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Zhang J, Lin R, Chen X, Yan F. Adaptive Bayesian information borrowing methods for finding and optimizing subgroup-specific doses. Clin Trials 2024; 21:308-321. [PMID: 38243401 PMCID: PMC11132956 DOI: 10.1177/17407745231212193] [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: 01/21/2024]
Abstract
In precision oncology, integrating multiple cancer patient subgroups into a single master protocol allows for the simultaneous assessment of treatment effects in these subgroups and promotes the sharing of information between them, ultimately reducing sample sizes and costs and enhancing scientific validity. However, the safety and efficacy of these therapies may vary across different subgroups, resulting in heterogeneous outcomes. Therefore, identifying subgroup-specific optimal doses in early-phase clinical trials is crucial for the development of future trials. In this article, we review various innovative Bayesian information-borrowing strategies that aim to determine and optimize subgroup-specific doses. Specifically, we discuss Bayesian hierarchical modeling, Bayesian clustering, Bayesian model averaging or selection, pairwise borrowing, and other relevant approaches. By employing these Bayesian information-borrowing methods, investigators can gain a better understanding of the intricate relationships between dose, toxicity, and efficacy in each subgroup. This increased understanding significantly improves the chances of identifying an optimal dose tailored to each specific subgroup. Furthermore, we present several practical recommendations to guide the design of future early-phase oncology trials involving multiple subgroups when using the Bayesian information-borrowing methods.
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Affiliation(s)
- Jingyi Zhang
- Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Ruitao Lin
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Xin Chen
- 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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2
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Yoshihara K, Lee SW, Kim YM, Enomoto T. The 1st annual meeting of the East Asian Gynecologic Oncology Trial Group (EAGOT). J Gynecol Oncol 2023; 34:e87. [PMID: 37668080 DOI: 10.3802/jgo.2023.34.e87] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Revised: 08/09/2023] [Accepted: 08/09/2023] [Indexed: 09/06/2023] Open
Affiliation(s)
- Kosuke Yoshihara
- Department of Obstetrics and Gynecology, Niigata University Graduate School of Medical and Dental Sciences, Niigata, Japan
| | - Shin Wha Lee
- Department of Obstetrics and Gynecology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Yong-Man Kim
- Department of Obstetrics and Gynecology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Takayuki Enomoto
- Department of Obstetrics and Gynecology, Osaka University Graduate School of Medicine, Osaka, Japan.
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Benest J, Rhodes S, Evans TG, White RG. The Correlated Beta Dose Optimisation Approach: Optimal Vaccine Dosing Using Mathematical Modelling and Adaptive Trial Design. Vaccines (Basel) 2022; 10:1838. [PMID: 36366347 PMCID: PMC9693615 DOI: 10.3390/vaccines10111838] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 10/16/2022] [Accepted: 10/28/2022] [Indexed: 12/02/2022] Open
Abstract
Mathematical modelling methods and adaptive trial design are likely to be effective for optimising vaccine dose but are not yet commonly used. This may be due to uncertainty with regard to the correct choice of parametric model for dose-efficacy or dose-toxicity. Non-parametric models have previously been suggested to be potentially useful in this situation. We propose a novel approach for locating optimal vaccine dose based on the non-parametric Continuous Correlated Beta Process model and adaptive trial design. We call this the 'Correlated Beta' or 'CoBe' dose optimisation approach. We evaluated the CoBe dose optimisation approach compared to other vaccine dose optimisation approaches using a simulation study. Despite using simpler assumptions than other modelling-based methods, we found that the CoBe dose optimisation approach was able to effectively locate the maximum efficacy dose for both single and prime/boost administration vaccines. The CoBe dose optimisation approach was also effective in finding a dose that maximises vaccine efficacy and minimises vaccine-related toxicity. Further, we found that these modelling methods can benefit from the inclusion of expert knowledge, which has been difficult for previous parametric modelling methods. This work further shows that using mathematical modelling and adaptive trial design is likely to be beneficial to locating optimal vaccine dose, ensuring maximum vaccine benefit and disease burden reduction, ultimately saving lives.
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Affiliation(s)
- John Benest
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK
| | - Sophie Rhodes
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK
| | - Thomas G. Evans
- Vaccitech Ltd., The Schrodinger Building, Heatley Road, The Oxford Science Park, Oxford OX4 4GE, UK
| | - Richard G. White
- Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK
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Alam MI, Shanto SI. Patient-specific dose finding in seamless phase I/II clinical trials. Seq Anal 2022. [DOI: 10.1080/07474946.2022.2105361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
Affiliation(s)
- M. Iftakhar Alam
- Institute of Statistical Research and Training, University of Dhaka, Dhaka, Bangladesh
| | - Shantonu Islam Shanto
- Institute of Statistical Research and Training, University of Dhaka, Dhaka, Bangladesh
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McGovern A, Chapple AG, Ma C. 2 stage subgroup-specific time-to-event (2S-Sub-TITE): An adaptive two-stage time-to-toxicity design for subgroup-specific dose finding in phase I oncology trials. Pharm Stat 2022; 21:1138-1148. [PMID: 35560864 DOI: 10.1002/pst.2231] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 03/01/2022] [Accepted: 05/03/2022] [Indexed: 11/08/2022]
Abstract
For phase I trials, the subgroup-specific time-to-event (Sub-TITE) design identifies the maximum tolerated dose (MTD) separately in 2+ heterogeneous patient subgroups. Sub-TITE allows borrowing strength and dynamic clustering across subgroups from the trial's start, but delaying the initiation of borrowing and clustering may improve trial accuracy. We propose the 2-stage Sub-TITE (2S-Sub-TITE) design in which the trial starts by estimating separate models per subgroup, and then initiates the Sub-TITE design at some pre-specified point of patient accrual. We evaluate the operating characteristics of the 2S-Sub-TITE design using simulations. Nine configurations of the 2S-Sub-TITE design (varying in timing of initiation of borrowing/clustering and prior probability of subgroup heterogeneity, p_hetero) and three control methods were compared across 1000 randomly-generated true toxicity probability scenarios. Effects of priors, sample size, escalation rules, target toxicity probability, accrual rate, and number of subgroups were evaluated. Metrics included: proportion of correct selection (PCS) of the true MTD, and average number of toxicities incurred. Among the 5 2S-Sub-TITE configurations (out of 9 total) with the highest PCS (45%) when the subgroup heterogeneity assumption is correct (all of which out-perform the control methods by 2%-6%), the configuration which enables borrowing and clustering allowance with p_hetero = 0.7 starting at 75% patient accrual best minimizes toxicities as well as losses in accuracy if the heterogeneity assumption is incorrect. For trials with high confidence in subgroup heterogeneity, the 2S-Sub-TITE configuration enabling borrowing/clustering with p_hetero = 0.7 starting at 75% patient accrual exhibits superior dose-finding accuracy compared to existing methods.
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Affiliation(s)
- Alana McGovern
- Dana-Farber/Boston Children's Cancer and Blood Disorders Center, Boston, Massachusetts, USA.,Department of Statistics, University of Washington, Seattle, Washington, USA
| | - Andrew G Chapple
- Biostatistics Program, School of Public Health, Louisiana State University Health Science Center, New Orleans, Louisiana, USA
| | - Clement Ma
- Dana-Farber/Boston Children's Cancer and Blood Disorders Center, Boston, Massachusetts, USA.,Harvard Medical School, Boston, Massachusetts, USA.,Centre for Addiction and Mental Health, Toronto, Canada.,Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, Canada
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Lin R, Thall PF, Yuan Y. A Phase I-II Basket Trial Design to Optimize Dose-Schedule Regimes Based on Delayed Outcomes. BAYESIAN ANALYSIS 2021; 16:179-202. [PMID: 34267857 PMCID: PMC8277108 DOI: 10.1214/20-ba1205] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
This paper proposes a Bayesian adaptive basket trial design to optimize the dose-schedule regimes of an experimental agent within disease subtypes, called "baskets", for phase I-II clinical trials based on late-onset efficacy and toxicity. To characterize the association among the baskets and regimes, a Bayesian hierarchical model is assumed that includes a heterogeneity parameter, adaptively updated during the trial, that quantifies information shared across baskets. To account for late-onset outcomes when doing sequential decision making, unobserved outcomes are treated as missing values and imputed by exploiting early biomarker and low-grade toxicity information. Elicited joint utilities of efficacy and toxicity are used for decision making. Patients are randomized adaptively to regimes while accounting for baskets, with randomization probabilities proportional to the posterior probability of achieving maximum utility. Simulations are presented to assess the design's robustness and ability to identify optimal dose-schedule regimes within disease subtypes, and to compare it to a simplified design that treats the subtypes independently.
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Affiliation(s)
- Ruitao Lin
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, U.S.A
| | - Peter F Thall
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, U.S.A
| | - Ying Yuan
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, U.S.A
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Tang N, Wang S, Ye G. A nonparametric Bayesian continual reassessment method in single-agent dose-finding studies. BMC Med Res Methodol 2018; 18:172. [PMID: 30563454 PMCID: PMC6299663 DOI: 10.1186/s12874-018-0604-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2018] [Accepted: 11/01/2018] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The main purpose of dose-finding studies in Phase I trial is to estimate maximum tolerated dose (MTD), which is the maximum test dose that can be assigned with an acceptable level of toxicity. Existing methods developed for single-agent dose-finding assume that the dose-toxicity relationship follows a specific parametric potency curve. This assumption may lead to bias and unsafe dose escalations due to the misspecification of parametric curve. METHODS This paper relaxes the parametric assumption of dose-toxicity relationship by imposing a Dirichlet process prior on unknown dose-toxicity curve. A hybrid algorithm combining the Gibbs sampler and adaptive rejection Metropolis sampling (ARMS) algorithm is developed to estimate the dose-toxicity curve, and a two-stage Bayesian nonparametric adaptive design is presented to estimate MTD. RESULTS For comparison, we consider two classical continual reassessment methods (CRMs) (i.e., logistic and power models). Numerical results show the flexibility of the proposed method for single-agent dose-finding trials, and the proposed method behaves better than two classical CRMs under our considered scenarios. CONCLUSIONS The proposed dose-finding procedure is model-free and robust, and behaves satisfactorily even in small sample cases.
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Affiliation(s)
- Niansheng Tang
- Key Lab of Statistical Modeling and Data Analysis of Yunnan Province, Yunnan University, Kunming, 650091, People's Republic of China.
| | - Songjian Wang
- Key Lab of Statistical Modeling and Data Analysis of Yunnan Province, Yunnan University, Kunming, 650091, People's Republic of China
| | - Gen Ye
- Key Lab of Statistical Modeling and Data Analysis of Yunnan Province, Yunnan University, Kunming, 650091, People's Republic of China
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Chapple AG, Thall PF. Subgroup-specific dose finding in phase I clinical trials based on time to toxicity allowing adaptive subgroup combination. Pharm Stat 2018; 17:734-749. [PMID: 30112806 DOI: 10.1002/pst.1891] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2018] [Revised: 05/24/2018] [Accepted: 06/24/2018] [Indexed: 01/26/2023]
Abstract
A Bayesian design is presented that does precision dose finding based on time to toxicity in a phase I clinical trial with two or more patient subgroups. The design, called Sub-TITE, makes sequentially adaptive subgroup-specific decisions while possibly combining subgroups that have similar estimated dose-toxicity curves. Decisions are based on posterior quantities computed under a logistic regression model for the probability of toxicity within a fixed follow-up period, as a function of dose and subgroup. Similarly to the time-to-event continual reassessment method (TITE-CRM, Cheung and Chappell), the Sub-TITE design downweights each patient's likelihood contribution using a function of follow-up time. Spike-and-slab priors are assumed for subgroup parameters, with latent subgroup combination variables included in the logistic model to allow different subgroups to be combined for dose finding if they are homogeneous. This framework can be used in trials where clinicians have identified patient subgroups but are not certain whether they will have different dose-toxicity curves. A simulation study shows that, when the dose-toxicity curves differ between all subgroups, Sub-TITE has superior performance compared with applying the TITE-CRM while ignoring subgroups and has slightly better performance than applying the TITE-CRM separately within subgroups or using the two-group maximum likelihood approach of Salter et al that borrows strength among the two groups. When two or more subgroups are truly homogeneous but differ from other subgroups, the Sub-TITE design is substantially superior to either ignoring subgroups, running separate trials within all subgroups, or the maximum likelihood approach of Salter et al. Practical guidelines and computer software are provided to facilitate application.
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Affiliation(s)
| | - Peter F Thall
- Department of Biostatistics, MD Anderson Cancer Center, Houston, Texas
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