1
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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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2
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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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3
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Xu H, Liu Y, Beckman RA. Adaptive Endpoints Selection with Application in Rare Disease. Stat Biopharm Res 2023. [DOI: 10.1080/19466315.2023.2183252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/25/2023]
Affiliation(s)
- Heng Xu
- Nektar Therapeutics, San Francisco, USA
| | - Yi Liu
- Nektar Therapeutics, San Francisco, USA
| | - Robert A. Beckman
- Departments of Oncology and of Biostatistics, Bioinformatics, and Biomathematics, Lombardi Comprehensive Cancer Center and Innovation Center for Biomedical Informatics, Georgetown University Medical Center
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4
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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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5
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Sinha AK, Moye L, Piller LB, Yamal JM, Barcenas CH, Song J, Davis BR. Simultaneous population enrichment and endpoint selection in phase 3 randomized controlled trials: An adaptive group sequential design with two binary alternative primary endpoints. COMMUN STAT-THEOR M 2023. [DOI: 10.1080/03610926.2022.2163180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Affiliation(s)
- Arup K. Sinha
- Department of Biostatistics, The University of Texas School of Public Health, Houston, Texas, USA
| | - Lemuel Moye
- Department of Biostatistics, The University of Texas School of Public Health, Houston, Texas, USA
| | - Linda B. Piller
- Division of Epidemiology, Human Genetics and Environmental Sciences, The University of Texas School of Public Health, Houston, Texas, USA
| | - Jose-Miguel Yamal
- Department of Biostatistics, The University of Texas School of Public Health, Houston, Texas, USA
| | - Carlos H. Barcenas
- Department of Breast Medical Oncology, Division of Cancer Medicine, MD Anderson Cancer Center, Houston, Texas, USA
| | - Jaejoon Song
- Department of Biostatistics, MD Anderson Cancer Center, Houston, Texas, USA
| | - Barry R. Davis
- Department of Biostatistics, The University of Texas School of Public Health, Houston, Texas, USA
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6
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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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7
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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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8
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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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9
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An Overview of Phase 2 Clinical Trial Designs. Int J Radiat Oncol Biol Phys 2022; 112:22-29. [PMID: 34363901 PMCID: PMC8688307 DOI: 10.1016/j.ijrobp.2021.07.1700] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Accepted: 07/22/2021] [Indexed: 01/03/2023]
Abstract
Clinical trials are studies to test new treatments in humans. Typically, these treatments are evaluated over several phases to assess their safety and efficacy. Phase 1 trials are designed to evaluate the safety and tolerability of a new treatment, typically with a small number of patients (eg, 20-80), generally spread across several dose levels. Phase 2 trials are designed to determine whether the new treatment has sufficiently promising efficacy to warrant further investigation in a large-scale randomized phase 3 trial, as well as to further assess safety. These studies usually involve a few hundred patients. This article provides an overview of some of the most commonly used phase 2 designs for clinical trials and emphasizes their critical elements and considerations. Key references to some of the most commonly used phase 2 designs are given to allow the reader to explore in more detail the critical aspects when planning a phase 2 trial. A comparison of 3 potential designs in the context of the NRG-HN002 trial is presented to complement the discussion about phase 2 trials.
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10
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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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11
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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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12
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Anderson BJ, Calfee CS, Liu KD, Reilly JP, Kangelaris KN, Shashaty MGS, Lazaar AL, Bayliffe AI, Gallop RJ, Miano TA, Dunn TG, Johansson E, Abbott J, Jauregui A, Deiss T, Vessel K, Belzer A, Zhuo H, Matthay MA, Meyer NJ, Christie JD. Plasma sTNFR1 and IL8 for prognostic enrichment in sepsis trials: a prospective cohort study. CRITICAL CARE : THE OFFICIAL JOURNAL OF THE CRITICAL CARE FORUM 2019; 23:400. [PMID: 31818332 PMCID: PMC6902425 DOI: 10.1186/s13054-019-2684-2] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/30/2019] [Accepted: 11/22/2019] [Indexed: 01/07/2023]
Abstract
Background Enrichment strategies improve therapeutic targeting and trial efficiency, but enrichment factors for sepsis trials are lacking. We determined whether concentrations of soluble tumor necrosis factor receptor-1 (sTNFR1), interleukin-8 (IL8), and angiopoietin-2 (Ang2) could identify sepsis patients at higher mortality risk and serve as prognostic enrichment factors. Methods In a multicenter prospective cohort study of 400 critically ill septic patients, we derived and validated thresholds for each marker and expressed prognostic enrichment using risk differences (RD) of 30-day mortality as predictive values. We then used decision curve analysis to simulate the prognostic enrichment of each marker and compare different prognostic enrichment strategies. Measurements and main results An admission sTNFR1 concentration > 8861 pg/ml identified patients with increased mortality in both the derivation (RD 21.6%) and validation (RD 17.8%) populations. Among immunocompetent patients, an IL8 concentration > 94 pg/ml identified patients with increased mortality in both the derivation (RD 17.7%) and validation (RD 27.0%) populations. An Ang2 level > 9761 pg/ml identified patients at 21.3% and 12.3% increased risk of mortality in the derivation and validation populations, respectively. Using sTNFR1 or IL8 to select high-risk patients improved clinical trial power and efficiency compared to selecting patients with septic shock. Ang2 did not outperform septic shock as an enrichment factor. Conclusions Thresholds for sTNFR1 and IL8 consistently identified sepsis patients with higher mortality risk and may have utility for prognostic enrichment in sepsis trials.
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Affiliation(s)
- Brian J Anderson
- Division of Pulmonary, Allergy and Critical Care Medicine, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, 5036 Gates Building, Philadelphia, PA, 19104, USA.
| | - Carolyn S Calfee
- Division of Pulmonary and Critical Care Medicine, University of California San Francisco, San Francisco, USA
| | - Kathleen D Liu
- Division of Pulmonary and Critical Care Medicine, University of California San Francisco, San Francisco, USA
| | - John P Reilly
- Division of Pulmonary, Allergy and Critical Care Medicine, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, 5036 Gates Building, Philadelphia, PA, 19104, USA
| | - Kirsten N Kangelaris
- Division of Hospital Medicine, Department of Medicine, University of California San Francisco, San Francisco, USA
| | - Michael G S Shashaty
- Division of Pulmonary, Allergy and Critical Care Medicine, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, 5036 Gates Building, Philadelphia, PA, 19104, USA.,Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Aili L Lazaar
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA.,GlaxoSmithKline R&D, Brentford, UK
| | | | - Robert J Gallop
- Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA.,Department of Mathematics, West Chester University, West Chester, USA
| | - Todd A Miano
- Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Thomas G Dunn
- Division of Pulmonary, Allergy and Critical Care Medicine, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, 5036 Gates Building, Philadelphia, PA, 19104, USA
| | - Erik Johansson
- Division of Pulmonary, Allergy and Critical Care Medicine, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, 5036 Gates Building, Philadelphia, PA, 19104, USA
| | - Jason Abbott
- Division of Pulmonary and Critical Care Medicine, University of California San Francisco, San Francisco, USA
| | - Alejandra Jauregui
- Division of Pulmonary and Critical Care Medicine, University of California San Francisco, San Francisco, USA
| | - Thomas Deiss
- Division of Pulmonary and Critical Care Medicine, University of California San Francisco, San Francisco, USA
| | - Kathryn Vessel
- Division of Pulmonary and Critical Care Medicine, University of California San Francisco, San Francisco, USA
| | - Annika Belzer
- Division of Pulmonary and Critical Care Medicine, University of California San Francisco, San Francisco, USA
| | - Hanjing Zhuo
- Division of Pulmonary and Critical Care Medicine, University of California San Francisco, San Francisco, USA
| | - Michael A Matthay
- Division of Pulmonary and Critical Care Medicine, University of California San Francisco, San Francisco, USA
| | - Nuala J Meyer
- Division of Pulmonary, Allergy and Critical Care Medicine, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, 5036 Gates Building, Philadelphia, PA, 19104, USA
| | - Jason D Christie
- Division of Pulmonary, Allergy and Critical Care Medicine, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, 5036 Gates Building, Philadelphia, PA, 19104, USA.,Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
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13
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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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14
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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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15
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Gal J, Milano G, Ferrero JM, Saâda-Bouzid E, Viotti J, Chabaud S, Gougis P, Le Tourneau C, Schiappa R, Paquet A, Chamorey E. Optimizing drug development in oncology by clinical trial simulation: Why and how? Brief Bioinform 2019; 19:1203-1217. [PMID: 28575140 DOI: 10.1093/bib/bbx055] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2017] [Indexed: 12/11/2022] Open
Abstract
In therapeutic research, the safety and efficacy of pharmaceutical products are necessarily tested on humans via clinical trials after an extensive and expensive preclinical development period. Methodologies such as computer modeling and clinical trial simulation (CTS) might represent a valuable option to reduce animal and human assays. The relevance of these methods is well recognized in pharmacokinetics and pharmacodynamics from the preclinical phase to postmarketing. However, they are barely used and are poorly regarded for drug approval, despite Food and Drug Administration and European Medicines Agency recommendations. The generalization of CTS could be greatly facilitated by the availability of software for modeling biological systems, by clinical trial studies and hospital databases. Data sharing and data merging raise legal, policy and technical issues that will need to be addressed. Development of future molecules will have to use CTS for faster development and thus enable better patient management. Drug activity modeling coupled with disease modeling, optimal use of medical data and increased computing speed should allow this leap forward. The realization of CTS requires not only bioinformatics tools to allow interconnection and global integration of all clinical data but also a universal legal framework to protect the privacy of every patient. While recognizing that CTS can never replace 'real-life' trials, they should be implemented in future drug development schemes to provide quantitative support for decision-making. This in silico medicine opens the way to the P4 medicine: predictive, preventive, personalized and participatory.
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Affiliation(s)
- Jocelyn Gal
- Epidemiology and Biostatistics Unit at the Antoine Lacassagne Center, Nice, France
| | | | | | | | | | | | - Paul Gougis
- Pitie´-Salp^etrie`re Hospital in Paris, France
| | | | | | - Agnes Paquet
- Molecular and Cellular Pharmacology Institute of Sophia Antipolis, Valbonne, France
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16
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Wan F, Titman AC, Jaki TF. Subgroup analysis of treatment effects for misclassified biomarkers with time‐to‐event data. J R Stat Soc Ser C Appl Stat 2019. [DOI: 10.1111/rssc.12364] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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17
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Sinha AK, Moye L, Piller LB, Yamal J, Barcenas CH, Lin J, Davis BR. Adaptive group‐sequential design with population enrichment in phase 3 randomized controlled trials with two binary co‐primary endpoints. Stat Med 2019; 38:3985-3996. [DOI: 10.1002/sim.8216] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2018] [Revised: 04/28/2019] [Accepted: 05/09/2019] [Indexed: 11/11/2022]
Affiliation(s)
- Arup K. Sinha
- Department of Biostatistics, School of Public HealthYale University New Haven Connecticut
| | - Lemuel Moye
- Department of Biostatistics, School of Public HealthThe University of Texas Health Science Center at Houston Houston Texas
| | - Linda B. Piller
- Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public HealthThe University of Texas Health Science Center at Houston Houston Texas
| | - Jose‐Miguel Yamal
- Department of Biostatistics, School of Public HealthThe University of Texas Health Science Center at Houston Houston Texas
| | - Carlos H. Barcenas
- Department of Breast Medical Oncology, Division of Cancer MedicineThe University of Texas MD Anderson Cancer Center Houston Texas
| | | | - Barry R. Davis
- Department of Biostatistics, School of Public HealthThe University of Texas Health Science Center at Houston Houston Texas
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18
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Jörgens S, Wassmer G, König F, Posch M. Nested combination tests with a time-to-event endpoint using a short-term endpoint for design adaptations. Pharm Stat 2019; 18:329-350. [PMID: 30652401 DOI: 10.1002/pst.1926] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2017] [Revised: 10/16/2018] [Accepted: 12/14/2018] [Indexed: 12/11/2022]
Abstract
Adaptive trial methodology for multiarmed trials and enrichment designs has been extensively discussed in the past. A general principle to construct test procedures that control the family-wise Type I error rate in the strong sense is based on combination tests within a closed test. Using survival data, a problem arises when using information of patients for adaptive decision making, which are under risk at interim. With the currently available testing procedures, either no testing of hypotheses in interim analyses is possible or there are restrictions on the interim data that can be used in the adaptation decisions as, essentially, only the interim test statistics of the primary endpoint may be used. We propose a general adaptive testing procedure, covering multiarmed and enrichment designs, which does not have these restrictions. An important application are clinical trials, where short-term surrogate endpoints are used as basis for trial adaptations, and we illustrate how such trials can be designed. We propose statistical models to assess the impact of effect sizes, the correlation structure between the short-term and the primary endpoint, the sample size, the timing of interim analyses, and the selection rule on the operating characteristics.
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Affiliation(s)
- Silke Jörgens
- Innovation Center, ICON Clinical Research Inc, Cologne, Germany
| | - Gernot Wassmer
- Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria
| | - Franz König
- Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria
| | - Martin Posch
- Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria
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19
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Lawrence NJ, Roncolato F, Martin A, Simes RJ, Stockler MR. Effect Sizes Hypothesized and Observed in Contemporary Phase III Trials of Targeted and Immunological Therapies for Advanced Cancer. JNCI Cancer Spectr 2018; 2:pky037. [PMID: 31360867 PMCID: PMC6649714 DOI: 10.1093/jncics/pky037] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2017] [Revised: 05/05/2018] [Accepted: 07/13/2018] [Indexed: 12/22/2022] Open
Abstract
Background We sought to compare the effect sizes hypothesized in the trial design, observed in the trial results, and considered clinically meaningful by the American Society of Clinical Oncology (ASCO) 2014 recommendations, in phase III trials of targeted and immunological therapies. Methods We studied phase III, superiority trials of targeted and immunological therapies in advanced cancers published from 2005 to 2015. We recorded the characteristics, design parameters, and observed results for the primary endpoint of each trial. The effect sizes hypothesized in the trial design were compared with the ASCO 2014 recommendation that phase III trials be designed to detect overall survival (OS) benefits that are clinically meaningful (hazard ratio ≤0.8). Results All critical elements of the trial design (effect sizes hypothesized, estimated survival in the control group, power, and significance level) were identified in 165 of 213 included trials (77%). Of trials with a statistically significant result for the primary endpoint, 16 of 30 (53%) with a primary endpoint of OS and 20 of 53 (38%) with a primary endpoint of progression free survival (PFS) had an observed effect size less extreme than hypothesized; and 7 of 30 trials (23%) reported an observed effect size for OS that was statistically significant but not clinically meaningful (HR > 0.80) according to the ASCO 2014 recommendations. Conclusion Many trials were designed such that an observed benefit in OS or PFS that was not clinically meaningful would be statistically significant. Phase III trials should be designed to provide results that are statistically significant for observed effects that are clinically meaningful but not for observed results that are of dubious clinical importance.
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Affiliation(s)
- Nicola Jane Lawrence
- NHMRC Clinical Trials Centre, University of Sydney, Camperdown, New South Wales, Australia
| | - Felicia Roncolato
- NHMRC Clinical Trials Centre, University of Sydney, Camperdown, New South Wales, Australia.,Macarthur Cancer Therapy Centre, Campbelltown, New South Wales, Australia
| | - Andrew Martin
- NHMRC Clinical Trials Centre, University of Sydney, Camperdown, New South Wales, Australia
| | - Robert John Simes
- NHMRC Clinical Trials Centre, University of Sydney, Camperdown, New South Wales, Australia
| | - Martin R Stockler
- NHMRC Clinical Trials Centre, University of Sydney, Camperdown, New South Wales, Australia.,Concord Cancer Centre, Concord Repatriation General Hospital, Concord, New South Wales, Australia.,Chris O'Brien Lifehouse, Camperdown, New South Wales, Australia
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20
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Lai TL, Lavori PW, Tsang KW. Adaptive enrichment designs for confirmatory trials. Stat Med 2018; 38:613-624. [DOI: 10.1002/sim.7946] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2017] [Revised: 04/06/2018] [Accepted: 07/26/2018] [Indexed: 11/08/2022]
Affiliation(s)
- Tze Leung Lai
- Department of Statistics Stanford University Stanford California
- Department of Biomedical Data Science Stanford University Stanford California
- School of Science and Engineering The Chinese University of Hong Kong Shenzhen Guangdong China
| | - Philip W. Lavori
- Department of Statistics Stanford University Stanford California
- Department of Biomedical Data Science Stanford University Stanford California
| | - Ka Wai Tsang
- School of Science and Engineering The Chinese University of Hong Kong Shenzhen Guangdong China
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21
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Auto-adaptive Alpha Allocation: A Strategy to Mitigate Risk on Study Assumptions. STATISTICS IN BIOSCIENCES 2018. [DOI: 10.1007/s12561-017-9192-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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22
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Sugitani T, Posch M, Bretz F, Koenig F. Flexible alpha allocation strategies for confirmatory adaptive enrichment clinical trials with a prespecified subgroup. Stat Med 2018; 37:3387-3402. [PMID: 29945304 DOI: 10.1002/sim.7851] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2017] [Revised: 03/08/2018] [Accepted: 05/25/2018] [Indexed: 02/05/2023]
Abstract
Adaptive enrichment designs have recently received considerable attention as they have the potential to make drug development process for personalized medicine more efficient. Several statistical approaches have been proposed so far in the literature and the operating characteristics of these approaches are extensively investigated using simulation studies. In this paper, we improve on existing adaptive enrichment designs by assigning unequal weights to the significance levels associated with the hypotheses of the overall population and a prespecified subgroup. More specifically, we focus on the standard combination test, a modified combination test, the marginal combination test, and the partial conditional error rate approach and explore the operating characteristics of these approaches by a simulation study. We show that these approaches can lead to power gains, compared to existing approaches, if the weights are chosen carefully.
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Affiliation(s)
- Toshifumi Sugitani
- Biostatistics Group, Astellas Pharma Inc, Tokyo, Japan.,Section for Medical Statistics, Medical University of Vienna, Vienna, Austria
| | - Martin Posch
- Section for Medical Statistics, Medical University of Vienna, Vienna, Austria
| | - Frank Bretz
- Section for Medical Statistics, Medical University of Vienna, Vienna, Austria.,Statistical Methodology, Novartis Pharma AG, Basel, Switzerland
| | - Franz Koenig
- Section for Medical Statistics, Medical University of Vienna, Vienna, Austria
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23
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Mortality Benefit of Recombinant Human Interleukin-1 Receptor Antagonist for Sepsis Varies by Initial Interleukin-1 Receptor Antagonist Plasma Concentration. Crit Care Med 2017; 46:21-28. [PMID: 28991823 DOI: 10.1097/ccm.0000000000002749] [Citation(s) in RCA: 67] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
OBJECTIVE Plasma interleukin-1 beta may influence sepsis mortality, yet recombinant human interleukin-1 receptor antagonist did not reduce mortality in randomized trials. We tested for heterogeneity in the treatment effect of recombinant human interleukin-1 receptor antagonist by baseline plasma interleukin-1 beta or interleukin-1 receptor antagonist concentration. DESIGN Retrospective subgroup analysis of randomized controlled trial. SETTING Multicenter North American and European clinical trial. PATIENTS Five hundred twenty-nine subjects with sepsis and hypotension or hypoperfusion, representing 59% of the original trial population. INTERVENTIONS Random assignment of placebo or recombinant human interleukin-1 receptor antagonist × 72 hours. MEASUREMENTS AND MAIN RESULTS We measured prerandomization plasma interleukin-1 beta and interleukin-1 receptor antagonist and tested for statistical interaction between recombinant human interleukin-1 receptor antagonist treatment and baseline plasma interleukin-1 receptor antagonist or interleukin-1 beta concentration on 28-day mortality. There was significant heterogeneity in the effect of recombinant human interleukin-1 receptor antagonist treatment by plasma interleukin-1 receptor antagonist concentration whether plasma interleukin-1 receptor antagonist was divided into deciles (interaction p = 0.046) or dichotomized (interaction p = 0.028). Interaction remained present across different predicted mortality levels. Among subjects with baseline plasma interleukin-1 receptor antagonist above 2,071 pg/mL (n = 283), recombinant human interleukin-1 receptor antagonist therapy reduced adjusted mortality from 45.4% to 34.3% (adjusted risk difference, -0.12; 95% CI, -0.23 to -0.01), p = 0.044. Mortality in subjects with plasma interleukin-1 receptor antagonist below 2,071 pg/mL was not reduced by recombinant human interleukin-1 receptor antagonist (adjusted risk difference, +0.07; 95% CI, -0.04 to +0.17), p = 0.230. Interaction between plasma interleukin-1 beta concentration and recombinant human interleukin-1 receptor antagonist treatment was not statistically significant. CONCLUSIONS We report a heterogeneous effect of recombinant human interleukin-1 receptor antagonist on 28-day sepsis mortality that is potentially predictable by plasma interleukin-1 receptor antagonist in one trial. A precision clinical trial of recombinant human interleukin-1 receptor antagonist targeted to septic patients with high plasma interleukin-1 receptor antagonist may be worthy of consideration.
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24
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Ondra T, Jobjörnsson S, Beckman RA, Burman CF, König F, Stallard N, Posch M. Optimized adaptive enrichment designs. Stat Methods Med Res 2017; 28:2096-2111. [PMID: 29254436 PMCID: PMC6613177 DOI: 10.1177/0962280217747312] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
Based on a Bayesian decision theoretic approach, we optimize frequentist single-
and adaptive two-stage trial designs for the development of targeted therapies,
where in addition to an overall population, a pre-defined subgroup is
investigated. In such settings, the losses and gains of decisions can be
quantified by utility functions that account for the preferences of different
stakeholders. In particular, we optimize expected utilities from the
perspectives both of a commercial sponsor, maximizing the net present value, and
also of the society, maximizing cost-adjusted expected health benefits of a new
treatment for a specific population. We consider single-stage and adaptive
two-stage designs with partial enrichment, where the proportion of patients
recruited from the subgroup is a design parameter. For the adaptive designs, we
use a dynamic programming approach to derive optimal adaptation rules. The
proposed designs are compared to trials which are non-enriched (i.e. the
proportion of patients in the subgroup corresponds to the prevalence in the
underlying population). We show that partial enrichment designs can
substantially improve the expected utilities. Furthermore, adaptive partial
enrichment designs are more robust than single-stage designs and retain high
expected utilities even if the expected utilities are evaluated under a
different prior than the one used in the optimization. In addition, we find that
trials optimized for the sponsor utility function have smaller sample sizes
compared to trials optimized under the societal view and may include the overall
population (with patients from the complement of the subgroup) even if there is
substantial evidence that the therapy is only effective in the subgroup.
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Affiliation(s)
- Thomas Ondra
- 1 Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria
| | | | - Robert A Beckman
- 3 Departments of Oncology and of Biostatistics, Bioinformatics, and Biomathematics, Georgetown University Medical Center, Washington, DC, USA
| | - Carl-Fredrik Burman
- 2 Department of Mathematics, Chalmers University, Gothenburg, Sweden.,4 Statistical Innovation, AstraZeneca R&D, Molndal, Sweden
| | - Franz König
- 1 Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria
| | - Nigel Stallard
- 5 Warwick Medical School, The University of Warwick, Coventry, UK
| | - Martin Posch
- 1 Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria
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25
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Su SC, Li X, Zhao Y, Chan ISF. Population-Enrichment Adaptive Design Strategy for an Event-Driven Vaccine Efficacy Trial. STATISTICS IN BIOSCIENCES 2017. [DOI: 10.1007/s12561-017-9202-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
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26
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Meyer NJ, Calfee CS. Novel translational approaches to the search for precision therapies for acute respiratory distress syndrome. THE LANCET RESPIRATORY MEDICINE 2017; 5:512-523. [PMID: 28664850 PMCID: PMC7103930 DOI: 10.1016/s2213-2600(17)30187-x] [Citation(s) in RCA: 53] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/13/2017] [Revised: 03/30/2017] [Accepted: 04/06/2017] [Indexed: 02/07/2023]
Abstract
In the 50 years since acute respiratory distress syndrome (ARDS) was first described, substantial progress has been made in identifying the risk factors for and the pathogenic contributors to the syndrome and in characterising the protein expression patterns in plasma and bronchoalveolar lavage fluid from patients with ARDS. Despite this effort, however, pharmacological options for ARDS remain scarce. Frequently cited reasons for this absence of specific drug therapies include the heterogeneity of patients with ARDS, the potential for a differential response to drugs, and the possibility that the wrong targets have been studied. Advances in applied biomolecular technology and bioinformatics have enabled breakthroughs for other complex traits, such as cardiovascular disease or asthma, particularly when a precision medicine paradigm, wherein a biomarker or gene expression pattern indicates a patient's likelihood of responding to a treatment, has been pursued. In this Review, we consider the biological and analytical techniques that could facilitate a precision medicine approach for ARDS.
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Affiliation(s)
- Nuala J Meyer
- Division of Pulmonary, Allergy, and Critical Care Medicine and Center for Translational Lung Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Carolyn S Calfee
- Department of Medicine and Department of Anesthesia, University of California, San Francisco, CA, USA.
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27
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Curtin F, Heritier S. The role of adaptive trial designs in drug development. Expert Rev Clin Pharmacol 2017; 10:727-736. [DOI: 10.1080/17512433.2017.1321985] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Affiliation(s)
- François Curtin
- Division of Clinical Pharmacology and Toxicology, University of Geneva, Geneva, Switzerland
- Research Center for Statistics, Geneva School of Economics and Management, University of Geneva, Geneva, Switzerland
- Geneuro SA, Geneva, Switzerland
| | - Stephane Heritier
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
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28
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Li W, Chen C, Li X, Beckman RA. Estimation of treatment effect in two-stage confirmatory oncology trials of personalized medicines. Stat Med 2017; 36:1843-1861. [PMID: 28303586 DOI: 10.1002/sim.7272] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2016] [Accepted: 02/14/2017] [Indexed: 12/26/2022]
Abstract
A personalized medicine may benefit a subpopulation with certain predictive biomarker signatures or certain disease types. However, there is great uncertainty about drug activity in a subpopulation when designing a confirmatory trial in practice, and it is logical to take a two-stage approach with the study unless credible external information is available for decision-making purpose. The first stage deselects (or prunes) non-performing subpopulations at an interim analysis, and the second stage pools the remaining subpopulations in the final analysis. The endpoints used at the two stages can be different in general. A key issue of interest is the statistical property of the test statistics and point estimate at the final analysis. Previous research has focused on type I error control and power calculation for such two-stage designs. This manuscript will investigate estimation bias of the treatment effect, which is implicit in the adjustment of nominal type I error for multiplicity control in such two-stage designs. Previous work handles the treatment effect of an intermediate endpoint as a nuisance parameter to provide the most conservative type I error control. This manuscript takes the same approach to explore the bias. The methodology is applied to the two previously studied designs. In the first design, patients with different biomarker levels are enrolled in a study, and the treatment effect is assumed to be in an order. The goal of the interim analysis is to identify a biomarker cut-off point for the subpopulations. In the second design, patients with different tumour types but the same biomarker signature are included in a trial applying a basket design. The goal of the interim analysis is to identify a subset of tumour types in the absence of treatment effect ordering. Closed-form equations are provided for the estimation bias as well as the variance under the two designs. Simulations are conducted under various scenarios to validate the analytic results that demonstrated that the bias can be properly estimated in practice. Worked examples are presented. Extensions to general adaptive designs and operational considerations are discussed. Copyright © 2017 John Wiley & Sons, Ltd.
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Affiliation(s)
- Wen Li
- Biostatistics and Research Decision Sciences, Merck Research Laboratories (MRL), Merck & Co., Inc, Kenilworth, NJ, U.S.A
| | - Cong Chen
- Biostatistics and Research Decision Sciences, Merck Research Laboratories (MRL), Merck & Co., Inc, Kenilworth, NJ, U.S.A
| | - Xiaoyun Li
- Biostatistics and Research Decision Sciences, Merck Research Laboratories (MRL), Merck & Co., Inc, Kenilworth, NJ, U.S.A
| | - Robert A Beckman
- Departments of Oncology and of Biostatistics, Bioinformatics, and Biomathematics, Lombardi Comprehensive Cancer Center and Innovation Center for Biomedical Informatics, Georgetown University Medical Center, 2115 Wisconsin Avenue, Suite 110, Washington, DC, 20007, U.S.A
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29
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Ventz S, Barry WT, Parmigiani G, Trippa L. Bayesian response-adaptive designs for basket trials. Biometrics 2017; 73:905-915. [PMID: 28211944 DOI: 10.1111/biom.12668] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2016] [Revised: 11/01/2016] [Accepted: 01/01/2017] [Indexed: 12/01/2022]
Abstract
We develop a general class of response-adaptive Bayesian designs using hierarchical models, and provide open source software to implement them. Our work is motivated by recent master protocols in oncology, where several treatments are investigated simultaneously in one or multiple disease types, and treatment efficacy is expected to vary across biomarker-defined subpopulations. Adaptive trials such as I-SPY-2 (Barker et al., 2009) and BATTLE (Zhou et al., 2008) are special cases within our framework. We discuss the application of our adaptive scheme to two distinct research goals. The first is to identify a biomarker subpopulation for which a therapy shows evidence of treatment efficacy, and to exclude other subpopulations for which such evidence does not exist. This leads to a subpopulation-finding design. The second is to identify, within biomarker-defined subpopulations, a set of cancer types for which an experimental therapy is superior to the standard-of-care. This goal leads to a subpopulation-stratified design. Using simulations constructed to faithfully represent ongoing cancer sequencing projects, we quantify the potential gains of our proposed designs relative to conventional non-adaptive designs.
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Affiliation(s)
- Steffen Ventz
- University of Rhode Island, Kingston, Rhode Island
- Dana-Farber Cancer Institute, Boston, Massachusetts
| | - William T Barry
- Dana-Farber Cancer Institute, Boston, Massachusetts
- Harvard Medical School, Boston, Massachusetts
| | - Giovanni Parmigiani
- Dana-Farber Cancer Institute, Boston, Massachusetts
- Harvard School of Public Health, Boston, Massachusetts
| | - Lorenzo Trippa
- Dana-Farber Cancer Institute, Boston, Massachusetts
- Harvard School of Public Health, Boston, Massachusetts
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30
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Mehta C, Schäfer H, Daniel H, Irle S. Biomarker-driven population enrichment for adaptive oncology trials with time to event endpoints. Stat Med 2016; 35:5320. [DOI: 10.1002/sim.7068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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31
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Adaptive Biomarker Population Selection in Phase III Confirmatory Trials with Time-to-Event Endpoints. STATISTICS IN BIOSCIENCES 2016. [DOI: 10.1007/s12561-016-9178-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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32
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Chen C, Li N, Shentu Y, Pang L, Beckman RA. Adaptive Informational Design of Confirmatory Phase III Trials With an Uncertain Biomarker Effect to Improve the Probability of Success. Stat Biopharm Res 2016. [DOI: 10.1080/19466315.2016.1173582] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Affiliation(s)
- Cong Chen
- Biostatistics and Research Decision Sciences, Merck Research Laboratories, Upper Gwynedd, PA, USA
| | - Nicole Li
- Biostatistics and Research Decision Sciences, Merck Research Laboratories, Upper Gwynedd, PA, USA
| | - Yue Shentu
- Biostatistics and Research Decision Sciences, Merck Research Laboratories, Upper Gwynedd, PA, USA
| | - Lei Pang
- Departments of Oncology and Biostatistics, Bioinformatics, and Biomathematics, Lombardi Comprehensive Cancer Center and Innovation Center for Biomedical Informatics, Georgetown University Medical Center, Washington, DC, USA
| | - Robert A. Beckman
- Departments of Oncology and Biostatistics, Bioinformatics, and Biomathematics, Lombardi Comprehensive Cancer Center and Innovation Center for Biomedical Informatics, Georgetown University Medical Center, Washington, DC, USA
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33
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Affiliation(s)
- Deepak L Bhatt
- From Brigham and Women's Hospital Heart and Vascular Center and Harvard Medical School (D.L.B.) and Harvard T.H. Chan School of Public Health (C.M.), Boston, and Cytel, Cambridge (C.M.) - all in Massachusetts
| | - Cyrus Mehta
- From Brigham and Women's Hospital Heart and Vascular Center and Harvard Medical School (D.L.B.) and Harvard T.H. Chan School of Public Health (C.M.), Boston, and Cytel, Cambridge (C.M.) - all in Massachusetts
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34
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Rauch G, Schüler S, Wirths M, Englert S, Kieser M. Adaptive Designs for Two Candidate Primary Time-to-Event Endpoints. Stat Biopharm Res 2016. [DOI: 10.1080/19466315.2016.1143391] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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35
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Kim RS, Goossens N, Hoshida Y. Use of big data in drug development for precision medicine. EXPERT REVIEW OF PRECISION MEDICINE AND DRUG DEVELOPMENT 2016; 1:245-253. [PMID: 27430024 DOI: 10.1080/23808993.2016.1174062] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
Drug development has been a costly and lengthy process with an extremely low success rate and lack of consideration of individual diversity in drug response and toxicity. Over the past decade, an alternative "big data" approach has been expanding at an unprecedented pace based on the development of electronic databases of chemical substances, disease gene/protein targets, functional readouts, and clinical information covering inter-individual genetic variations and toxicities. This paradigm shift has enabled systematic, high-throughput, and accelerated identification of novel drugs or repurposed indications of existing drugs for pathogenic molecular aberrations specifically present in each individual patient. The exploding interest from the information technology and direct-to-consumer genetic testing industries has been further facilitating the use of big data to achieve personalized Precision Medicine. Here we overview currently available resources and discuss future prospects.
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Affiliation(s)
- Rosa S Kim
- Division of Liver Diseases, Department of Medicine, Liver Cancer Program, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Nicolas Goossens
- Division of Liver Diseases, Department of Medicine, Liver Cancer Program, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, USA; Division of Gastroenterology and Hepatology, Geneva University Hospital, Geneva, Switzerland
| | - Yujin Hoshida
- Division of Liver Diseases, Department of Medicine, Liver Cancer Program, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, USA
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36
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Comparison of different clinical development plans for confirmatory subpopulation selection. Contemp Clin Trials 2016; 47:78-84. [DOI: 10.1016/j.cct.2015.12.014] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2015] [Revised: 12/15/2015] [Accepted: 12/19/2015] [Indexed: 01/13/2023]
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37
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Antoniou M, Jorgensen AL, Kolamunnage-Dona R. Biomarker-Guided Adaptive Trial Designs in Phase II and Phase III: A Methodological Review. PLoS One 2016; 11:e0149803. [PMID: 26910238 PMCID: PMC4766245 DOI: 10.1371/journal.pone.0149803] [Citation(s) in RCA: 68] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2015] [Accepted: 02/04/2016] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND Personalized medicine is a growing area of research which aims to tailor the treatment given to a patient according to one or more personal characteristics. These characteristics can be demographic such as age or gender, or biological such as a genetic or other biomarker. Prior to utilizing a patient's biomarker information in clinical practice, robust testing in terms of analytical validity, clinical validity and clinical utility is necessary. A number of clinical trial designs have been proposed for testing a biomarker's clinical utility, including Phase II and Phase III clinical trials which aim to test the effectiveness of a biomarker-guided approach to treatment; these designs can be broadly classified into adaptive and non-adaptive. While adaptive designs allow planned modifications based on accumulating information during a trial, non-adaptive designs are typically simpler but less flexible. METHODS AND FINDINGS We have undertaken a comprehensive review of biomarker-guided adaptive trial designs proposed in the past decade. We have identified eight distinct biomarker-guided adaptive designs and nine variations from 107 studies. Substantial variability has been observed in terms of how trial designs are described and particularly in the terminology used by different authors. We have graphically displayed the current biomarker-guided adaptive trial designs and summarised the characteristics of each design. CONCLUSIONS Our in-depth overview provides future researchers with clarity in definition, methodology and terminology for biomarker-guided adaptive trial designs.
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Affiliation(s)
- Miranta Antoniou
- MRC North West Hub For Trials Methodology Research, Liverpool, United Kingdom
- Department of Biostatistics, Institute of Translational Medicine, University of Liverpool, L69 3GL, Liverpool, United Kingdom
- * E-mail:
| | - Andrea L Jorgensen
- MRC North West Hub For Trials Methodology Research, Liverpool, United Kingdom
- Department of Biostatistics, Institute of Translational Medicine, University of Liverpool, L69 3GL, Liverpool, United Kingdom
| | - Ruwanthi Kolamunnage-Dona
- MRC North West Hub For Trials Methodology Research, Liverpool, United Kingdom
- Department of Biostatistics, Institute of Translational Medicine, University of Liverpool, L69 3GL, Liverpool, United Kingdom
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38
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Magirr D, Jaki T, Koenig F, Posch M. Sample Size Reassessment and Hypothesis Testing in Adaptive Survival Trials. PLoS One 2016; 11:e0146465. [PMID: 26863139 PMCID: PMC4749572 DOI: 10.1371/journal.pone.0146465] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2015] [Accepted: 12/17/2015] [Indexed: 11/18/2022] Open
Abstract
Mid-study design modifications are becoming increasingly accepted in confirmatory clinical trials, so long as appropriate methods are applied such that error rates are controlled. It is therefore unfortunate that the important case of time-to-event endpoints is not easily handled by the standard theory. We analyze current methods that allow design modifications to be based on the full interim data, i.e., not only the observed event times but also secondary endpoint and safety data from patients who are yet to have an event. We show that the final test statistic may ignore a substantial subset of the observed event times. An alternative test incorporating all event times is found, where a conservative assumption must be made in order to guarantee type I error control. We examine the power of this approach using the example of a clinical trial comparing two cancer therapies.
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Affiliation(s)
- Dominic Magirr
- Section of Medical Statistics, Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria
| | - Thomas Jaki
- Medical and Pharmaceutical Statistics Research Unit, Lancaster University, Lancaster, United Kingdom
| | - Franz Koenig
- Section of Medical Statistics, Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria
| | - Martin Posch
- Section of Medical Statistics, Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria
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Bauer P, Bretz F, Dragalin V, König F, Wassmer G. Twenty-five years of confirmatory adaptive designs: opportunities and pitfalls. Stat Med 2016; 35:325-47. [PMID: 25778935 PMCID: PMC6680191 DOI: 10.1002/sim.6472] [Citation(s) in RCA: 130] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2014] [Revised: 02/03/2015] [Accepted: 02/19/2015] [Indexed: 12/26/2022]
Abstract
'Multistage testing with adaptive designs' was the title of an article by Peter Bauer that appeared 1989 in the German journal Biometrie und Informatik in Medizin und Biologie. The journal does not exist anymore but the methodology found widespread interest in the scientific community over the past 25 years. The use of such multistage adaptive designs raised many controversial discussions from the beginning on, especially after the publication by Bauer and Köhne 1994 in Biometrics: Broad enthusiasm about potential applications of such designs faced critical positions regarding their statistical efficiency. Despite, or possibly because of, this controversy, the methodology and its areas of applications grew steadily over the years, with significant contributions from statisticians working in academia, industry and agencies around the world. In the meantime, such type of adaptive designs have become the subject of two major regulatory guidance documents in the US and Europe and the field is still evolving. Developments are particularly noteworthy in the most important applications of adaptive designs, including sample size reassessment, treatment selection procedures, and population enrichment designs. In this article, we summarize the developments over the past 25 years from different perspectives. We provide a historical overview of the early days, review the key methodological concepts and summarize regulatory and industry perspectives on such designs. Then, we illustrate the application of adaptive designs with three case studies, including unblinded sample size reassessment, adaptive treatment selection, and adaptive endpoint selection. We also discuss the availability of software for evaluating and performing such designs. We conclude with a critical review of how expectations from the beginning were fulfilled, and - if not - discuss potential reasons why this did not happen.
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Affiliation(s)
- Peter Bauer
- Section of Medical StatisticsMedical University of ViennaSpitalgasse 231090 WienAustria
| | - Frank Bretz
- Novartis Pharma AGLichtstrasse 354002BaselSwitzerland
- Shanghai University of Finance and EconomicsChina
| | | | - Franz König
- Section of Medical StatisticsMedical University of ViennaSpitalgasse 231090 WienAustria
| | - Gernot Wassmer
- Aptiv Solutions, an ICON plc companyRobert‐Perthel‐Str. 77a50739KölnGermany
- Institute for Medical Statistics, Informatics and EpidemiologyUniversity of Cologne50924KölnGermany
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40
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Renfro LA, Mallick H, An MW, Sargent DJ, Mandrekar SJ. Clinical trial designs incorporating predictive biomarkers. Cancer Treat Rev 2016; 43:74-82. [PMID: 26827695 DOI: 10.1016/j.ctrv.2015.12.008] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2015] [Revised: 12/26/2015] [Accepted: 12/29/2015] [Indexed: 01/13/2023]
Abstract
Development of oncologic therapies has traditionally been performed in a sequence of clinical trials intended to assess safety (phase I), preliminary efficacy (phase II), and improvement over the standard of care (phase III) in homogeneous (in terms of tumor type and disease stage) patient populations. As cancer has become increasingly understood on the molecular level, newer "targeted" drugs that inhibit specific cancer cell growth and survival mechanisms have increased the need for new clinical trial designs, wherein pertinent questions on the relationship between patient biomarkers and response to treatment can be answered. Herein, we review the clinical trial design literature from initial to more recently proposed designs for targeted agents or those treatments hypothesized to have enhanced effectiveness within patient subgroups (e.g., those with a certain biomarker value or who harbor a certain genetic tumor mutation). We also describe a number of real clinical trials where biomarker-based designs have been utilized, including a discussion of their respective advantages and challenges. As cancers become further categorized and/or reclassified according to individual patient and tumor features, we anticipate a continued need for novel trial designs to keep pace with the changing frontier of clinical cancer research.
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Affiliation(s)
- Lindsay A Renfro
- Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, USA.
| | - Himel Mallick
- Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA; The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Ming-Wen An
- Department of Mathematics and Statistics, Vassar College, Poughkeepsie, NY, USA
| | - Daniel J Sargent
- Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, USA
| | - Sumithra J Mandrekar
- Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, USA
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Ondra T, Dmitrienko A, Friede T, Graf A, Miller F, Stallard N, Posch M. Methods for identification and confirmation of targeted subgroups in clinical trials: A systematic review. J Biopharm Stat 2016; 26:99-119. [PMID: 26378339 PMCID: PMC4732423 DOI: 10.1080/10543406.2015.1092034] [Citation(s) in RCA: 73] [Impact Index Per Article: 9.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2015] [Accepted: 08/14/2015] [Indexed: 12/30/2022]
Abstract
Important objectives in the development of stratified medicines include the identification and confirmation of subgroups of patients with a beneficial treatment effect and a positive benefit-risk balance. We report the results of a literature review on methodological approaches to the design and analysis of clinical trials investigating a potential heterogeneity of treatment effects across subgroups. The identified approaches are classified based on certain characteristics of the proposed trial designs and analysis methods. We distinguish between exploratory and confirmatory subgroup analysis, frequentist, Bayesian and decision-theoretic approaches and, last, fixed-sample, group-sequential, and adaptive designs and illustrate the available trial designs and analysis strategies with published case studies.
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Affiliation(s)
- Thomas Ondra
- Center for Medical Statistics and Informatics, Medizinische Universität Wien, Vienna, Austria
| | - Alex Dmitrienko
- Center for Statistics in Drug Development, Quintiles, Overland Park, Kansas, USA
| | - Tim Friede
- Department of Medical Statistics, Universitaetsmedizin, Göttingen, Göttingen, Germany
| | - Alexandra Graf
- Center for Medical Statistics and Informatics, Medizinische Universität Wien, Vienna, Austria
| | - Frank Miller
- Statistiska institutionen, Stockholms Universitet, Stockholm, Sweden
| | - Nigel Stallard
- Department of Statistics and Epidemiology, University of Warwick, Coventry, UK
| | - Martin Posch
- Center for Medical Statistics and Informatics, Medizinische Universität Wien, Vienna, Austria
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42
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Optimal decision rules for biomarker-based subgroup selection for a targeted therapy in oncology. Int J Mol Sci 2015; 16:10354-75. [PMID: 25961947 PMCID: PMC4463650 DOI: 10.3390/ijms160510354] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2015] [Revised: 04/16/2015] [Accepted: 04/29/2015] [Indexed: 01/14/2023] Open
Abstract
Throughout recent years, there has been a rapidly increasing interest regarding the evaluation of so-called targeted therapies. These therapies are assumed to show a greater benefit in a pre-specified subgroup of patients—commonly identified by a predictive biomarker—as compared to the total patient population of interest. This situation has led to the necessity to develop biostatistical methods allowing an efficient evaluation of such treatments. Among others, adaptive enrichment designs have been proposed as a solution. These designs allow the selection of the most promising patient population based on an efficacy analysis at interim and restricting recruitment to these patients afterwards. As has recently been shown, the performance of the applied interim decision rule in such a design plays a crucial role in ensuring a successful trial. In this work, we investigate the situation when the primary outcome of the trial is a binary variable. Optimal decision rules are derived which incorporate the uncertainty about the treatment effects. These optimal decision rules are evaluated with respect to their performance in an adaptive enrichment design in terms of correct selection probability and power, and are compared to proposed ad hoc decision rules. Our methods are illustrated by means of a clinical trial example.
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43
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Hill SR, Olson LG. NICE, social values, and balancing objectivity and equity. PHARMACOECONOMICS 2014; 32:1039-1041. [PMID: 25249201 DOI: 10.1007/s40273-014-0220-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Affiliation(s)
- Suzanne R Hill
- University of Melbourne Medical School, University of Melbourne, Parkville, VIC, 3010, Australia,
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