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Cherlin S, Wason JMS. Cross-validated risk scores adaptive enrichment (CADEN) design. Contemp Clin Trials 2024; 144:107620. [PMID: 38977178 DOI: 10.1016/j.cct.2024.107620] [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: 03/04/2024] [Revised: 07/01/2024] [Accepted: 07/03/2024] [Indexed: 07/10/2024]
Abstract
We propose a Cross-validated ADaptive ENrichment design (CADEN) in which a trial population is enriched with a subpopulation of patients who are predicted to benefit from the treatment more than an average patient (the sensitive group). This subpopulation is found using a risk score constructed from the baseline (potentially high-dimensional) information about patients. The design incorporates an early stopping rule for futility. Simulation studies are used to assess the properties of CADEN against the original (non-enrichment) cross-validated risk scores (CVRS) design which constructs a risk score at the end of the trial. We show that when there exists a sensitive group of patients, CADEN achieves a higher power and a reduction in the expected sample size compared to the CVRS design. We illustrate the application of the design in two real clinical trials. We conclude that the new design offers improved statistical efficiency over the existing non-enrichment method, as well as increased benefit to patients. The method has been implemented in an R package caden.
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Affiliation(s)
- Svetlana Cherlin
- Population Health Sciences Institute, Newcastle University, Baddiley-Clark Building, Newcastle upon Tyne, UK.
| | - James M S Wason
- Population Health Sciences Institute, Newcastle University, Baddiley-Clark Building, Newcastle upon Tyne, UK
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2
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Lipkovich I, Svensson D, Ratitch B, Dmitrienko A. Modern approaches for evaluating treatment effect heterogeneity from clinical trials and observational data. Stat Med 2024. [PMID: 39054669 DOI: 10.1002/sim.10167] [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: 11/20/2023] [Revised: 05/28/2024] [Accepted: 06/21/2024] [Indexed: 07/27/2024]
Abstract
In this paper, we review recent advances in statistical methods for the evaluation of the heterogeneity of treatment effects (HTE), including subgroup identification and estimation of individualized treatment regimens, from randomized clinical trials and observational studies. We identify several types of approaches using the features introduced in Lipkovich et al (Stat Med 2017;36: 136-196) that distinguish the recommended principled methods from basic methods for HTE evaluation that typically rely on rules of thumb and general guidelines (the methods are often referred to as common practices). We discuss the advantages and disadvantages of various principled methods as well as common measures for evaluating their performance. We use simulated data and a case study based on a historical clinical trial to illustrate several new approaches to HTE evaluation.
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Affiliation(s)
- Ilya Lipkovich
- Advanced Analytics and Access Capabilities, Eli Lilly and Company, Indianapolis, Indiana, USA
| | - David Svensson
- Statistical Innovation, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| | - Bohdana Ratitch
- Clinical Statistics and Analytics, Research & Development, Pharmaceuticals, Bayer Inc., Mississauga, Ontario, Canada
| | - Alex Dmitrienko
- Department of Biostatistics, Mediana, San Juan, Puerto Rico, USA
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Zhao B, Fine J, Ivanova A. Finding the best subgroup with differential treatment effect with multiple outcomes. Stat Med 2024; 43:2487-2500. [PMID: 38621856 DOI: 10.1002/sim.10083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 02/15/2024] [Accepted: 04/02/2024] [Indexed: 04/17/2024]
Abstract
Precision medicine aims to identify specific patient subgroups that may benefit the most from a particular treatment than the whole population. Existing definitions for the best subgroup in subgroup analysis are based on a single outcome and do not consider multiple outcomes; specifically, outcomes of different types. In this article, we introduce a definition for the best subgroup under a multiple-outcome setting with continuous, binary, and censored time-to-event outcomes. Our definition provides a trade-off between the subgroup size and the conditional average treatment effects (CATE) in the subgroup with respect to each of the outcomes while taking the relative contribution of the outcomes into account. We conduct simulations to illustrate the proposed definition. By examining the outcomes of urinary tract infection and renal scarring in the RIVUR clinical trial, we identify a subgroup of children that would benefit the most from long-term antimicrobial prophylaxis.
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Affiliation(s)
- Beibo Zhao
- Department of Biostatistics, CB #7420, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Jason Fine
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland, USA
| | - Anastasia Ivanova
- Department of Biostatistics, CB #7420, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
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Vinnat V, Annane D, Chevret S. Bayesian Sequential Design for Identifying and Ranking Effective Patient Subgroups in Precision Medicine in the Case of Counting Outcome Data with Inflated Zeros. J Pers Med 2023; 13:1560. [PMID: 38003875 PMCID: PMC10672716 DOI: 10.3390/jpm13111560] [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: 10/03/2023] [Revised: 10/23/2023] [Accepted: 10/29/2023] [Indexed: 11/26/2023] Open
Abstract
Precision medicine is revolutionizing health care, particularly by addressing patient variability due to different biological profiles. As traditional treatments may not always be appropriate for certain patient subsets, the rise of biomarker-stratified clinical trials has driven the need for innovative methods. We introduced a Bayesian sequential scheme to evaluate therapeutic interventions in an intensive care unit setting, focusing on complex endpoints characterized by an excess of zeros and right truncation. By using a zero-inflated truncated Poisson model, we efficiently addressed this data complexity. The posterior distribution of rankings and the surface under the cumulative ranking curve (SUCRA) approach provided a comprehensive ranking of the subgroups studied. Different subsets of subgroups were evaluated depending on the availability of biomarker data. Interim analyses, accounting for early stopping for efficacy, were an integral aspect of our design. The simulation study demonstrated a high proportion of correct identification of the subgroup which is the most predictive of the treatment effect, as well as satisfactory false positive and true positive rates. As the role of personalized medicine grows, especially in the intensive care setting, it is critical to have designs that can manage complicated endpoints and that can control for decision error. Our method seems promising in this challenging context.
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Affiliation(s)
- Valentin Vinnat
- ECSTRRA Team, INSERM U1153, Université Paris Cité, 75010 Paris, France;
| | - Djillali Annane
- Intensive Care Unit, Raymond Poincaré Hospital, 78266 Garches, France;
| | - Sylvie Chevret
- ECSTRRA Team, INSERM U1153, Université Paris Cité, 75010 Paris, France;
- Institut Universitaire de France (IUF), 75231 Paris, France
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Baldi Antognini A, Frieri R, Zagoraiou M. New insights into adaptive enrichment designs. Stat Pap (Berl) 2023. [DOI: 10.1007/s00362-023-01433-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/03/2023]
Abstract
AbstractThe transition towards personalized medicine is happening and the new experimental framework is raising several challenges, from a clinical, ethical, logistical, regulatory, and statistical perspective. To face these challenges, innovative study designs with increasing complexity have been proposed. In particular, adaptive enrichment designs are becoming more attractive for their flexibility. However, these procedures rely on an increasing number of parameters that are unknown at the planning stage of the clinical trial, so the study design requires particular care. This review is dedicated to adaptive enrichment studies with a focus on design aspects. While many papers deal with methods for the analysis, the sample size determination and the optimal allocation problem have been overlooked. We discuss the multiple aspects involved in adaptive enrichment designs that contribute to their advantages and disadvantages. The decision-making process of whether or not it is worth enriching should be driven by clinical and ethical considerations as well as scientific and statistical concerns.
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Park Y. Challenges and opportunities in biomarker-driven trials: adaptive randomization. ANNALS OF TRANSLATIONAL MEDICINE 2022; 10:1035. [PMID: 36267794 PMCID: PMC9577777 DOI: 10.21037/atm-21-6027] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/13/2021] [Accepted: 02/25/2022] [Indexed: 11/25/2022]
Abstract
In an era of precision medicine, as advanced technology such as molecular profiling at individual patient level has been developed and become increasingly accessible and affordable, biomarker-driven trials have been received a lot of attention and are expected to receive more attention in order to integrate clinical practice with clinical research. Biomarkers play a critical role to identify patients who are expected to get benefit from a treatment, and it is important to effectively incorporate the biomarkers into clinical trials to understand the biomarker-treatment relationship and increase the efficiency. We investigate incorporating biomarkers in adaptive randomization to identify patients who would respond better to the treatment and optimize the treatment allocation. The covariate-adjusted variants of the existing response-adaptive randomization are used to implement biomarker-driven randomization, and the performance of the biomarker-driven randomization is compared with the existing randomization methods, such as traditional fixed randomization with equal probability and response-adaptive randomization without incorporating biomarkers, under the group sequential design allowing early stopping due to superiority and futility. Various scenarios are taken into account to see the impact of the biomarker-driven randomization in the simulation study. It shows that the overall type I error rate is likely to be inflated by the effect of prognostic biomarkers. Several suggestions and considerations for the challenges are discussed to maintain the type I error rate at the nominal level.
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Affiliation(s)
- Yeonhee Park
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA
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Seifu Y, Gamalo-Siebers M, Lin J. A note regarding the special issue on innovative design and analysis of complex clinical trials and opportunities for future research. J Biopharm Stat 2021; 31:113-116. [PMID: 33678133 DOI: 10.1080/10543406.2021.1895193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Affiliation(s)
- Yodit Seifu
- Amgen, Inc., One, Thousand Oaks, California, USA
| | | | - Junjing Lin
- Takeda Pharmaceutical Co. Limited, Cambridge, Massachusetts, USA
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Ivanova A, Israel E, LaVange LM, Peters MC, Denlinger LC, Moore WC, Bacharier LB, Marquis MA, Gotman NM, Kosorok MR, Tomlinson C, Mauger DT, Georas SN, Wright RJ, Noel P, Rosner GL, Akuthota P, Billheimer D, Bleecker ER, Cardet JC, Castro M, DiMango EA, Erzurum SC, Fahy JV, Fajt ML, Gaston BM, Holguin F, Jain S, Kenyon NJ, Krishnan JA, Kraft M, Kumar R, Liu MC, Ly NP, Moy JN, Phipatanakul W, Ross K, Smith LJ, Szefler SJ, Teague WG, Wechsler ME, Wenzel SE, White SR. The precision interventions for severe and/or exacerbation-prone asthma (PrecISE) adaptive platform trial: statistical considerations. J Biopharm Stat 2020; 30:1026-1037. [PMID: 32941098 PMCID: PMC7954787 DOI: 10.1080/10543406.2020.1821705] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Accepted: 08/17/2020] [Indexed: 12/24/2022]
Abstract
The Precision Interventions for Severe and/or Exacerbation-prone Asthma (PrecISE) study is an adaptive platform trial designed to investigate novel interventions to severe asthma. The study is conducted under a master protocol and utilizes a crossover design with each participant receiving up to five interventions and at least one placebo. Treatment assignments are based on the patients' biomarker profiles and precision health methods are incorporated into the interim and final analyses. We describe key elements of the PrecISE study including the multistage adaptive enrichment strategy, early stopping of an intervention for futility, power calculations, and the primary analysis strategy.
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Affiliation(s)
| | - Elliot Israel
- Brigham and Women’s Hospital and Harvard Medical School, Boston, MA
| | | | | | | | | | | | | | | | | | | | | | | | | | - Patricia Noel
- Division of Lung Diseases, National Heart, Lung and Blood Institute (NHLBI), National Institutes of Health, Bethesda, MD
| | | | - Praveen Akuthota
- Asthma and Airway Disease Research Center, University of Arizona, Tucson
| | - Dean Billheimer
- Asthma and Airway Disease Research Center, University of Arizona, Tucson
| | | | | | | | | | | | | | - Merritt L. Fajt
- Wells Center for Pediatric Research, Indiana University, Indianapolis
| | | | | | | | | | - Jerry A. Krishnan
- Asthma and Airway Disease Research Center, University of Arizona, Tucson
| | | | | | | | - Ngoc P. Ly
- Rush University Medical Center, Chicago, IL
| | - James N. Moy
- Boston Children’s Hospital and Harvard Medical School, Boston, MA
| | - Wanda Phipatanakul
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD
| | - Kristie Ross
- UH Rainbow Babies and Children’s Hospitals, Cleveland, OH
| | | | - Stanley J. Szefler
- Children’s Hospital Colorado and University of Colorado School of Medicine, Aurora, CO
| | | | | | - Sally E. Wenzel
- National Jewish Health, Denver, CO, and University of Colorado School of Medicine, Aurora, CO
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