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Biard L, Andrillon A, Silva RB, Lee SM. Dose optimization for cancer treatments with considerations for late-onset toxicities. Clin Trials 2024; 21:322-330. [PMID: 38591582 PMCID: PMC11132952 DOI: 10.1177/17407745231221152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/10/2024]
Abstract
Given that novel anticancer therapies have different toxicity profiles and mechanisms of action, it is important to reconsider the current approaches for dose selection. In an effort to move away from considering the maximum tolerated dose as the optimal dose, the Food and Drug Administration Project Optimus points to the need of incorporating long-term toxicity evaluation, given that many of these novel agents lead to late-onset or cumulative toxicities and there are no guidelines on how to handle them. Numerous methods have been proposed to handle late-onset toxicities in dose-finding clinical trials. A summary and comparison of these methods are provided. Moreover, using PI3K inhibitors as a case study, we show how late-onset toxicity can be integrated into the dose-optimization strategy using current available approaches. We illustrate a re-design of this trial to compare the approach to those that only consider early toxicity outcomes and disregard late-onset toxicities. We also provide proposals going forward for dose optimization in early development of novel anticancer agents with considerations for late-onset toxicities.
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Affiliation(s)
- Lucie Biard
- INSERM U1153 Team ECSTRRA, Université Paris Cité, Paris, France
| | - Anaïs Andrillon
- INSERM U1153 Team ECSTRRA, Université Paris Cité, Paris, France
- Department of Statistical Methodology, Saryga, Tournus, France
| | - Rebecca B Silva
- Columbia University, Mailman School of Public Health, Department of Biostatistics, New York, USA
| | - Shing M Lee
- Columbia University, Mailman School of Public Health, Department of Biostatistics, New York, USA
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2
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Kojima M. Application of multi-armed bandits to dose-finding clinical designs. Artif Intell Med 2023; 146:102713. [PMID: 38042600 DOI: 10.1016/j.artmed.2023.102713] [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: 06/07/2022] [Revised: 10/29/2023] [Accepted: 10/30/2023] [Indexed: 12/04/2023]
Abstract
Multi-armed bandits are very simple and powerful methods to determine actions to maximize a reward in a limited number of trials. An early phase in dose-finding clinical trials needs to identify the maximum tolerated dose among multiple doses by repeating the dose-assignment. We consider applying the superior selection performance of multi-armed bandits to dose-finding clinical designs. Among the multi-armed bandits, we first consider the use of Thompson sampling which determines actions based on random samples from a posterior distribution. In the small sample size, as shown in dose-finding trials, because the tails of posterior distribution are heavier and random samples are too much variability, we also consider an application of regularized Thompson sampling and greedy algorithm. The greedy algorithm determines a dose based on a posterior mean. In addition, we also propose a method to determine a dose based on a posterior mode. We evaluate the performance of our proposed designs for nine scenarios via simulation studies.
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Affiliation(s)
- Masahiro Kojima
- Kyowa Kirin Co., Ltd, Japan; The Institute of Statistical Mathematics, Japan.
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3
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Ursino M, Biard L, Chevret S. DICE: A Bayesian model for early dose finding in phase I trials with multiple treatment courses. Biom J 2022; 64:1486-1497. [PMID: 34729815 DOI: 10.1002/bimj.202000369] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2021] [Revised: 09/02/2021] [Accepted: 09/04/2021] [Indexed: 12/14/2022]
Abstract
Dose-finding clinical trials in oncology aim to determine the maximum tolerated dose (MTD) of a new drug, generally defined by the proportion of patients with short-term dose-limiting toxicities (DLTs). Model-based approaches for such phase I oncology trials have been widely designed and are mostly restricted to the DLTs occurring during the first cycle of treatment, although patients continue to receive treatment for multiple cycles. We aim to estimate the probability of DLTs over sequences of treatment cycles via a Bayesian cumulative modeling approach, where the probability of DLT is modeled taking into account the cumulative effect of the administered drug and the DLT cycle of occurrence. We propose a design, called DICE (Dose-fInding CumulativE), for dose escalation and de-escalation according to previously observed toxicities, which aims at finding the MTD sequence (MTS). We performed an extensive simulation study comparing this approach to the time-to-event continual reassessment method (TITE-CRM) and a benchmark. In general, our approach achieved a better or comparable percentage of correct MTS selection. Moreover, we investigated the DICE prediction ability.
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Affiliation(s)
- Moreno Ursino
- Centre de Recherche des Cordeliers, INSERM, Sorbonne Université, USPC, Université de Paris, Paris, France
- Inria, HeKA, Paris, France
- Unit of Clinical Epidemiology, Assistance Publique-Hp̂itaux de Paris, CHU Robert Debré, Université de Paris, Sorbonne Paris-Cité, INSERM CIC-EC 1426, Paris, France
| | - Lucie Biard
- Hôpital Saint Louis, Service de Biostatistique et Information Médicale, INSERM U1153 Team ECSTRRA, Université de Paris, AP-HP, Paris, France
| | - Sylvie Chevret
- Hôpital Saint Louis, Service de Biostatistique et Information Médicale, INSERM U1153 Team ECSTRRA, Université de Paris, AP-HP, Paris, France
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4
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Brown SR, Hinsley S, Hall E, Hurt C, Baird RD, Forster M, Scarsbrook AF, Adams RA. A Road Map for Designing Phase I Clinical Trials of Radiotherapy-Novel Agent Combinations. Clin Cancer Res 2022; 28:3639-3651. [PMID: 35552622 PMCID: PMC9433953 DOI: 10.1158/1078-0432.ccr-21-4087] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 01/26/2022] [Accepted: 04/28/2022] [Indexed: 01/07/2023]
Abstract
Radiotherapy has proven efficacy in a wide range of cancers. There is growing interest in evaluating radiotherapy-novel agent combinations and a drive to initiate this earlier in the clinical development of the novel agent, where the scientific rationale and preclinical evidence for a radiotherapy combination approach are high. Optimal design, delivery, and interpretation of studies are essential. In particular, the design of phase I studies to determine safety and dosing is critical to an efficient development strategy. There is significant interest in early-phase research among scientific and clinical communities over recent years, at a time when the scrutiny of the trial methodology has significantly increased. To enhance trial design, optimize safety, and promote efficient trial conduct, this position paper reviews the current phase I trial design landscape. Key design characteristics extracted from 37 methodology papers were used to define a road map and a design selection process for phase I radiotherapy-novel agent trials. Design selection is based on single- or dual-therapy dose escalation, dose-limiting toxicity categorization, maximum tolerated dose determination, subgroup evaluation, software availability, and design performance. Fifteen of the 37 designs were identified as being immediately accessible and relevant to radiotherapy-novel agent phase I trials. Applied examples of using the road map are presented. Developing these studies is intensive, highlighting the need for funding and statistical input early in the trial development to ensure appropriate design and implementation from the outset. The application of this road map will improve the design of phase I radiotherapy-novel agent combination trials, enabling a more efficient development pathway.
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Affiliation(s)
- Sarah R. Brown
- Leeds Cancer Research UK Clinical Trials Unit, Leeds Institute of Clinical Trials Research, University of Leeds, Leeds, United Kingdom
| | - Samantha Hinsley
- Clinical Trials Unit Glasgow, University of Glasgow, Glasgow, United Kingdom
| | - Emma Hall
- Clinical Trials and Statistics Unit, The Institute of Cancer Research, London, United Kingdom
| | - Chris Hurt
- Centre for Trials Research, Cardiff University, Cardiff, United Kingdom
| | | | | | - Andrew F. Scarsbrook
- Radiotherapy Research Group, Leeds Institute of Medical Research at St James's, Faculty of Medicine and Health, University of Leeds, Leeds, United Kingdom
| | - Richard A. Adams
- Centre for Trials Research, Cardiff University and Velindre Cancer Centre, Cardiff, United Kingdom
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5
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Gerard E, Zohar S, Lorenzato C, Ursino M, Riviere MK. Bayesian modeling of a bivariate toxicity outcome for early phase oncology trials evaluating dose regimens. Stat Med 2021; 40:5096-5114. [PMID: 34259343 PMCID: PMC9292544 DOI: 10.1002/sim.9113] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Revised: 04/23/2021] [Accepted: 05/25/2021] [Indexed: 11/05/2022]
Abstract
Most phase I trials in oncology aim to find the maximum tolerated dose (MTD) based on the occurrence of dose limiting toxicities (DLT). Evaluating the schedule of administration in addition to the dose may improve drug tolerance. Moreover, for some molecules, a bivariate toxicity endpoint may be more appropriate than a single endpoint. However, standard dose‐finding designs do not account for multiple dose regimens and bivariate toxicity endpoint within the same design. In this context, following a phase I motivating trial, we proposed modeling the first type of DLT, cytokine release syndrome, with the entire dose regimen using pharmacokinetics and pharmacodynamics (PK/PD), whereas the other DLT (DLTo) was modeled with the cumulative dose. We developed three approaches to model the joint distribution of DLT, defining it as a bivariate binary outcome from the two toxicity types, under various assumptions about the correlation between toxicities: an independent model, a copula model and a conditional model. Our Bayesian approaches were developed to be applied at the end of the dose‐allocation stage of the trial, once all data, including PK/PD measurements, were available. The approaches were evaluated through an extensive simulation study that showed that they can improve the performance of selecting the true MTD‐regimen compared to the recommendation of the dose‐allocation method implemented. Our joint approaches can also predict the DLT probabilities of new dose regimens that were not tested in the study and could be investigated in further stages of the trial.
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Affiliation(s)
- Emma Gerard
- Inserm, Centre de Recherche des Cordeliers, Université de Paris, Sorbonne Université, Paris, France.,HeKA, Inria, Paris, France.,Oncology Biostatistics, Biostatistics and Programming Department, Sanofi R&D, Vitry-sur-Seine, France.,Statistical Methodology Group, Biostatistics and Programming Department, Sanofi R&D, Chilly-Mazarin, France
| | - Sarah Zohar
- Inserm, Centre de Recherche des Cordeliers, Université de Paris, Sorbonne Université, Paris, France.,HeKA, Inria, Paris, France
| | - Christelle Lorenzato
- Oncology Biostatistics, Biostatistics and Programming Department, Sanofi R&D, Vitry-sur-Seine, France
| | - Moreno Ursino
- Inserm, Centre de Recherche des Cordeliers, Université de Paris, Sorbonne Université, Paris, France.,HeKA, Inria, Paris, France.,Unit of Clinical Epidemiology, AP-HP, CHU Robert Debré, Université de Paris, Sorbonne Paris-Cité, Inserm U1123 and CIC-EC 1426, Paris, France
| | - Marie-Karelle Riviere
- Statistical Methodology Group, Biostatistics and Programming Department, Sanofi R&D, Chilly-Mazarin, France
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6
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Biard L, Lee SM, Cheng B. Seamless phase I/II design for novel anticancer agents with competing disease progression. Stat Med 2021; 40:4568-4581. [PMID: 34213022 DOI: 10.1002/sim.9080] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Revised: 03/19/2021] [Accepted: 05/09/2021] [Indexed: 11/08/2022]
Abstract
Molecularly targeted agents and immunotherapies have prolonged administration and complicated toxicity and efficacy profiles requiring longer toxicity observation windows and the inclusion of efficacy information to identify the optimal dose. Methods have been proposed to either jointly model toxicity and efficacy, or for prolonged observation windows. However, it is inappropriate to address these issues individually in the setting of dose-finding because longer toxicity windows increase the risk of patients experiencing disease progression and discontinuing the trial, with progression defining a competing event to toxicity, and progression-free survival being a commonly used efficacy endpoint. No method has been proposed to address this issue in a competing risk framework. We propose a seamless phase I/II design, namely the competing risks continual reassessment method (CR-CRM). Given an observation window, the objective is to recommend doses that minimize the progression probability, among a set of tolerable doses in terms of toxicity risk. In toxicity-centered stage of the design, doses are assigned based on toxicity alone, and in optimization stage of the design, doses are assigned integrating both toxicity and progression information. Design operating characteristics were examined in a simulation study compared with benchmark performances, including sensitivity to time-varying hazards and correlated events. The method performs well in selecting doses with acceptable toxicity risk and minimum progression risk across a wide range of scenarios.
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Affiliation(s)
- Lucie Biard
- Department of Biostatistics, Columbia University Mailman School of Public Health, New York City, New York, USA.,Université de Paris, AP-HP, Hôpital Saint Louis, DMU PRISME, INSERM U1153 Team ECSTRRA, Paris, France
| | - Shing M Lee
- Department of Biostatistics, Columbia University Mailman School of Public Health, New York City, New York, USA
| | - Bin Cheng
- Department of Biostatistics, Columbia University Mailman School of Public Health, New York City, New York, USA
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7
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Mi G, Bian Y, Wang X, Zhang W. SPA: Single patient acceleration in oncology dose-escalation trials. Contemp Clin Trials 2021; 105:106378. [PMID: 33823296 DOI: 10.1016/j.cct.2021.106378] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Revised: 03/17/2021] [Accepted: 03/26/2021] [Indexed: 11/30/2022]
Abstract
Efficient identification of the optimal dose and dosing scheme is one of the most critical and challenging tasks in early-phase oncology trials. The results are far-reaching because advancing a sub-optimal dose to late-stage development may not only jeopardize patients' safety or fail to deliver desired efficacy, but also be costly to sponsors as refined doses must be evaluated further before seeking regulatory approval. A good dose-escalation design is anticipated to yield high accuracy of selecting the correct dose while using fewer patients and keeping the trial duration short. Recently, treating a single patient at each lower dose level until certain events are triggered to switch to larger cohorts has gained much popularity. We name this approach "Single Patient Acceleration" (SPA), which is essentially a variant of the Accelerated Titration Design (ATD) by Simon et al. [25]. Although literature on novel dose-escalation methods is abundant in the past decade, there is a surprisingly lack of research on evaluating the ATD/SPA framework. In this article, we conduct comprehensive simulations to evaluate the performance of dose-escalation designs with or without SPA, and show that SPA improves design efficiency with similar or better accuracy to those without the "single patient" component under certain circumstances (e.g., slow initial enrollment, or the true maximum tolerated dose is at higher candidate dose levels). Potential safety concerns as a cost of efficiency improvement are also investigated in a quantitative manner to illustrate a comprehensive benefit-risk profile of SPA. Practical considerations and recommendations in using SPA are also discussed.
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Affiliation(s)
- Gu Mi
- Statistics, Data and Analytics, Eli Lilly and Company, Lilly Corporate Center, Indianapolis, IN 46285, USA.
| | - Yuanyuan Bian
- Statistics, Data and Analytics, Eli Lilly and Company, Lilly Corporate Center, Indianapolis, IN 46285, USA.
| | - Xuejing Wang
- Statistics, Data and Analytics, Eli Lilly and Company, Lilly Corporate Center, Indianapolis, IN 46285, USA.
| | - Wei Zhang
- Statistics, Data and Analytics, Eli Lilly and Company, Lilly Corporate Center, Indianapolis, IN 46285, USA.
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8
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Altzerinakou MA, Paoletti X. Change-point joint model for identification of plateau of activity in early phase trials. Stat Med 2021; 40:2113-2138. [PMID: 33561898 DOI: 10.1002/sim.8889] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2020] [Revised: 12/19/2020] [Accepted: 01/06/2021] [Indexed: 11/10/2022]
Abstract
This article presents a phase I/II trial design for targeted therapies and immunotherapies, with the objective of identifying the optimal dose (OD). We employ a joint modeling technique for discrete time-to-event toxicity data and repeated and continuous biomarker measurements. For the biomarker measurements, we implement a change point linear mixed effects skeleton model. This model can accommodate both plateauing and nonplateauing dose-activity relationships. For each new cohort of patients, we estimate the maximum tolerated dose (MTD) taking toxicity as a cumulative endpoint, over six treatment cycles. Then, we select the OD using two different criteria. The OD is a dose that is equally active to the MTD or a dose located on the beginning of the plateau of the dose-activity relationship. Joint modeling allows us to take into account informative censoring due to toxicities or lack of activity and we also consider consent withdrawal and intermittent missing responses. Model estimation relies on likelihood inference.
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Affiliation(s)
| | - Xavier Paoletti
- Université Versailles St Quentin, Université Paris Saclay, INSERM U900 STAMPM, Saint-Cloud, France.,Institut Curie, Paris, France
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9
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Lee SM, Wages NA, Goodman KA, Lockhart AC. Designing Dose-Finding Phase I Clinical Trials: Top 10 Questions That Should Be Discussed With Your Statistician. JCO Precis Oncol 2021; 5:317-324. [PMID: 34151131 DOI: 10.1200/po.20.00379] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Revised: 12/08/2020] [Accepted: 12/21/2020] [Indexed: 01/22/2023] Open
Abstract
In recent years, the landscape in clinical trial development has changed to involve many molecularly targeted agents, immunotherapies, or radiotherapy, as a single agent or in combination. Given their different mechanisms of action and lengths of administration, these agents have different toxicity profiles, which has resulted in numerous challenges when applying traditional designs such as the 3 + 3 design in dose-finding clinical trials. Novel methods have been proposed to address these design challenges such as combinations of therapies or late-onset toxicities. However, their design and implementation require close collaboration between clinicians and statisticians to ensure that the appropriate design is selected to address the aims of the study and that the design assumptions are pertinent to the study drug. The goal of this paper is to provide guidelines for appropriate questions that should be considered early in the design stage to facilitate the interactions between clinical and statistical teams and to improve the design of dose-finding clinical trials for novel anticancer agents.
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Affiliation(s)
- Shing M Lee
- Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY
| | - Nolan A Wages
- Division of Translational Research and Applied Statistics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA
| | - Karyn A Goodman
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY
| | - A Craig Lockhart
- Division of Medical Oncology, University of Miami, Sylvester Comprehensive Cancer Center, Miami, FL
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Andrillon A, Chevret S, Lee SM, Biard L. Dose-finding design and benchmark for a right censored endpoint. J Biopharm Stat 2020; 30:948-963. [DOI: 10.1080/10543406.2020.1821702] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Affiliation(s)
- Anaïs Andrillon
- INSERM U1153 Team ECSTRRA, Université De Paris, Paris, France
| | - Sylvie Chevret
- INSERM U1153 Team ECSTRRA, Université De Paris, Paris, France
| | - Shing M Lee
- Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY, USA
| | - Lucie Biard
- INSERM U1153 Team ECSTRRA, Université De Paris, Paris, France
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11
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Mozgunov P, Jaki T. An information theoretic approach for selecting arms in clinical trials. J R Stat Soc Series B Stat Methodol 2020. [DOI: 10.1111/rssb.12391] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
| | - Thomas Jaki
- Lancaster University and University of Cambridge UK
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