge by allowing subgroup selection using an early intermediate endpoint that predicts clinical benefits (i.e. a surrogate endpoint). The proposed design initiates with a Phase 2 stage involving all participants and can potentially expand into a Phase 3 study focused on the subgroup demonstrating the most favorable clinical outcomes. We will show that, under certain correlation assumptions, the overall type I error may not be inflated at the end of the study. In scenarios where the assumptions may not hold, we present a general framework to control the multiplicity. The flexibility and efficacy of the proposed design are highlighted through an extensive simulation study and illustrated in a case study in multiple myeloma.
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Affiliation(s)
- Liwen Wu
- Statistical and Quantitative Sciences, Takeda Pharmaceuticals, Cambridge, Massachusetts, USA
| | - Jianchang Lin
- Statistical and Quantitative Sciences, Takeda Pharmaceuticals, Cambridge, Massachusetts, USA
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3
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Gera RG, Friede T. Blinded sample size recalculation in multiple composite population designs with normal data and baseline adjustments. Biom J 2023; 65:e2000326. [PMID: 37309256 DOI: 10.1002/bimj.202000326] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Revised: 12/13/2022] [Accepted: 03/07/2023] [Indexed: 06/14/2023]
Abstract
The increasing interest in subpopulation analysis has led to the development of various new trial designs and analysis methods in the fields of personalized medicine and targeted therapies. In this paper, subpopulations are defined in terms of an accumulation of disjoint population subsets and will therefore be called composite populations. The proposed trial design is applicable to any set of composite populations, considering normally distributed endpoints and random baseline covariates. Treatment effects for composite populations are tested by combining p-values, calculated on the subset levels, using the inverse normal combination function to generate test statistics for those composite populations while the closed testing procedure accounts for multiple testing. Critical boundaries for intersection hypothesis tests are derived using multivariate normal distributions, reflecting the joint distribution of composite population test statistics given no treatment effect exists. For sample size calculation and sample size, recalculation multivariate normal distributions are derived which describe the joint distribution of composite population test statistics under an assumed alternative hypothesis. Simulations demonstrate the absence of any practical relevant inflation of the type I error rate. The target power after sample size recalculation is typically met or close to being met.
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Affiliation(s)
- Roland G Gera
- Department of Medical Statistics, University Medical Centre Göttingen, Göttingen, Germany
| | - Tim Friede
- Department of Medical Statistics, University Medical Centre Göttingen, Göttingen, Germany
- DZHK (German Center for Cardiovascular Research), Partner Site Göttingen, Göttingen, Germany
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4
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Han L, Arfè A, Trippa L. Sensitivity Analyses of Clinical Trial Designs: Selecting Scenarios and Summarizing Operating Characteristics. AM STAT 2023; 78:76-87. [PMID: 38680760 PMCID: PMC11052542 DOI: 10.1080/00031305.2023.2216253] [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: 01/20/2023] [Accepted: 05/14/2023] [Indexed: 05/01/2024]
Abstract
The use of simulation-based sensitivity analyses is fundamental for evaluating and comparing candidate designs of future clinical trials. In this context, sensitivity analyses are especially useful to assess the dependence of important design operating characteristics with respect to various unknown parameters. Typical examples of operating characteristics include the likelihood of detecting treatment effects and the average study duration, which depend on parameters that are unknown until after the onset of the clinical study, such as the distributions of the primary outcomes and patient profiles. Two crucial components of sensitivity analyses are (i) the choice of a set of plausible simulation scenarios and (ii) the list of operating characteristics of interest. We propose a new approach for choosing the set of scenarios to be included in a sensitivity analysis. We maximize a utility criterion that formalizes whether a specific set of sensitivity scenarios is adequate to summarize how the operating characteristics of the trial design vary across plausible values of the unknown parameters. Then, we use optimization techniques to select the best set of simulation scenarios (according to the criteria specified by the investigator) to exemplify the operating characteristics of the trial design. We illustrate our proposal in three trial designs.
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Affiliation(s)
- Larry Han
- Department of Biostatistics, Harvard T.H. Chan School of Public Health
| | - Andrea Arfè
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center
| | - Lorenzo Trippa
- Department of Biostatistics, Harvard T.H. Chan School of Public Health
- Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute
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5
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Duputel B, Stallard N, Montestruc F, Zohar S, Ursino M. Using dichotomized survival data to construct a prior distribution for a Bayesian seamless Phase II/III clinical trial. Stat Methods Med Res 2023; 32:963-977. [PMID: 36919403 PMCID: PMC10521165 DOI: 10.1177/09622802231160554] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/16/2023]
Abstract
Master protocol designs allow for simultaneous comparison of multiple treatments or disease subgroups. Master protocols can also be designed as seamless studies, in which two or more clinical phases are considered within the same trial. They can be divided into two categories: operationally seamless, in which the two phases are separated into two independent studies, and inferentially seamless, in which the interim analysis is considered an adaptation of the study. Bayesian designs are scarcely studied. Our aim is to propose and compare Bayesian operationally seamless Phase II/III designs using a binary endpoint for the first stage and a time-to-event endpoint for the second stage. At the end of Phase II, arm selection is based on posterior (futility) and predictive (selection) probabilities. The results of the first phase are then incorporated into prior distributions of a time-to-event model. Simulation studies showed that Bayesian operationally seamless designs can approach the inferentially seamless counterpart, allowing for an increasing simulated power with respect to the operationally frequentist design.
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Affiliation(s)
- Benjamin Duputel
- Universitè Paris Citè, Sorbonne Université, Inserm, Centre de Recherche des Cordeliers, Paris, France
- Inria, HeKA, Paris, France
- eXYSTAT, Malakoff, France
| | - Nigel Stallard
- Warwick Clinical Trials Unit, Warwick Medical School, University of Warwick, Coventry, UK
| | | | - Sarah Zohar
- Universitè Paris Citè, Sorbonne Université, Inserm, Centre de Recherche des Cordeliers, Paris, France
- Inria, HeKA, Paris, France
| | - Moreno Ursino
- Universitè Paris Citè, Sorbonne Université, Inserm, Centre de Recherche des Cordeliers, Paris, France
- Inria, HeKA, Paris, France
- Unit of Clinical Epidemiology, Assistance Publique-Hôpitaux de Paris, CHU Robert Debrè, Paris, France
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6
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Chen X, Zhang J, Jiang L, Yan F. IBIS: identify biomarker-based subgroups with a Bayesian enrichment design for targeted combination therapy. BMC Med Res Methodol 2023; 23:66. [PMID: 36941537 PMCID: PMC10026491 DOI: 10.1186/s12874-023-01877-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Accepted: 02/24/2023] [Indexed: 03/23/2023] Open
Abstract
BACKGROUND Combination therapies directed at multiple targets have potentially improved treatment effects for cancer patients. Compared to monotherapy, targeted combination therapy leads to an increasing number of subgroups and complicated biomarker-based efficacy profiles, making it more difficult for efficacy evaluation in clinical trials. Therefore, it is necessary to develop innovative clinical trial designs to explore the efficacy of targeted combination therapy in different subgroups and identify patients who are more likely to benefit from the investigational combination therapy. METHODS We propose a statistical tool called 'IBIS' to Identify BIomarker-based Subgroups and apply it to the enrichment design framework. The IBIS contains three main elements: subgroup division, efficacy evaluation and subgroup identification. We first enumerate all possible subgroup divisions based on biomarker levels. Then, Jensen-Shannon divergence is used to distinguish high-efficacy and low-efficacy subgroups, and Bayesian hierarchical model (BHM) is employed to borrow information within these two subsets for efficacy evaluation. Regarding subgroup identification, a hypothesis testing framework based on Bayes factors is constructed. This framework also plays a key role in go/no-go decisions and enriching specific population. Simulation studies are conducted to evaluate the proposed method. RESULTS The accuracy and precision of IBIS could reach a desired level in terms of estimation performance. In regard to subgroup identification and population enrichment, the proposed IBIS has superior and robust characteristics compared with traditional methods. An example of how to obtain design parameters for an adaptive enrichment design under the IBIS framework is also provided. CONCLUSIONS IBIS has the potential to be a useful tool for biomarker-based subgroup identification and population enrichment in clinical trials of targeted combination therapy.
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Affiliation(s)
- Xin Chen
- Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Jingyi Zhang
- Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Liyun Jiang
- Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Fangrong Yan
- Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, China.
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7
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Schöffski P, Lahmar M, Lucarelli A, Maki RG. Brightline-1: phase II/III trial of the MDM2-p53 antagonist BI 907828 versus doxorubicin in patients with advanced DDLPS. Future Oncol 2023; 19:621-629. [PMID: 36987836 DOI: 10.2217/fon-2022-1291] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/30/2023] Open
Abstract
Dedifferentiated liposarcoma (DDLPS) is a rare, aggressive liposarcoma associated with poor prognosis. First-line treatment for advanced/metastatic DDLPS is systemic chemotherapy, but efficacy is poor and toxicities substantial. Most DDLPS tumors have amplification of the MDM2 gene, which encodes a negative regulator of the p53 suppressor protein. BI 907828 is a highly potent, oral MDM2-p53 antagonist that inhibits the interaction between p53 and MDM2, thereby restoring p53 activity. BI 907828 has shown promising activity in preclinical studies and in a phase Ia/Ib study in patients with solid tumors, particularly those with DDLPS. This manuscript describes the rationale and design of an ongoing multicenter, randomized, phase II/III trial (Brightline-1; NCT05218499) evaluating BI 907828 versus doxorubicin as first-line treatment for advanced DDLPS.
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Affiliation(s)
- Patrick Schöffski
- Department of General Medical Oncology, University Hospitals Leuven, Leuven Cancer Institute, & Department of Oncology, KU Leuven, Laboratory of Experimental Oncology, Leuven, Belgium
| | - Mehdi Lahmar
- Boehringer Ingelheim International GmbH, Ingelheim am Rhein, Germany
| | | | - Robert G Maki
- Abramson Cancer Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Memorial Sloan Kettering Cancer Center, New York, NY, USA
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8
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Stallard N. Rejoinder to discussion on "Adaptive enrichment designs with a continuous biomarker". Biometrics 2023; 79:36-38. [PMID: 35363907 DOI: 10.1111/biom.13639] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Accepted: 02/10/2022] [Indexed: 11/28/2022]
Affiliation(s)
- Nigel Stallard
- Statistics and Epidemiology, Division of Health Sciences, Warwick Medical School, University of Warwick, Coventry, UK
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9
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Stallard N. Adaptive enrichment designs with a continuous biomarker. Biometrics 2023; 79:9-19. [PMID: 35174875 DOI: 10.1111/biom.13644] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2021] [Accepted: 09/23/2021] [Indexed: 12/01/2022]
Abstract
A popular design for clinical trials assessing targeted therapies is the two-stage adaptive enrichment design with recruitment in stage 2 limited to a biomarker-defined subgroup chosen based on data from stage 1. The data-dependent selection leads to statistical challenges if data from both stages are used to draw inference on treatment effects in the selected subgroup. If subgroups considered are nested, as when defined by a continuous biomarker, treatment effect estimates in different subgroups follow the same distribution as estimates in a group-sequential trial. This result is used to obtain tests controlling the familywise type I error rate (FWER) for six simple subgroup selection rules, one of which also controls the FWER for any selection rule. Two approaches are proposed: one based on multivariate normal distributions suitable if the number of possible subgroups, k, is small, and one based on Brownian motion approximations suitable for large k. The methods, applicable in the wide range of settings with asymptotically normal test statistics, are illustrated using survival data from a breast cancer trial.
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Affiliation(s)
- Nigel Stallard
- Statistics and Epidemiology, Division of Health Sciences, Warwick Medical School, University of Warwick, Coventry, UK
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10
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Jennison C. Discussion on "Adaptive enrichment designs with a continuous biomarker" by N. Stallard. Biometrics 2023; 79:26-30. [PMID: 35344206 DOI: 10.1111/biom.13642] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Accepted: 12/23/2021] [Indexed: 11/29/2022]
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11
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Placzek M, Friede T. Blinded sample size recalculation in adaptive enrichment designs. Biom J 2023; 65:e2000345. [PMID: 35983952 DOI: 10.1002/bimj.202000345] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 09/24/2021] [Accepted: 11/07/2021] [Indexed: 12/17/2022]
Abstract
In the precision medicine era, (prespecified) subgroup analyses are an integral part of clinical trials. Incorporating multiple populations and hypotheses in the design and analysis plan, adaptive designs promise flexibility and efficiency in such trials. Adaptations include (unblinded) interim analyses (IAs) or blinded sample size reviews. An IA offers the possibility to select promising subgroups and reallocate sample size in further stages. Trials with these features are known as adaptive enrichment designs. Such complex designs comprise many nuisance parameters, such as prevalences of the subgroups and variances of the outcomes in the subgroups. Additionally, a number of design options including the timepoint of the sample size review and timepoint of the IA have to be selected. Here, for normally distributed endpoints, we propose a strategy combining blinded sample size recalculation and adaptive enrichment at an IA, that is, at an early timepoint nuisance parameters are reestimated and the sample size is adjusted while subgroup selection and enrichment is performed later. We discuss implications of different scenarios concerning the variances as well as the timepoints of blinded review and IA and investigate the design characteristics in simulations. The proposed method maintains the desired power if planning assumptions were inaccurate and reduces the sample size and variability of the final sample size when an enrichment is performed. Having two separate timepoints for blinded sample size review and IA improves the timing of the latter and increases the probability to correctly enrich a subgroup.
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Affiliation(s)
- Marius Placzek
- Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany
| | - Tim Friede
- Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany.,DZHK (German Center for Cardiovascular Research), partner site Göttingen, Göttingen, Germany
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12
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Khan JN, Kimani PK, Glimm E, Stallard N. Adjusting for treatment selection in phase II/III clinical trials with time to event data. Stat Med 2023; 42:146-163. [PMID: 36419206 PMCID: PMC10098876 DOI: 10.1002/sim.9606] [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: 03/22/2022] [Revised: 09/25/2022] [Accepted: 11/01/2022] [Indexed: 11/26/2022]
Abstract
Phase II/III clinical trials are efficient two-stage designs that test multiple experimental treatments. In stage 1, patients are allocated to the control and all experimental treatments, with the data collected from them used to select experimental treatments to continue to stage 2. Patients recruited in stage 2 are allocated to the selected treatments and the control. Combined data of stage 1 and stage 2 are used for a confirmatory phase III analysis. Appropriate analysis needs to adjust for selection bias of the stage 1 data. Point estimators exist for normally distributed outcome data. Extending these estimators to time to event data is not straightforward because treatment selection is based on correlated treatment effects and stage 1 patients who do not get events in stage 1 are followed-up in stage 2. We have derived an approximately uniformly minimum variance conditional unbiased estimator (UMVCUE) and compared its biases and mean squared errors to existing bias adjusted estimators. In simulations, one existing bias adjusted estimator has similar properties as the practically unbiased UMVCUE while the others can have noticeable biases but they are less variable than the UMVCUE. For confirmatory phase II/III clinical trials where unbiased estimators are desired, we recommend the UMVCUE or the existing estimator with which it has similar properties.
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Affiliation(s)
| | - Peter K Kimani
- Warwick Medical School, University of Warwick, Coventry, UK
| | | | - Nigel Stallard
- Warwick Medical School, University of Warwick, Coventry, UK
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13
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A gated group sequential design for seamless Phase II/III trial with subpopulation selection. BMC Med Res Methodol 2023; 23:2. [PMID: 36597042 PMCID: PMC9809114 DOI: 10.1186/s12874-022-01825-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Accepted: 12/19/2022] [Indexed: 01/04/2023] Open
Abstract
BACKGROUND Due to the high cost and high failure rate of Phase III trials where a classical group sequential design (GSD) is usually used, seamless Phase II/III designs are more and more popular to improve trial efficiency. A potential attraction of Phase II/III design is to allow a randomized proof-of-concept stage prior to committing to the full cost of a Phase III trial. Population selection during the trial allows a trial to adapt and focus investment where it is most likely to provide patient benefit. Previous methods have been developed for this problem when there is a single primary endpoint and two possible populations. METHODS To find the population that potentially benefits with one or two primary endpoints (e.g., progression free survival (PFS), overall survival (OS)), we propose a gated group sequential design for a seamless Phase II/III trial design with adaptive population selection. RESULTS The investigated design controls the familywise error rate and allows multiple interim analyses to enable early stopping for efficacy or futility. Simulations and an illustrative example suggest that the proposed gated group sequential design has more power and requires less time and resources compared to the group sequential design and adaptive design. CONCLUSIONS Combining the group sequential design and adaptive design, the gated group sequential design has more power and higher efficiency while controlling for the familywise error rate. It has the potential to save drug development cost and more quickly fulfill unmet medical needs.
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14
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Liu Y, Kairalla JA, Renfro LA. Bayesian adaptive trial design for a continuous biomarker with possibly nonlinear or nonmonotone prognostic or predictive effects. Biometrics 2022; 78:1441-1453. [PMID: 34415052 PMCID: PMC8858338 DOI: 10.1111/biom.13550] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2020] [Accepted: 07/22/2021] [Indexed: 12/30/2022]
Abstract
As diseases like cancer are increasingly understood on a molecular level, clinical trials are being designed to reveal or validate subpopulations in which an experimental therapy has enhanced benefit. Such biomarker-driven designs, particularly "adaptive enrichment" designs that initially enroll an unselected population and then allow for later restriction of accrual to "marker-positive" patients based on interim results, are increasingly popular. Many biomarkers of interest are naturally continuous, however, and most existing design approaches either require upfront dichotomization or force monotonicity through algorithmic searches for a single marker threshold, thereby excluding the possibility that the continuous biomarker has a nondisjoint and truly nonlinear or nonmonotone prognostic relationship with outcome or predictive relationship with treatment effect. To address this, we propose a novel trial design that leverages both the actual shapes of any continuous marker effects (both prognostic and predictive) and their corresponding posterior uncertainty in an adaptive decision-making framework. At interim analyses, this marker knowledge is updated and overall or marker-driven decisions are reached such as continuing enrollment to the next interim analysis or terminating early for efficacy or futility. Using simulations and patient-level data from a multi-center Children's Oncology Group trial in Acute Lymphoblastic Leukemia, we derive the operating characteristics of our design and compare its performance to a traditional approach that identifies and applies a dichotomizing marker threshold.
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Affiliation(s)
- Yusha Liu
- Department of Human Genetics, University of Chicago, Chicago, Illinois, USA
| | - John A Kairalla
- Department of Biostatistics, University of Florida, Gainesville, Florida, USA
| | - Lindsay A Renfro
- Division of Biostatistics, University of Southern California and Children's Oncology Group, Los Angeles, California, USA
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15
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Collignon O, Schiel A, Burman C, Rufibach K, Posch M, Bretz F. Estimands and Complex Innovative Designs. Clin Pharmacol Ther 2022; 112:1183-1190. [PMID: 35253205 PMCID: PMC9790227 DOI: 10.1002/cpt.2575] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Accepted: 03/01/2022] [Indexed: 01/31/2023]
Abstract
Since the release of the ICH E9(R1) (International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use Addendum on Estimands and Sensitivity Analysis in Clinical Trials to the Guideline on Statistical Principles for Clinical Trials) document in 2019, the estimand framework has become a fundamental part of clinical trial protocols. In parallel, complex innovative designs have gained increased popularity in drug development, in particular in early development phases or in difficult experimental situations. While the estimand framework is relevant to any study in which a treatment effect is estimated, experience is lacking as regards its application to these designs. In a basket trial for example, should a different estimand be specified for each subpopulation of interest, defined, for example, by cancer site? Or can a single estimand focusing on the general population (defined, for example, by the positivity to a certain biomarker) be used? In the case of platform trials, should a different estimand be proposed for each drug investigated? In this work we discuss possible ways of implementing the estimand framework for different types of complex innovative designs. We consider trials that allow adding or selecting experimental treatment arms, modifying the control arm or the standard of care, and selecting or pooling populations. We also address the potentially data-driven, adaptive selection of estimands in an ongoing trial and disentangle certain statistical issues that pertain to estimation rather than to estimands, such as the borrowing of nonconcurrent information. We hope this discussion will facilitate the implementation of the estimand framework and its description in the study protocol when the objectives of the trial require complex innovative designs.
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Affiliation(s)
| | | | - Carl‐Fredrik Burman
- Statistical Innovation, Data Science & Artificial IntelligenceAstraZeneca Research & DevelopmentGothenburgSweden
| | - Kaspar Rufibach
- Methods, Collaboration, and Outreach Group, Product Development Data SciencesF.Hoffmann‐La RocheBaselSwitzerland
| | - Martin Posch
- Section for Medical StatisticsCenter for Medical Statistics Informatics, and Intelligent SystemsMedical University of ViennaViennaAustria
| | - Frank Bretz
- Section for Medical StatisticsCenter for Medical Statistics Informatics, and Intelligent SystemsMedical University of ViennaViennaAustria,NovartisBaselSwitzerland
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16
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Zhang W, Ro S, Jiang Q, Li X, Liu R, Lu C'C, Marchenko O, Zhao J, Xu Z. Statistical and Operational Considerations for 2-Stage Adaptive Designs with Simultaneous Evaluation of Overall and Marker-Selected Populations in Oncology Confirmatory Trials. Ther Innov Regul Sci 2022; 56:552-560. [PMID: 35503503 DOI: 10.1007/s43441-022-00407-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Accepted: 04/07/2022] [Indexed: 11/24/2022]
Abstract
In biomarker enrichment study designs that start with an all-comer population, simultaneous evaluation of the entire and the marker-selected populations can be more desirable than pre-specifying the testing order, when the degree of marker predictiveness is uncertain. While there has been substantial research on this approach, our goal is to provide a complete overview and guidance in all aspects of this approach, including the interim analysis potentially using different endpoints, combination tests with associated multiplicity control, and the final treatment effect estimation. Regulatory/operational aspects and actual cases demonstrating the potential advantage of this approach are also described.
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Affiliation(s)
| | - Sunhee Ro
- Sierra Oncology, Inc., San Mateo, CA, USA
| | | | | | - Rong Liu
- Bristol Myers Squibb, Co., New York, NY, USA
| | | | | | - Jing Zhao
- Merck & Co, Inc., Kenilworth, NJ, USA
| | - Zhenzhen Xu
- Food and Drug Administration, Silver Spring, MD, USA
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17
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Zhong C, Li Q, Wu L, Lin J. Using surrogate information to improve confirmatory platform trial with sample size re-estimation. J Biopharm Stat 2022; 32:547-566. [PMID: 35714331 DOI: 10.1080/10543406.2022.2080693] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
Platform design which allows exploring multiple arms with a common control simultaneously is becoming essential for efficient drug development. However, one of the critical challenges for confirmatory platform trials is immature data for interim decisions, particularly for the treatment arm selection and sample size determination with limited data available. We use a modified conditional power (CP) for both treatment arm selection and sample size determination at interim analysis for the proposed platform trial. The modified CP uses the available data from both primary and surrogate endpoints. We also demonstrated the application in a case study of a lung cancer trial.
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Affiliation(s)
- Chengxue Zhong
- Department of Biostatistics and Data Science, Biostatistics, University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Qing Li
- Biostatistics and data management, MorphoSys US Inc, Boston, Massachusetts, USA
| | - Liwen Wu
- Statistical & Quantitative Sciences, Takeda Pharmaceuticals, Cambridge, Massachusetts
| | - Jianchang Lin
- Statistical & Quantitative Sciences, Takeda Pharmaceuticals, Cambridge, Massachusetts
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18
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Park Y, Liu S. A randomized group sequential enrichment design for immunotherapy and targeted therapy. Contemp Clin Trials 2022; 116:106742. [DOI: 10.1016/j.cct.2022.106742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Revised: 03/02/2022] [Accepted: 03/26/2022] [Indexed: 11/25/2022]
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19
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Laska E, Siegel C, Lin Z. A likely responder approach for the analysis of randomized controlled trials. Contemp Clin Trials 2022; 114:106688. [PMID: 35085831 PMCID: PMC8934276 DOI: 10.1016/j.cct.2022.106688] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Revised: 12/03/2021] [Accepted: 01/19/2022] [Indexed: 11/16/2022]
Abstract
OBJECTIVE To further the precision medicine goal of tailoring medical treatment to individual patient characteristics by providing a method of analysis of the effect of test treatment, T, compared to a reference treatment, R, in participants in a RCT who are likely responders to T. METHODS Likely responders to T are individuals whose expected response at baseline exceeds a prespecified minimum. A prognostic score, the expected response predicted as a function of baseline covariates, is obtained at trial completion. It is a balancing score that can be used to match likely responders randomized to T with those randomized to R; the result is comparable treatment groups that have a common covariance distribution. Treatments are compared based on observed outcomes in this enriched sample. The approach is illustrated in a RCT comparing two treatments for opioid use disorder. RESULTS A standard statistical analysis of the opioid use disorder RCT found no treatment difference in the total sample. However, a subset of likely responders to T were identified and in this group, T was statistically superior to R. CONCLUSION The causal treatment effect of T relative to R among likely responders may be more important than the effect in the whole target population. The prognostic score function provides quantitative information to support patient specific treatment decisions regarding T furthering the goal of precision medicine.
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Affiliation(s)
- Eugene Laska
- Department of Psychiatry, New York University Grossman School of Medicine, One Park Avenue, New York, NY 10016, USA; Department of Population Health, Division of Biostatistics, NYU Grossman School of Medicine, 180 Madison Avenue, New York, NY 10016, USA.
| | - Carole Siegel
- Department of Psychiatry, New York University Grossman School of Medicine, One Park Avenue, New York, NY 10016, USA; Department of Population Health, Division of Biostatistics, NYU Grossman School of Medicine, 180 Madison Avenue, New York, NY 10016, USA.
| | - Ziqiang Lin
- Department of Psychiatry, New York University Grossman School of Medicine, One Park Avenue, New York, NY 10016, USA.
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20
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Wu L, Li Q, Liu M, Lin J. Incorporating Surrogate Information for Adaptive Subgroup Enrichment Design with Sample Size Re-estimation. Stat Biopharm Res 2022. [DOI: 10.1080/19466315.2022.2046150] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Affiliation(s)
- Liwen Wu
- Takeda Pharmaceuticals, 40 Landsdowne Street, Cambridge, MA, 02139, USA
| | - Qing Li
- MorphoSys US Inc., 470 Atlantic Ave 14th Floor, Boston, MA, 02210, USA
| | - Mengya Liu
- Takeda Pharmaceuticals, 40 Landsdowne Street, Cambridge, MA, 02139, USA
| | - Jianchang Lin
- Takeda Pharmaceuticals, 40 Landsdowne Street, Cambridge, MA, 02139, USA
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21
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Danzer MF, Terzer T, Berthold F, Faldum A, Schmidt R. Confirmatory adaptive group sequential designs for single-arm phase II studies with multiple time-to-event endpoints. Biom J 2022; 64:312-342. [PMID: 35152459 DOI: 10.1002/bimj.202000205] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 05/26/2021] [Accepted: 05/30/2021] [Indexed: 11/07/2022]
Abstract
Existing methods concerning the assessment of long-term survival outcomes in one-armed trials are commonly restricted to one primary endpoint. Corresponding adaptive designs suffer from limitations regarding the use of information from other endpoints in interim design changes. Here we provide adaptive group sequential one-sample tests for testing hypotheses on the multivariate survival distribution derived from multi-state models, while making provision for data-dependent design modifications based on all involved time-to-event endpoints. We explicitly elaborate application of the methodology to one-sample tests for the joint distribution of (i) progression-free survival (PFS) and overall survival (OS) in the context of an illness-death model, and (ii) time to toxicity and time to progression while accounting for death as a competing event. Large sample distributions are derived using a counting process approach. Small sample properties are studied by simulation. An already established multi-state model for non-small cell lung cancer is used to illustrate the adaptive procedure.
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Affiliation(s)
- Moritz Fabian Danzer
- Institute of Biostatistics and Clinical Research, University of Münster, Münster, Germany
| | - Tobias Terzer
- Division of Biostatistics, German Cancer Research Center, Heidelberg, Germany
| | - Frank Berthold
- Department of Pediatric Oncology and Hematology, Center for Integrated Oncology, University of Cologne, Cologne, Germany
| | - Andreas Faldum
- Institute of Biostatistics and Clinical Research, University of Münster, Münster, Germany
| | - Rene Schmidt
- Institute of Biostatistics and Clinical Research, University of Münster, Münster, Germany
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22
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A comparison of estimation methods adjusting for selection bias in adaptive enrichment designs with time‐to‐event endpoints. Stat Med 2022; 41:1767-1779. [DOI: 10.1002/sim.9327] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Revised: 01/03/2022] [Accepted: 01/04/2022] [Indexed: 11/07/2022]
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23
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Feld J, Faldum A, Schmidt R. Adaptive group sequential survival comparisons based on log-rank and pointwise test statistics. Stat Methods Med Res 2021; 30:2562-2581. [PMID: 34641702 PMCID: PMC8649467 DOI: 10.1177/09622802211043262] [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: 11/16/2022]
Abstract
Whereas the theory of confirmatory adaptive designs is well understood for uncensored data, implementation of adaptive designs in the context of survival trials remains challenging. Commonly used adaptive survival tests are based on the independent increments structure of the log-rank statistic. This implies some relevant limitations: On the one hand, essentially only the interim log-rank statistic may be used for design modifications (such as data-dependent sample size recalculation). Furthermore, the treatment arm allocation ratio in these classical methods is assumed to be constant throughout the trial period. Here, we propose an extension of the independent increments approach to adaptive survival tests that addresses some of these limitations. We present a confirmatory adaptive two-sample log-rank test that allows rejection regions and sample size recalculation rules to be based not only on the interim log-rank statistic, but also on point-wise survival rate estimates, simultaneously. In addition, the possibility is opened to adapt the treatment arm allocation ratio after each interim analysis in a data-dependent way. The ability to include point-wise survival rate estimators in the rejection region of a test for comparing survival curves might be attractive, e.g., for seamless phase II/III designs. Data-dependent adaptation of the allocation ratio could be helpful in multi-arm trials in order to successively steer recruitment into the study arms with the greatest chances of success. The methodology is motivated by the LOGGIC Europe Trial from pediatric oncology. Distributional properties are derived using martingale techniques in the large sample limit. Small sample properties are studied by simulation.
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Affiliation(s)
- Jannik Feld
- 352489Institute of Biostatistics and Clinical Research, 9185University of Münster, Muenster, Germany
| | - Andreas Faldum
- 352489Institute of Biostatistics and Clinical Research, 9185University of Münster, Muenster, Germany
| | - Rene Schmidt
- 352489Institute of Biostatistics and Clinical Research, 9185University of Münster, Muenster, Germany
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24
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Garcia Barrado L, Burzykowski T, Legrand C, Buyse M. Using an interim analysis based exclusively on an early outcome in a randomized clinical trial with a long-term clinical endpoint. Pharm Stat 2021; 21:209-219. [PMID: 34505395 DOI: 10.1002/pst.2165] [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: 01/07/2021] [Revised: 06/04/2021] [Accepted: 07/15/2021] [Indexed: 11/10/2022]
Abstract
In RCTs with an interest in a long-term efficacy endpoint, the follow-up time necessary to observe the endpoint may be substantial. In order to reduce the expected duration of such trials, early-outcome data may be collected to enrich an interim analysis aimed at stopping the trial early for efficacy. We propose to extend such a design with an additional interim analysis using solely early-outcome data in order to expedite the evaluation of treatment's efficacy. We evaluate the potential gain in operating characteristics (power, expected trial duration, and expected sample size) when introducing such an early interim analysis, in function of the properties of the early outcome as a surrogate for the long-term endpoint. In the context of a longitudinal age-related macular degeneration (ARMD) ophthalmology trial, results show potentially substantial gains in both the expected trial duration and the expected sample size. A prerequisite, though, is that the treatment effect on the early outcome has to be strongly correlated with the treatment effect on the long-term endpoint, that is, that the early outcome is a validated surrogate for the long-term endpoint.
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Affiliation(s)
- Leandro Garcia Barrado
- International Drug Development Institute (IDDI), Louvain-la-Neuve, Belgium.,Institute of Statistics, Biostatistics, and Actuarial Sciences (ISBA), Louvain Institute for Data Analysis and Modeling, Louvain-la-Neuve, Belgium
| | - Tomasz Burzykowski
- International Drug Development Institute (IDDI), Louvain-la-Neuve, Belgium.,Data Science Institute, I-BioStat, Hasselt University, Hasselt, Belgium
| | - Catherine Legrand
- Institute of Statistics, Biostatistics, and Actuarial Sciences (ISBA), Louvain Institute for Data Analysis and Modeling, Louvain-la-Neuve, Belgium
| | - Marc Buyse
- International Drug Development Institute (IDDI), Louvain-la-Neuve, Belgium.,Data Science Institute, I-BioStat, Hasselt University, Hasselt, Belgium
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25
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Zhang C, Mayo MS, Wick JA, Gajewski BJ. Designing and analyzing clinical trials for personalized medicine via Bayesian models. Pharm Stat 2021; 20:573-596. [PMID: 33463906 DOI: 10.1002/pst.2095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2019] [Revised: 09/21/2020] [Accepted: 12/31/2020] [Indexed: 11/11/2022]
Abstract
Patients with different characteristics (e.g., biomarkers, risk factors) may have different responses to the same medicine. Personalized medicine clinical studies that are designed to identify patient subgroup treatment efficacies can benefit patients and save medical resources. However, subgroup treatment effect identification complicates the study design in consideration of desired operating characteristics. We investigate three Bayesian adaptive models for subgroup treatment effect identification: pairwise independent, hierarchical, and cluster hierarchical achieved via Dirichlet Process (DP). The impact of interim analysis and longitudinal data modeling on the personalized medicine study design is also explored. Interim analysis is considered since they can accelerate personalized medicine studies in cases where early stopping rules for success or futility are met. We apply integrated two-component prediction method (ITP) for longitudinal data simulation, and simple linear regression for longitudinal data imputation to optimize the study design. The designs' performance in terms of power for the subgroup treatment effects and overall treatment effect, sample size, and study duration are investigated via simulation. We found the hierarchical model is an optimal approach to identifying subgroup treatment effects, and the cluster hierarchical model is an excellent alternative approach in cases where sufficient information is not available for specifying the priors. The interim analysis introduction to the study design lead to the trade-off between power and expected sample size via the adjustment of the early stopping criteria. The introduction of the longitudinal modeling slightly improves the power. These findings can be applied to future personalized medicine studies with discrete or time-to-event endpoints.
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Affiliation(s)
- Chuanwu Zhang
- Department of Biostatistics & Data Science, University of Kansas Medical Center, Kansas City, Kansas, USA.,Sanofi, Waltham, Massachusetts, USA
| | - Matthew S Mayo
- Department of Biostatistics & Data Science, University of Kansas Medical Center, Kansas City, Kansas, USA
| | - Jo A Wick
- Department of Biostatistics & Data Science, University of Kansas Medical Center, Kansas City, Kansas, USA
| | - Byron J Gajewski
- Department of Biostatistics & Data Science, University of Kansas Medical Center, Kansas City, Kansas, USA
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26
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Park Y, Liu S, Thall PF, Yuan Y. Bayesian group sequential enrichment designs based on adaptive regression of response and survival time on baseline biomarkers. Biometrics 2021; 78:60-71. [PMID: 33438761 DOI: 10.1111/biom.13421] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2020] [Revised: 12/08/2020] [Accepted: 12/22/2020] [Indexed: 11/29/2022]
Abstract
Precision medicine relies on the idea that, for a particular targeted agent, only a subpopulation of patients is sensitive to it and thus may benefit from it therapeutically. In practice, it is often assumed based on preclinical data that a treatment-sensitive subpopulation is known, and moreover that the agent is substantively efficacious in that subpopulation. Due to important differences between preclinical settings and human biology, however, data from patients treated with a new targeted agent often show that one or both of these assumptions are false. This paper provides a Bayesian randomized group sequential enrichment design that compares an experimental treatment to a control based on survival time and uses early response as an ancillary outcome to assist with adaptive variable selection and enrichment. Initially, the design enrolls patients under broad eligibility criteria. At each interim decision, submodels for regression of response and survival time on a baseline covariate vector and treatment are fit; variable selection is used to identify a covariate subvector that characterizes treatment-sensitive patients and determines a personalized benefit index, and comparative superiority and futility decisions are made. Enrollment of each cohort is restricted to the most recent adaptively identified treatment-sensitive patients. Group sequential decision cutoffs are calibrated to control overall type I error and account for the adaptive enrollment restriction. The design provides a basis for precision medicine by identifying a treatment-sensitive subpopulation, if it exists, and determining whether the experimental treatment is superior to the control in that subpopulation. A simulation study shows that the proposed design reliably identifies a sensitive subpopulation, yields much higher generalized power compared to several existing enrichment designs and a conventional all-comers group sequential design, and is robust.
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Affiliation(s)
- Yeonhee Park
- Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, Wisconsin, USA
| | - Suyu Liu
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Peter F Thall
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Ying Yuan
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
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27
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Lin R, Yang Z, Yuan Y, Yin G. Sample size re-estimation in adaptive enrichment design. Contemp Clin Trials 2020; 100:106216. [PMID: 33246098 DOI: 10.1016/j.cct.2020.106216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Revised: 10/23/2020] [Accepted: 11/10/2020] [Indexed: 10/22/2022]
Abstract
Clinical trial participants are often heterogeneous, which is a fundamental problem in the rapidly developing field of precision medicine. Participants heterogeneity causes considerable difficulty in the current phase III trial designs. Adaptive enrichment designs provide a flexible and intuitive solution. At the interim analysis, we enrich the subgroup of trial participants who have a higher likelihood to benefit from the new treatment. However, it is critical to control the level of the test size and maintain adequate power after enrichment of certain subgroup of participants. We develop two adaptive enrichment strategies with sample size re-estimation and verify their feasibility and practicability through extensive simulations and sensitivity analyses. The simulation studies show that the proposed methods can control the overall type I error rate and exhibit competitive improvement in terms of statistical power and expected sample size. The proposed designs are exemplified with a real trial application.
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Affiliation(s)
- Ruitao Lin
- Department of Biostatistics, The University of Texas M. D. Anderson Cancer Center, Houston, TX 77030, USA
| | - Zhao Yang
- Department of Statistics and Actuarial Science, The University of Hong Kong, Pokfulam Road, Hong Kong, China
| | - Ying Yuan
- Department of Biostatistics, The University of Texas M. D. Anderson Cancer Center, Houston, TX 77030, USA
| | - Guosheng Yin
- Department of Statistics and Actuarial Science, The University of Hong Kong, Pokfulam Road, Hong Kong, China.
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28
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Liu Y, Xu H. Sample size re-estimation for pivotal clinical trials. Contemp Clin Trials 2020; 102:106215. [PMID: 33217555 DOI: 10.1016/j.cct.2020.106215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Revised: 10/13/2020] [Accepted: 11/10/2020] [Indexed: 10/22/2022]
Abstract
It is well known that if the hypothesis test is left unchanged, the Type I error rate may be inflated for sample size re-estimation (SSR) designs. To address this issue, three main approaches have been proposed in the literature: combination test, conditional error and conventional test with sample size increase in the allowable region (AR) only. These three seemingly different approaches are in fact connected. For each combination test, there is a corresponding conditional error function and AR. Designing adaptation rules in this AR with conventional test guarantees the Type I error rate control but at the same time always leads to smaller power comparing to the corresponding combination test (or conditional error) approach. In cases where conventional test is still preferable, step-wise type adaptation rules that do not fully reside in the AR can be alternatively considered. We believe controversies in the statistical community on the efficiency comparisons between group sequential (GS) and SSR design stem partially from the misalignment of performance metrics and conditional versus unconditional evaluations. We advocate summary metrics, such as median, variance or tail probabilities of the sample size in addition to expectation and personalizing efficiency definition for each trial sponsor. Conditional metrics by favorable, promising and unfavorable zones of the interim results provide additional insights and should always be incorporated into the decision-making process.
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Affiliation(s)
- Yi Liu
- Nektar Therapeutics, San Francisco, CA 94107, USA.
| | - Heng Xu
- Nektar Therapeutics, San Francisco, CA 94107, USA
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29
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Joshi N, Nguyen C, Ivanova A. Multi-stage adaptive enrichment trial design with subgroup estimation. J Biopharm Stat 2020; 30:1038-1049. [PMID: 33073685 DOI: 10.1080/10543406.2020.1832109] [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: 01/02/2023]
Abstract
We consider the problem of estimating the best subgroup and testing for treatment effect in a clinical trial. We define the best subgroup as the subgroup that maximizes a utility function that reflects the trade-off between the subgroup size and the treatment effect. For moderate effect sizes and sample sizes, simpler methods for subgroup estimation worked better than more complex tree-based regression approaches. We propose a three-stage design with a weighted inverse normal combination test to test the hypothesis of no treatment effect across the three stages.
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Affiliation(s)
- Neha Joshi
- Department of Biostatistics, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Crystal Nguyen
- Department of Biostatistics, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Anastasia Ivanova
- Department of Biostatistics, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
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30
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Zhan T, Zhang H, Hartford A, Mukhopadhyay S. Modified Goldilocks Design with strict type I error control in confirmatory clinical trials. J Biopharm Stat 2020; 30:821-833. [PMID: 32297825 DOI: 10.1080/10543406.2020.1744620] [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: 10/24/2022]
Abstract
Goldilocks Design (GD) utilizes predictive probability to adaptively select a trial's sample size based on accumulating data. In order to control type I error at a desired level for a subset of the null space, extensive simulations at the study design stage are required to choose critical values, which is a challenge for this type of Bayesian adaptive design to be used for confirmatory trials. In this article, we propose a Modified Goldilocks Design (MGD) where type I error is analytically controlled over the entire null space. We do so by applying the conditional invariance principle and a combination test approach on [Formula: see text]-values that are obtained from independent cohorts of subjects. Simulation studies show that despite analytic control of type I error rate, the proposed MGD has similar power when compared with the original GD. We further apply it to an example trial with time-to-event endpoint in oncology.
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Affiliation(s)
- Tianyu Zhan
- Data and Statistical Sciences, AbbVie Inc ., North Chicago, IL, USA
| | - Hongtao Zhang
- Global Biometric and Data Sciences, Bristol Myers Squibb, Berkeley Heights , NJ, USA
| | - Alan Hartford
- Statistical and Quantitative Sciences, Data Sciences Institute, Research and Development, Takeda Pharmaceuticals USA, Inc ., Cambridge, MA, USA
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31
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Nguyen Duc A, Heinzmann D, Berge C, Wolbers M. A pragmatic adaptive enrichment design for selecting the right target population for cancer immunotherapies. Pharm Stat 2020; 20:202-211. [PMID: 32869509 DOI: 10.1002/pst.2066] [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] [Received: 03/18/2020] [Revised: 06/09/2020] [Accepted: 08/11/2020] [Indexed: 02/02/2023]
Abstract
One of the challenges in the design of confirmatory trials is to deal with uncertainties regarding the optimal target population for a novel drug. Adaptive enrichment designs (AED) which allow for a data-driven selection of one or more prespecified biomarker subpopulations at an interim analysis have been proposed in this setting but practical case studies of AEDs are still relatively rare. We present the design of an AED with a binary endpoint in the highly dynamic setting of cancer immunotherapy. The trial was initiated as a conventional trial in early triple-negative breast cancer but amended to an AED based on emerging data external to the trial suggesting that PD-L1 status could be a predictive biomarker. Operating characteristics are discussed including the concept of a minimal detectable difference, that is, the smallest observed treatment effect that would lead to a statistically significant result in at least one of the target populations at the interim or the final analysis, respectively, in the setting of AED.
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Affiliation(s)
- Anh Nguyen Duc
- Biostatistics, Hoffmann-La Roche Pharma AG, Basel, Switzerland
| | | | - Claude Berge
- Biostatistics, Hoffmann-La Roche Pharma AG, Basel, Switzerland
| | - Marcel Wolbers
- Biostatistics, Hoffmann-La Roche Pharma AG, Basel, Switzerland
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32
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Kunz CU, Jörgens S, Bretz F, Stallard N, Van Lancker K, Xi D, Zohar S, Gerlinger C, Friede T. Clinical Trials Impacted by the COVID-19 Pandemic: Adaptive Designs to the Rescue? Stat Biopharm Res 2020; 12:461-477. [PMID: 34191979 PMCID: PMC8011492 DOI: 10.1080/19466315.2020.1799857] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Revised: 07/17/2020] [Accepted: 07/18/2020] [Indexed: 01/09/2023]
Abstract
Very recently the new pathogen severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was identified and the coronavirus disease 2019 (COVID-19) declared a pandemic by the World Health Organization. The pandemic has a number of consequences for ongoing clinical trials in non-COVID-19 conditions. Motivated by four current clinical trials in a variety of disease areas we illustrate the challenges faced by the pandemic and sketch out possible solutions including adaptive designs. Guidance is provided on (i) where blinded adaptations can help; (ii) how to achieve Type I error rate control, if required; (iii) how to deal with potential treatment effect heterogeneity; (iv) how to use early read-outs; and (v) how to use Bayesian techniques. In more detail approaches to resizing a trial affected by the pandemic are developed including considerations to stop a trial early, the use of group-sequential designs or sample size adjustment. All methods considered are implemented in a freely available R shiny app. Furthermore, regulatory and operational issues including the role of data monitoring committees are discussed.
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Affiliation(s)
| | | | - Frank Bretz
- Novartis Pharma AG, Basel, Switzerland
- Section for Medical Statistics, Medical University of Vienna, Vienna, Austria
| | - Nigel Stallard
- Division of Health Sciences, Warwick Medical School, The University of Warwick, Coventry, UK
| | - Kelly Van Lancker
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium
| | - Dong Xi
- Novartis Pharmaceuticals, East Hanover, NJ
| | - Sarah Zohar
- INSERM, Centre de Recherche des Cordeliers, Sorbonne Université, Université de Paris, Paris, France
| | - Christoph Gerlinger
- Statistics and Data Insights, Bayer AG, Berlin, Germany
- Department of Gynecology, Obstetrics and Reproductive Medicine, University Medical School of Saarland, Homburg/Saar, Germany
| | - Tim Friede
- Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany
- DZHK (German Center for Cardiovascular Research), Partner Site Göttingen, Göttingen, Germany
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33
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Li Q, Lin J, Lin Y. Adaptive design implementation in confirmatory trials: methods, practical considerations and case studies. Contemp Clin Trials 2020; 98:106096. [PMID: 32739496 DOI: 10.1016/j.cct.2020.106096] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2020] [Revised: 07/13/2020] [Accepted: 07/27/2020] [Indexed: 10/23/2022]
Abstract
The rapidly changing drug development landscapes have brought unique challenges to sponsors in designing clinical trials in a faster and more efficient way. With the ability to accelerate development timeline, reduce redundant sample size, and select the right dose and patient population during the clinical trial, adaptive designs help to increase the probability of success of clinical trials and eventually contribute to bringing the promising drugs to patients earlier and fulfilling their unmet medical needs. Although extensive adaptive design methods have been proposed in recent years, a comprehensive review of how to implement adaptive design in the practical confirmatory trials is still lacking. In this paper, we will review the evolving history of adaptive designs, updates of newly released regulatory guidance and emerging practical adaptive designs, including but not limited to sample size re-estimation, seamless design and surrogate endpoint used in the interim analysis. Furthermore, we will discuss the current practice of adaptive design implementation by demonstrating a complex oncology seamless phase 2/3 adaptive design case study. Through this example, we will introduce the critical roles of each cross disciplinary function, communication process and important documents when adaptive designs are implemented in real-world setting.
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Affiliation(s)
- Qing Li
- Takeda Pharmaceuticals, 300 Massachusetts Ave, Cambridge, MA 02139, United States of America.
| | - Jianchang Lin
- Takeda Pharmaceuticals, 300 Massachusetts Ave, Cambridge, MA 02139, United States of America
| | - Yunzhi Lin
- Sanofi, 50 Binney Street, Cambridge, MA 02142, United States of America
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34
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Dimairo M, Pallmann P, Wason J, Todd S, Jaki T, Julious SA, Mander AP, Weir CJ, Koenig F, Walton MK, Nicholl JP, Coates E, Biggs K, Hamasaki T, Proschan MA, Scott JA, Ando Y, Hind D, Altman DG. The adaptive designs CONSORT extension (ACE) statement: a checklist with explanation and elaboration guideline for reporting randomised trials that use an adaptive design. Trials 2020; 21:528. [PMID: 32546273 PMCID: PMC7298968 DOI: 10.1186/s13063-020-04334-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Adaptive designs (ADs) allow pre-planned changes to an ongoing trial without compromising the validity of conclusions and it is essential to distinguish pre-planned from unplanned changes that may also occur. The reporting of ADs in randomised trials is inconsistent and needs improving. Incompletely reported AD randomised trials are difficult to reproduce and are hard to interpret and synthesise. This consequently hampers their ability to inform practice as well as future research and contributes to research waste. Better transparency and adequate reporting will enable the potential benefits of ADs to be realised.This extension to the Consolidated Standards Of Reporting Trials (CONSORT) 2010 statement was developed to enhance the reporting of randomised AD clinical trials. We developed an Adaptive designs CONSORT Extension (ACE) guideline through a two-stage Delphi process with input from multidisciplinary key stakeholders in clinical trials research in the public and private sectors from 21 countries, followed by a consensus meeting. Members of the CONSORT Group were involved during the development process.The paper presents the ACE checklists for AD randomised trial reports and abstracts, as well as an explanation with examples to aid the application of the guideline. The ACE checklist comprises seven new items, nine modified items, six unchanged items for which additional explanatory text clarifies further considerations for ADs, and 20 unchanged items not requiring further explanatory text. The ACE abstract checklist has one new item, one modified item, one unchanged item with additional explanatory text for ADs, and 15 unchanged items not requiring further explanatory text.The intention is to enhance transparency and improve reporting of AD randomised trials to improve the interpretability of their results and reproducibility of their methods, results and inference. We also hope indirectly to facilitate the much-needed knowledge transfer of innovative trial designs to maximise their potential benefits. In order to encourage its wide dissemination this article is freely accessible on the BMJ and Trials journal websites."To maximise the benefit to society, you need to not just do research but do it well" Douglas G Altman.
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Affiliation(s)
- Munyaradzi Dimairo
- School of Health and Related Research, University of Sheffield, Sheffield, S1 4DA, UK.
| | | | - James Wason
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
- Institute of Health and Society, Newcastle University, Newcastle, UK
| | - Susan Todd
- Department of Mathematics and Statistics, University of Reading, Reading, UK
| | - Thomas Jaki
- Department of Mathematics and Statistics, Lancaster University, Lancaster, UK
| | - Steven A Julious
- School of Health and Related Research, University of Sheffield, Sheffield, S1 4DA, UK
| | - Adrian P Mander
- Centre for Trials Research, Cardiff University, Cardiff, UK
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - Christopher J Weir
- Edinburgh Clinical Trials Unit, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Franz Koenig
- Centre for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria
| | - Marc K Walton
- Janssen Pharmaceuticals, Titusville, New Jersey, USA
| | - Jon P Nicholl
- School of Health and Related Research, University of Sheffield, Sheffield, S1 4DA, UK
| | - Elizabeth Coates
- School of Health and Related Research, University of Sheffield, Sheffield, S1 4DA, UK
| | - Katie Biggs
- School of Health and Related Research, University of Sheffield, Sheffield, S1 4DA, UK
| | | | - Michael A Proschan
- National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, USA
| | - John A Scott
- Division of Biostatistics in the Center for Biologics Evaluation and Research, Food and Drug Administration, Rockville, USA
| | - Yuki Ando
- Pharmaceuticals and Medical Devices Agency, Tokyo, Japan
| | - Daniel Hind
- School of Health and Related Research, University of Sheffield, Sheffield, S1 4DA, UK
| | - Douglas G Altman
- Centre for Statistics in Medicine, University of Oxford, Oxford, UK
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35
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Dimairo M, Pallmann P, Wason J, Todd S, Jaki T, Julious SA, Mander AP, Weir CJ, Koenig F, Walton MK, Nicholl JP, Coates E, Biggs K, Hamasaki T, Proschan MA, Scott JA, Ando Y, Hind D, Altman DG. The Adaptive designs CONSORT Extension (ACE) statement: a checklist with explanation and elaboration guideline for reporting randomised trials that use an adaptive design. BMJ 2020; 369:m115. [PMID: 32554564 PMCID: PMC7298567 DOI: 10.1136/bmj.m115] [Citation(s) in RCA: 54] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 11/19/2019] [Indexed: 12/11/2022]
Abstract
Adaptive designs (ADs) allow pre-planned changes to an ongoing trial without compromising the validity of conclusions and it is essential to distinguish pre-planned from unplanned changes that may also occur. The reporting of ADs in randomised trials is inconsistent and needs improving. Incompletely reported AD randomised trials are difficult to reproduce and are hard to interpret and synthesise. This consequently hampers their ability to inform practice as well as future research and contributes to research waste. Better transparency and adequate reporting will enable the potential benefits of ADs to be realised.This extension to the Consolidated Standards Of Reporting Trials (CONSORT) 2010 statement was developed to enhance the reporting of randomised AD clinical trials. We developed an Adaptive designs CONSORT Extension (ACE) guideline through a two-stage Delphi process with input from multidisciplinary key stakeholders in clinical trials research in the public and private sectors from 21 countries, followed by a consensus meeting. Members of the CONSORT Group were involved during the development process.The paper presents the ACE checklists for AD randomised trial reports and abstracts, as well as an explanation with examples to aid the application of the guideline. The ACE checklist comprises seven new items, nine modified items, six unchanged items for which additional explanatory text clarifies further considerations for ADs, and 20 unchanged items not requiring further explanatory text. The ACE abstract checklist has one new item, one modified item, one unchanged item with additional explanatory text for ADs, and 15 unchanged items not requiring further explanatory text.The intention is to enhance transparency and improve reporting of AD randomised trials to improve the interpretability of their results and reproducibility of their methods, results and inference. We also hope indirectly to facilitate the much-needed knowledge transfer of innovative trial designs to maximise their potential benefits.
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Affiliation(s)
- Munyaradzi Dimairo
- School of Health and Related Research, University of Sheffield, Sheffield S1 4DA, UK
| | | | - James Wason
- MRC Biostatistics Unit, University of Cambridge, UK
- Institute of Health and Society, Newcastle University, UK
| | - Susan Todd
- Department of Mathematics and Statistics, University of Reading, UK
| | - Thomas Jaki
- Department of Mathematics and Statistics, Lancaster University, UK
| | - Steven A Julious
- School of Health and Related Research, University of Sheffield, Sheffield S1 4DA, UK
| | - Adrian P Mander
- Centre for Trials Research, Cardiff University, UK
- MRC Biostatistics Unit, University of Cambridge, UK
| | - Christopher J Weir
- Edinburgh Clinical Trials Unit, Usher Institute, University of Edinburgh, UK
| | - Franz Koenig
- Centre for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Austria
| | | | - Jon P Nicholl
- School of Health and Related Research, University of Sheffield, Sheffield S1 4DA, UK
| | - Elizabeth Coates
- School of Health and Related Research, University of Sheffield, Sheffield S1 4DA, UK
| | - Katie Biggs
- School of Health and Related Research, University of Sheffield, Sheffield S1 4DA, UK
| | | | - Michael A Proschan
- National Institute of Allergy and Infectious Diseases, National Institutes of Health, USA
| | - John A Scott
- Division of Biostatistics in the Center for Biologics Evaluation and Research, Food and Drug Administration, USA
| | - Yuki Ando
- Pharmaceuticals and Medical Devices Agency, Japan
| | - Daniel Hind
- School of Health and Related Research, University of Sheffield, Sheffield S1 4DA, UK
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Chen X, Hartford A, Zhao J. A model-based approach for simulating adaptive clinical studies with surrogate endpoints used for interim decision-making. Contemp Clin Trials Commun 2020; 18:100562. [PMID: 32395663 PMCID: PMC7205753 DOI: 10.1016/j.conctc.2020.100562] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2020] [Revised: 03/15/2020] [Accepted: 03/28/2020] [Indexed: 11/28/2022] Open
Abstract
In clinical trials, when exploring multiple dose groups to establish efficacy and safety on one or more selected doses, adaptive designs with interim dose selection are often used for dropping less effective dose groups. When it takes a long time to observe primary outcomes, utilizing information on a surrogate endpoint available at an earlier interim may be preferred for selecting which dose to continue. We propose a Bayesian model-based approach where historical data can be leveraged to incorporate a correlation model for investigating the design's operating characteristics. Simulation studies were conducted and the method can be readily applied for power and sample size calculations.
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Affiliation(s)
- Xiaotian Chen
- Data and Statistical Sciences, AbbVie Inc, North Chicago, IL, United States
| | - Alan Hartford
- Statistical and Quantitative Sciences, Takeda Pharmaceuticals Inc, Cambridge, MA, United States
| | - Jun Zhao
- Data Science, Astellas Pharma Global Development, Northbrook, IL, United States
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37
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Kimani PK, Todd S, Renfro LA, Glimm E, Khan JN, Kairalla JA, Stallard N. Point and interval estimation in two-stage adaptive designs with time to event data and biomarker-driven subpopulation selection. Stat Med 2020; 39:2568-2586. [PMID: 32363603 PMCID: PMC7785132 DOI: 10.1002/sim.8557] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2019] [Revised: 03/31/2020] [Accepted: 04/06/2020] [Indexed: 02/02/2023]
Abstract
In personalized medicine, it is often desired to determine if all patients or only a subset of them benefit from a treatment. We consider estimation in two-stage adaptive designs that in stage 1 recruit patients from the full population. In stage 2, patient recruitment is restricted to the part of the population, which, based on stage 1 data, benefits from the experimental treatment. Existing estimators, which adjust for using stage 1 data for selecting the part of the population from which stage 2 patients are recruited, as well as for the confirmatory analysis after stage 2, do not consider time to event patient outcomes. In this work, for time to event data, we have derived a new asymptotically unbiased estimator for the log hazard ratio and a new interval estimator with good coverage probabilities and probabilities that the upper bounds are below the true values. The estimators are appropriate for several selection rules that are based on a single or multiple biomarkers, which can be categorical or continuous.
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Affiliation(s)
- Peter K Kimani
- Warwick Medical School, University of Warwick, Coventry, UK
| | - Susan Todd
- Department of Mathematics and Statistics, University of Reading, Reading, UK
| | - Lindsay A Renfro
- Division of Biostatistics, University of Southern California, Los Angeles, CA, USA
| | | | | | - John A Kairalla
- Department of Biostatistics, University of Florida, Gainesville, Florida, USA
| | - Nigel Stallard
- Warwick Medical School, University of Warwick, Coventry, UK
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38
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Teng Z, Tian Y, Liu Y, Liu G. Seamless phase 2/3 oncology trial design with flexible sample size determination. Stat Med 2020; 39:2373-2386. [PMID: 32338410 DOI: 10.1002/sim.8543] [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] [Received: 06/20/2019] [Revised: 02/19/2020] [Accepted: 03/13/2020] [Indexed: 11/06/2022]
Abstract
Conventional seamless phase 2/3 design with fixed sample size determination (SSD) has gained its popularity in oncology drug development due to attractive features such as significantly shortening the development timeline, minimizing sample size, as well as early decision making. However, this design is not immune to inaccurate treatment effect assumption when only limited efficacy data are available at study design stage. We propose an innovative seamless phase 2/3 study design with flexible SSD for oncology trials, in which the trial is designed under a distribution of treatment effect instead of one single assumption due to huge uncertainty of treatment effect at design stage and the sample size for end of phase 3 analysis is not predetermined at design stage, but rather dynamically determined based on observed treatment effect at phase 2 portion. Some practical sample size determination rules for end of phase 3 analysis will be discussed. The proposed design can lead to reduced sample size or/and improved power compared with conventional seamless phase 2/3 design with fixed SSD. This innovative study design can be especially useful for programs with aggressive development strategy to expedite the process in delivering efficacious treatment to patients.
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Affiliation(s)
| | - Yuan Tian
- Department of Statistics, North Carolina State University, Raleigh, North Carolina, USA
| | - Yi Liu
- Nektar Therapeutics, San Francisco, California, USA
| | - Guohui Liu
- Takeda Pharmaceuticals Inc., Cambridge, Massachusetts, USA
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39
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Rosenblum M, Fang EX, Liu H. Optimal, two-stage, adaptive enrichment designs for randomized trials, using sparse linear programming. J R Stat Soc Series B Stat Methodol 2020. [DOI: 10.1111/rssb.12366] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Affiliation(s)
| | | | - Han Liu
- Northwestern University; Evanston USA
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40
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Mehta CR, Liu L, Theuer C. An adaptive population enrichment phase III trial of TRC105 and pazopanib versus pazopanib alone in patients with advanced angiosarcoma (TAPPAS trial). Ann Oncol 2020; 30:103-108. [PMID: 30357394 PMCID: PMC6336002 DOI: 10.1093/annonc/mdy464] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Background Major challenges in clinical trials of ultra-orphan oncology diseases include limited patient availability and paucity of reliable prior data for estimating the treatment effect and, therefore, determining optimal sample size. Angiosarcoma (AS), a particularly aggressive form of soft tissue sarcoma with an incidence of about 2000 cases per year in the United States and Europe is poorly addressed by current systemic therapies. Pazopanib, an inhibitor of vascular endothelial growth factor receptor (VEGFR) is approved for the treatment of AS, with modest benefit. TRC105 (carotuximab) is a monoclonal antibody to endoglin, an essential angiogenic target highly expressed on proliferating endothelium and both tumor vessels and tumor cells in AS, that has the potential to complement VEGFR tyrosine kinase inhibitors. In a phase I/II study of soft tissue sarcoma, TRC105 combined safely with pazopanib and the combination demonstrated durable complete responses and encouraging progression-free survival (PFS). In addition, there was a suggestion of superior benefit in patients with cutaneous lesions versus those with the non-cutaneous lesions. Patients and methods This article describes the design of a recently initiated phase III trial of TRC105 And Pazopanib versus Pazopanib alone in patients with advanced AngioSarcoma (TAPPAS trial). Given the ultra-orphan status of the disease and the paucity of reliable prior data on PFS or overall survival (end points required for regulatory approval as a pivotal trial), an adaptive design incorporating population enrichment and sample size re-estimation was implemented. The design incorporated regulatory input from the Food and Drug Administration (FDA) and European Medicines Agency and proceeded following special protocol assessment designation by the FDA. Conclusions It is shown that the benefit of the adaptive design as compared with a conventional single-look design arises from the learning and subsequent improvements in power that occur after an unblinded analysis of interim data. Registered on Clinicaltrials.gov NCT02979899.
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Affiliation(s)
- C R Mehta
- Department of Biostatistics, Cytel Inc, Cambridge; Department of Biostatistics, Harvard TH Chan School of Public Health, Boston.
| | - L Liu
- Department of Biostatistics, Cytel Inc, Cambridge
| | - C Theuer
- Clinical Department, TRACON Pharmaceuticals, San Diego, USA
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41
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Friede T, Stallard N, Parsons N. Adaptive seamless clinical trials using early outcomes for treatment or subgroup selection: Methods, simulation model and their implementation in R. Biom J 2020; 62:1264-1283. [PMID: 32118317 PMCID: PMC8614126 DOI: 10.1002/bimj.201900020] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2019] [Revised: 01/10/2020] [Accepted: 01/12/2020] [Indexed: 11/12/2022]
Abstract
Adaptive seamless designs combine confirmatory testing, a domain of phase III trials, with features such as treatment or subgroup selection, typically associated with phase II trials. They promise to increase the efficiency of development programmes of new drugs, for example, in terms of sample size and/or development time. It is well acknowledged that adaptive designs are more involved from a logistical perspective and require more upfront planning, often in the form of extensive simulation studies, than conventional approaches. Here, we present a framework for adaptive treatment and subgroup selection using the same notation, which links the somewhat disparate literature on treatment selection on one side and on subgroup selection on the other. Furthermore, we introduce a flexible and efficient simulation model that serves both designs. As primary endpoints often take a long time to observe, interim analyses are frequently informed by early outcomes. Therefore, all methods presented accommodate interim analyses informed by either the primary outcome or an early outcome. The R package asd, previously developed to simulate designs with treatment selection, was extended to include subgroup selection (so‐called adaptive enrichment designs). Here, we describe the functionality of the R package asd and use it to present some worked‐up examples motivated by clinical trials in chronic obstructive pulmonary disease and oncology. The examples both illustrate various features of the R package and provide insights into the operating characteristics of adaptive seamless studies.
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Affiliation(s)
- Tim Friede
- Department of Medical StatisticsUniversity Medical Center GöttingenGöttingen Germany
| | - Nigel Stallard
- Division of Health SciencesWarwick Medical SchoolUniversity of WarwickCoventry UK
| | - Nicholas Parsons
- Division of Health SciencesWarwick Medical SchoolUniversity of WarwickCoventry UK
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42
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Takazawa A, Morita S. Optimal Decision Criteria for the Study Design and Sample Size of a Biomarker-Driven Phase III Trial. Ther Innov Regul Sci 2020; 54:1018-1034. [PMID: 31989540 DOI: 10.1007/s43441-020-00119-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2019] [Accepted: 11/26/2019] [Indexed: 10/25/2022]
Abstract
BACKGROUND The design and sample size of a phase III study for new medical technologies were historically determined within the framework of frequentist hypothesis testing. Recently, drug development using predictive biomarkers, which can predict efficacy based on the status of biomarkers, has attracted attention, and various study designs using predictive biomarkers have been suggested. Additionally, when choosing a study design, considering economic factors, such as the risk of development, expected revenue, and cost, is important. METHODS Here, we propose a method to determine the optimal phase III design and sample size and judge whether the phase III study will be conducted using the expected net present value (eNPV). The eNPV is defined using the probability of success of the study calculated based on historical data, the revenue that will be obtained after the success of the phase III study, and the cost of the study. Decision procedures of the optimal phase III design and sample size considering historical data obtained up to the start of the phase III study were considered using numerical examples. RESULTS Based on the numerical examples, the optimal study design and sample size depend on the mean treatment effect in the biomarker-positive and biomarker-negative populations obtained from historical data, the between-trial variance of response, the prevalence of the biomarker-positive population, and the threshold value of probability of success required to go to phase III study. CONCLUSIONS Thus, the design and sample size of a biomarker-driven phase III study can be appropriately determined based on the eNPV.
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Affiliation(s)
- Akira Takazawa
- Data Science Department, ONO Pharmaceutical Co., Ltd., 8-2, Kyutaromachi 1-Chome, Chuo-ku, Osaka, 541-8564, Japan. .,Department of Biomedical Statistics and Bioinformatics, Kyoto University Graduate School of Medicine, Kyoto, Japan.
| | - Satoshi Morita
- Department of Biomedical Statistics and Bioinformatics, Kyoto University Graduate School of Medicine, Kyoto, Japan
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43
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Uozumi R, Hamada C. Utility-Based Interim Decision Rule Planning in Adaptive Population Selection Designs With Survival Endpoints. Stat Biopharm Res 2019. [DOI: 10.1080/19466315.2019.1689844] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Affiliation(s)
- Ryuji Uozumi
- Department of Biomedical Statistics and Bioinformatics, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Chikuma Hamada
- Department of Information and Computer Technology, Tokyo University of Science, Tokyo, Japan
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44
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Simon R. Review of Statistical Methods for Biomarker-Driven Clinical Trials. JCO Precis Oncol 2019; 3:1-9. [PMID: 35100721 DOI: 10.1200/po.18.00407] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
The discovery of somatic driver mutations in kinases and receptors has stimulated the development of molecularly targeted treatments that require companion diagnostics and new approaches to clinical development. This article reviews some of the clinical trial designs that have been developed to address these opportunities, including phase II basket and platform trials as well as phase III enrichment and biomarker adaptive designs. It also re-examines some of the conventional wisdom that previously dominated clinical trial design and discusses development and internal validation of a predictive biomarker as a new paradigm for optimizing the intended-use subset for a treatment. Statistical methods now being used in adaptive biomarker-driven clinical trials are reviewed. Some previous paradigms for clinical trial design can limit the development of more effective methods on the basis of prospectively planned adaptive methods, but useful new methods have been developed for analysis of genome-wide data and for the design of adaptively enriched studies. In many cases, the heterogeneity of populations eligible for clinical trials as traditionally defined makes it unlikely that molecularly targeted treatments will be effective for a majority of the eligible patients. New methods for dealing with patient heterogeneity in therapeutic response should be used in the design of phase III clinical trials.
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45
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Nugent C, Guo W, Müller P, Ji Y. Bayesian Approaches to Subgroup Analysis and Related Adaptive Clinical Trial Designs. JCO Precis Oncol 2019; 3:PO.19.00003. [PMID: 32923858 PMCID: PMC7446414 DOI: 10.1200/po.19.00003] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/29/2019] [Indexed: 11/20/2022] Open
Abstract
We review Bayesian and Bayesian decision theoretic approaches to subgroup analysis and applications to subgroup-based adaptive clinical trial designs. Subgroup analysis refers to inference about subpopulations with significantly distinct treatment effects. The discussion mainly focuses on inference for a benefiting subpopulation, that is, a characterization of a group of patients who benefit from the treatment under consideration more than the overall population. We introduce alternative approaches and demonstrate them with a small simulation study. Then, we turn to clinical trial designs. When the selection of the interesting subpopulation is carried out as the trial proceeds, the design becomes an adaptive clinical trial design, using subgroup analysis to inform the randomization and assignment of treatments to patients. We briefly review some related designs. There are a variety of approaches to Bayesian subgroup analysis. Practitioners should consider the type of subpopulations in which they are interested and choose their methods accordingly. We demonstrate how subgroup analysis can be carried out by different Bayesian methods and discuss how they identify slightly different subpopulations.
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Affiliation(s)
| | | | | | - Yuan Ji
- University of Chicago, Chicago, IL
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46
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Li W, Zhao J, Li X, Chen C, Beckman RA. Multi‐stage enrichment and basket trial designs with population selection. Stat Med 2019; 38:5470-5485. [DOI: 10.1002/sim.8371] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2018] [Revised: 06/03/2019] [Accepted: 08/16/2019] [Indexed: 11/06/2022]
Affiliation(s)
- Wen Li
- Biostatistics and Research Decision Sciences, Merck Research LaboratoriesMerck & Co, Inc Kenilworth New Jersey
| | - Jing Zhao
- Biostatistics and Research Decision Sciences, Merck Research LaboratoriesMerck & Co, Inc Kenilworth New Jersey
| | - Xiaoyun Li
- Biostatistics and Research Decision Sciences, Merck Research LaboratoriesMerck & Co, Inc Kenilworth New Jersey
| | - Cong Chen
- Biostatistics and Research Decision Sciences, Merck Research LaboratoriesMerck & Co, Inc Kenilworth New Jersey
| | - Robert A. Beckman
- Departments of Oncology and of Biostatistics, Bioinformatics, and Biomathematics, Lombardi Comprehensive Cancer Center and Innovation Center for Biomedical InformaticsGeorgetown University Medical Center Washington District of Columbia
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47
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Huber C, Benda N, Friede T. A comparison of subgroup identification methods in clinical drug development: Simulation study and regulatory considerations. Pharm Stat 2019; 18:600-626. [PMID: 31270933 PMCID: PMC6772173 DOI: 10.1002/pst.1951] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2018] [Revised: 02/15/2019] [Accepted: 04/22/2019] [Indexed: 12/13/2022]
Abstract
With advancement of technologies such as genomic sequencing, predictive biomarkers have become a useful tool for the development of personalized medicine. Predictive biomarkers can be used to select subsets of patients, which are most likely to benefit from a treatment. A number of approaches for subgroup identification were proposed over the last years. Although overviews of subgroup identification methods are available, systematic comparisons of their performance in simulation studies are rare. Interaction trees (IT), model-based recursive partitioning, subgroup identification based on differential effect, simultaneous threshold interaction modeling algorithm (STIMA), and adaptive refinement by directed peeling were proposed for subgroup identification. We compared these methods in a simulation study using a structured approach. In order to identify a target population for subsequent trials, a selection of the identified subgroups is needed. Therefore, we propose a subgroup criterion leading to a target subgroup consisting of the identified subgroups with an estimated treatment difference no less than a pre-specified threshold. In our simulation study, we evaluated these methods by considering measures for binary classification, like sensitivity and specificity. In settings with large effects or huge sample sizes, most methods perform well. For more realistic settings in drug development involving data from a single trial only, however, none of the methods seems suitable for selecting a target population. Using the subgroup criterion as alternative to the proposed pruning procedures, STIMA and IT can improve their performance in some settings. The methods and the subgroup criterion are illustrated by an application in amyotrophic lateral sclerosis.
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Affiliation(s)
- Cynthia Huber
- Department of Medical StatisticsUniversity Medical Center GöttingenGöttingenGermany
| | - Norbert Benda
- Department of Medical StatisticsUniversity Medical Center GöttingenGöttingenGermany
- Federal Institute for Drugs and Medical Devices (BfArM) Research DepartmentBonnGermany
| | - Tim Friede
- Department of Medical StatisticsUniversity Medical Center GöttingenGöttingenGermany
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48
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Placzek M, Friede T. A conditional error function approach for adaptive enrichment designs with continuous endpoints. Stat Med 2019; 38:3105-3122. [PMID: 31066093 DOI: 10.1002/sim.8154] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2018] [Revised: 02/22/2019] [Accepted: 03/09/2019] [Indexed: 12/15/2022]
Abstract
Adaptive enrichment designs offer an efficient and flexible way to demonstrate the efficacy of a treatment in a clinically defined full population or in, eg, biomarker-defined subpopulations while controlling the family-wise Type I error rate in the strong sense. Frequently used testing strategies in designs with two or more stages include the combination test and the conditional error function approach. Here, we focus on the latter and present some extensions. In contrast to previous work, we allow for multiple subgroups rather than one subgroup only. For nested as well as nonoverlapping subgroups with normally distributed endpoints, we explore the effect of estimating the variances in the subpopulations. Instead of using a normal approximation, we derive new t-distribution-based methods for two different scenarios. First, in the case of equal variances across the subpopulations, we present exact results using a multivariate t-distribution. Second, in the case of potentially varying variances across subgroups, we provide some improved approximations compared to the normal approximation. The performance of the proposed conditional error function approaches is assessed and compared to the combination test in a simulation study. The proposed methods are motivated by an example in pulmonary arterial hypertension.
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Affiliation(s)
- Marius Placzek
- Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany
| | - Tim Friede
- Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany.,DZHK (German Center for Cardiovascular Research), Partner Site Göttingen, Göttingen, Germany
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Uozumi R, Yada S, Kawaguchi A. Patient recruitment strategies for adaptive enrichment designs with time-to-event endpoints. BMC Med Res Methodol 2019; 19:159. [PMID: 31331277 PMCID: PMC6647323 DOI: 10.1186/s12874-019-0800-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2018] [Accepted: 07/07/2019] [Indexed: 12/27/2022] Open
Abstract
Background Adaptive enrichment designs for clinical trials have great potential for the development of targeted therapies. They enable researchers to stop the recruitment process for a certain population in mid-course based on an interim analysis. However, adaptive enrichment designs increase the total trial period owing to the stoppage in patient recruitment to make interim decisions. This is a major drawback; it results in delays in the submission of clinical trial reports and the appearance of drugs on the market. Here, we explore three types of patient recruitment strategy for the development of targeted therapies based on the adaptive enrichment design. Methods We consider recruitment methods which provide an option to continue recruiting patients from the overall population or only from the biomarker-positive population even during the interim decision period. A simulation study was performed to investigate the operating characteristics by comparing an adaptive enrichment design using the recruitment methods with a non-enriched design. Results The number of patients was similar for both recruitment methods. Nevertheless, the adaptive enrichment design was beneficial in settings in which the recruitment period is expected to be longer than the follow-up period. In these cases, the adaptive enrichment design with continued recruitment from the overall population or only from the biomarker-positive population even during the interim decision period conferred a major advantage, since the total trial period did not differ substantially from that of trials employing the non-enriched design. By contrast, the non-enriched design should be used in settings in which the follow-up period is expected to be longer than the recruitment period, since the total trial period was notably shorter than that of the adaptive enrichment design. Furthermore, the utmost care is needed when the distribution of patient recruitment is concave, i.e., when patient recruitment is slow during the early period, since the total trial period is extended. Conclusions Adaptive enrichment designs that entail continued recruitment methods are beneficial owing to the shorter total trial period than expected in settings in which the recruitment period is expected to be longer than the follow-up period and the biomarker-positive population is promising.
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Affiliation(s)
- Ryuji Uozumi
- Department of Biomedical Statistics and Bioinformatics, Kyoto University Graduate School of Medicine, 54 Kawahara-cho, Shogoin, Sakyo-ku, Kyoto, 606-8507, Japan.
| | - Shinjo Yada
- Biostatistics Department I, Data Science Division, A2 Healthcare Corporation, 1-4-12, Utsubohommachi, Nishi-ku, Osaka, 550-0004, Japan
| | - Atsushi Kawaguchi
- Faculty of Medicine, Saga University, 5-1-1 Nabeshima, Saga, 849-8501, Japan
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Joshi N, Fine J, Chu R, Ivanova A. Estimating the subgroup and testing for treatment effect in a post-hoc analysis of a clinical trial with a biomarker. J Biopharm Stat 2019; 29:685-695. [PMID: 31269870 DOI: 10.1080/10543406.2019.1633655] [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: 12/26/2022]
Abstract
We consider the problem of estimating a biomarker-based subgroup and testing for treatment effect in the overall population and in the subgroup after the trial. We define the best subgroup as the subgroup that maximizes the power for comparing the experimental treatment with the control. In the case of continuous outcome and a single biomarker, both a non-parametric method of estimating the subgroup and a method based on fitting a linear model with treatment by biomarker interaction to the data perform well. Several procedures for testing for treatment effect in all and in the subgroup are discussed. Cross-validation with two cohorts is used to estimate the biomarker cut-off to determine the best subgroup and to test for treatment effect. An approach that combines the tests in all patients and in the subgroup using Hochberg's method is recommended. This test performs well in the case when there is a subgroup with sizable treatment effect and in the case when the treatment is beneficial to everyone.
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Affiliation(s)
- Neha Joshi
- a Department of Biostatistics, The University of North Carolina at Chapel Hill , Chapel Hill , NC , USA
| | - Jason Fine
- a Department of Biostatistics, The University of North Carolina at Chapel Hill , Chapel Hill , NC , USA
| | - Rong Chu
- b Biostatistics, Agensys, Inc , Santa Monica , CA , USA
| | - Anastasia Ivanova
- a Department of Biostatistics, The University of North Carolina at Chapel Hill , Chapel Hill , NC , USA
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