1
|
Zhang W, Lei W, Zhu X. A novel model of the continual reassessment method in Phase I trial. Sci Rep 2023; 13:5047. [PMID: 36977709 PMCID: PMC10050314 DOI: 10.1038/s41598-023-28148-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Accepted: 01/13/2023] [Indexed: 03/30/2023] Open
Abstract
For the model-based designs, the continual reassessment method (CRM) is widely used to identify the maximum tolerated dose (MTD) in phase I clinical trials. To improve the performance of classic CRM models, we propose a new CRM and its dose-toxicity probability function based on the Cox model whatever the treatment response is immediately observed or delayed. In the process of dose-finding trial, we can use our model in situations when either the response is delayed or not and can derive the likelihood function and posterior mean toxicity probabilities to find the MTD. Simulation is carried out to evaluate the performance of the proposed model with the classic CRM models. We also evaluate the operating characteristics of the proposed model by the Efficiency, Accuracy, Reliability, and Safety (EARS) criteria.
Collapse
Affiliation(s)
- Weijia Zhang
- Center of Statistical Research and School of Statistics, Southwestern University of Finance and Economics, Chengdu, 611130, Sichuan, China
| | - Wanni Lei
- Department of Applied Mathematics, Xi'an Jiaotong-Liverpool University, Suzhou, 215123, Jiangsu, China.
| | - Xiaojun Zhu
- Department of Financial and Actuarial Mathematics, Xi'an Jiaotong-Liverpool University, suzhou, 215123, Jiangsu, China
| |
Collapse
|
2
|
Lin R, Shi H, Yin G, Thall PF, Yuan Y, Flowers CR. BAYESIAN HIERARCHICAL RANDOM-EFFECTS META-ANALYSIS AND DESIGN OF PHASE I CLINICAL TRIALS. Ann Appl Stat 2022; 16:2481-2504. [PMID: 36329718 PMCID: PMC9624503 DOI: 10.1214/22-aoas1600] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/30/2024]
Abstract
We propose a curve-free random-effects meta-analysis approach to combining data from multiple phase I clinical trials to identify an optimal dose. Our method accounts for between-study heterogeneity that may stem from different study designs, patient populations, or tumor types. We also develop a meta-analytic-predictive (MAP) method based on a power prior that incorporates data from multiple historical studies into the design and conduct of a new phase I trial. Performances of the proposed methods for data analysis and trial design are evaluated by extensive simulation studies. The proposed random-effects meta-analysis method provides more reliable dose selection than comparators that rely on parametric assumptions. The MAP-based dose-finding designs are generally more efficient than those that do not borrow information, especially when the current and historical studies are similar. The proposed methodologies are illustrated by a meta-analysis of five historical phase I studies of Sorafenib, and design of a new phase I trial.
Collapse
Affiliation(s)
- Ruitao Lin
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, U.S.A
| | - Haolun Shi
- Department of Statistics and Actuarial Science, Simon Fraser University, British Columbia, Canada
| | - Guosheng Yin
- Department of Statistics and Actuarial Science, The University of Hong Kong, Pokfulam Road, Hong Kong
| | - 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
| | - Christopher R Flowers
- Department of Lymphoma/Myeloma, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, U.S.A
| |
Collapse
|
3
|
Chen X, Zhang J, Jiang Q, Yan F. Borrowing historical information to improve phase I clinical trials using meta-analytic-predictive priors. J Biopharm Stat 2022; 32:34-52. [PMID: 35594366 DOI: 10.1080/10543406.2022.2058526] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
Multiple phase I clinical trials may be performed to determine specific maximum tolerated doses (MTD) for specific races or cancer types. In these situations, borrowing historical information has potential to improve the accuracy of estimating toxicity rate and increase the probability of correctly targeting MTD. To utilize historical information in phase I clinical trials, we proposed using the Meta-Analytic-Predictive (MAP) priors to automatically estimate the heterogeneity between historical trials and give a relatively reasonable amount of borrowed information. We then applied MAP priors in some famous phase I trial designs, such as the continual reassessment method (CRM), Keyboard design and Bayesian optimal interval design (BOIN), to accomplish the process of dose finding. A clinical trial example and extended simulation studies show that our proposed methods have robust and efficient statistical performance, compared with those designs which do not consider borrowing information.
Collapse
Affiliation(s)
- Xin Chen
- Department of Biostatistics, China Pharmaceutical University, Nanjing, Jiangsu, China
| | - Jingyi Zhang
- Department of Biostatistics, China Pharmaceutical University, Nanjing, Jiangsu, China
| | - Qian Jiang
- Department of Biostatistics, China Pharmaceutical University, Nanjing, Jiangsu, China
| | - Fangrong Yan
- Department of Biostatistics, China Pharmaceutical University, Nanjing, Jiangsu, China
| |
Collapse
|
4
|
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.
Collapse
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
| |
Collapse
|
5
|
Zou Y, Li N, Shao LJZ, Liu FK, Xue FS, Tao X. Determination of the ED 95 of intranasal sufentanil combined with intranasal dexmedetomidine for moderate sedation during endoscopic ultrasonography. World J Clin Cases 2022; 10:2773-2782. [PMID: 35434098 PMCID: PMC8968820 DOI: 10.12998/wjcc.v10.i9.2773] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Revised: 12/24/2021] [Accepted: 01/29/2022] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Sedation during endoscopic ultrasonography (EUS) poses many challenges and moderate-to-deep sedation are often required. The conventional method to preform moderate-to-deep sedation is generally intravenous benzodiazepine alone or in combination with opioids. However, this combination has some limitations. Intranasal medication delivery may be an alternative to this sedation regimen.
AIM To determine, by continual reassessment method (CRM), the minimal effective dose of intranasal sufentanil (SUF) when combined with intranasal dexmedetomidine (DEX) for moderate sedation of EUS in at least 95% of patients (ED95).
METHODS Thirty patients aged 18-65 and scheduled for EUS were recruited in this study. Subjects received intranasal DEX and SUF for sedation. The dose of DEX (1 μg/kg) was fixed, while the dose of SUF was assigned sequentially to the subjects using CRM to determine ED95. The sedation status was assessed by modified observer’s assessment of alertness/sedation (MOAA/S) score. The adverse events and the satisfaction scores of patients and endoscopists were recorded.
RESULTS The ED95 was intranasal 0.3 μg/kg SUF when combined with intranasal 1 μg/kg DEX, with an estimated probability of successful moderate sedation for EUS of 94.9% (95% confidence interval: 88.1%-98.9%). When combined with intranasal 1 μg/kg DEX, probabilities of successful moderate sedation at each dose level of intranasal SUF were as follows: 0 μg/kg SUF, 52.8%; 0.1 μg/kg SUF, 75.4%; 0.2 μg/kg SUF, 89.9%; 0.3 μg/kg SUF, 94.9%; 0.4 μg/kg SUF, 98.0%; 0.5 μg/kg SUF, 99.0%.
CONCLUSION The ED95 needed for moderate sedation for EUS is intranasal 0.3 μg/kg SUF when combined with intranasal 1 μg/kg DEX, based on CRM.
Collapse
Affiliation(s)
- Yi Zou
- Department of Anesthesiology, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China
| | - Na Li
- Department of Anesthesiology, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China
| | - Liu-Jia-Zi Shao
- Department of Anesthesiology, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China
| | - Fu-Kun Liu
- Department of Anesthesiology, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China
| | - Fu-Shan Xue
- Department of Anesthesiology, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China
| | - Xing Tao
- Department of Anesthesiology, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China
| |
Collapse
|
6
|
Hi3 + 3: A model-assisted dose-finding design borrowing historical data. Contemp Clin Trials 2021; 109:106437. [PMID: 34020007 DOI: 10.1016/j.cct.2021.106437] [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: 10/22/2020] [Revised: 05/10/2021] [Accepted: 05/11/2021] [Indexed: 11/22/2022]
Abstract
BACKGROUND In phase I clinical trials, historical data may be available through multi-regional programs, reformulation of the same drug, or previous trials for a drug under the same class. Statistical designs that borrow information from historical data can reduce cost, speed up drug development, and maintain safety. PURPOSE Based on a hybrid design that partly uses probability models and partly uses algorithmic rules for decision making, we aim to improve the efficiency of the dose-finding trials in the presence of historical data, maintain safety for patients, and achieve a level of simplicity for practical applications. METHODS We propose the Hi3 + 3 design, in which the letter "H" represents "historical data". We apply the idea in power prior to borrow historical data and define the effective sample size (ESS) of the prior. Dose-finding decision rules follow the idea in the i3 + 3 design (Liu et al., 2020 [1]) while incorporating the historical data via the power prior and ESS. The proposed Hi3 + 3 design pretabulates the dosing decisions before the trial starts, a desirable feature for ease of application in practice. RESULTS In most cases we investigated, the Hi3 + 3 design is superior than the i3 + 3 design due to information borrow from historical data. Even when the historical data is incompatible with the current data, it is capable of maintaining a high level of safety for trial patients and comparable performances without sacrificing the ability to identify the correct MTD too much. Ilustration of this feature are found in the simulation results. CONCLUSION With the demonstrated safety, efficiency, and simplicity, the Hi3 + 3 design could be a desirable choice for dose-finding trials borrowing historical data.
Collapse
|
7
|
Günhan BK, Weber S, Seroutou A, Friede T. Phase I dose-escalation oncology trials with sequential multiple schedules. BMC Med Res Methodol 2021; 21:69. [PMID: 33853539 PMCID: PMC8045405 DOI: 10.1186/s12874-021-01218-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Accepted: 01/26/2021] [Indexed: 11/30/2022] Open
Abstract
BACKGROUND Conventional methods for phase I dose-escalation trials in oncology are based on a single treatment schedule only. More recently, however, multiple schedules are more frequently investigated in the same trial. METHODS Here, we consider sequential phase I trials, where the trial proceeds with a new schedule (e.g. daily or weekly dosing) once the dose escalation with another schedule has been completed. The aim is to utilize the information from both the completed and the ongoing schedules to inform decisions on the dose level for the next dose cohort. For this purpose, we adapted the time-to-event pharmacokinetics (TITE-PK) model, which were originally developed for simultaneous investigation of multiple schedules. TITE-PK integrates information from multiple schedules using a pharmacokinetics (PK) model. RESULTS In a simulation study, the developed approach is compared to the bridging continual reassessment method and the Bayesian logistic regression model using a meta-analytic-predictive prior. TITE-PK results in better performance than comparators in terms of recommending acceptable dose and avoiding overly toxic doses for sequential phase I trials in most of the scenarios considered. Furthermore, better performance of TITE-PK is achieved while requiring similar number of patients in the simulated trials. For the scenarios involving one schedule, TITE-PK displays similar performance with alternatives in terms of acceptable dose recommendations. The R and Stan code for the implementation of an illustrative sequential phase I trial example in oncology is publicly available ( https://github.com/gunhanb/TITEPK_sequential ). CONCLUSION In phase I oncology trials with sequential multiple schedules, the use of all relevant information is of great importance. For these trials, the adapted TITE-PK which combines information using PK principles is recommended.
Collapse
Affiliation(s)
- Burak Kürsad Günhan
- Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany.
| | | | | | - Tim Friede
- Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany
| |
Collapse
|
8
|
Zhou Y, Lee JJ, Wang S, Bailey S, Yuan Y. Incorporating historical information to improve phase I clinical trials. Pharm Stat 2021; 20:1017-1034. [PMID: 33793044 DOI: 10.1002/pst.2121] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2020] [Revised: 03/08/2021] [Accepted: 03/14/2021] [Indexed: 12/19/2022]
Abstract
Incorporating historical data has a great potential to improve the efficiency of phase I clinical trials and to accelerate drug development. For model-based designs, such as the continuous reassessment method (CRM), this can be conveniently carried out by specifying a "skeleton," that is, the prior estimate of dose limiting toxicity (DLT) probability at each dose. In contrast, little work has been done to incorporate historical data into model-assisted designs, such as the Bayesian optimal interval (BOIN), Keyboard, and modified toxicity probability interval (mTPI) designs. This has led to the misconception that model-assisted designs cannot incorporate prior information. In this paper, we propose a unified framework that allows for incorporating historical data into model-assisted designs. The proposed approach uses the well-established "skeleton" approach, combined with the concept of prior effective sample size, thus it is easy to understand and use. More importantly, our approach maintains the hallmark of model-assisted designs: simplicity-the dose escalation/de-escalation rule can be tabulated prior to the trial conduct. Extensive simulation studies show that the proposed method can effectively incorporate prior information to improve the operating characteristics of model-assisted designs, similarly to model-based designs.
Collapse
Affiliation(s)
- Yanhong Zhou
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - J Jack Lee
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Shunguang Wang
- Novartis Institutes for BioMedical Research, Cambridge, Massachusetts, USA
| | - Stuart Bailey
- Novartis Institutes for BioMedical Research, Cambridge, Massachusetts, USA
| | - Ying Yuan
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| |
Collapse
|
9
|
Estimating Similarity of Dose-Response Relationships in Phase I Clinical Trials-Case Study in Bridging Data Package. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18041639. [PMID: 33572323 PMCID: PMC7916097 DOI: 10.3390/ijerph18041639] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Revised: 02/03/2021] [Accepted: 02/05/2021] [Indexed: 02/05/2023]
Abstract
Bridging studies are designed to fill the gap between two populations in terms of clinical trial data, such as toxicity, efficacy, comorbidities and doses. According to ICH-E5 guidelines, clinical data can be extrapolated from one region to another if dose–reponse curves are similar between two populations. For instance, in Japan, Phase I clinical trials are often repeated due to this physiological/metabolic paradigm: the maximum tolerated dose (MTD) for Japanese patients is assumed to be lower than that for Caucasian patients, but not necessarily for all molecules. Therefore, proposing a statistical tool evaluating the similarity between two populations dose–response curves is of most interest. The aim of our work is to propose several indicators to evaluate the distance and the similarity of dose–toxicity curves and MTD distributions at the end of some of the Phase I trials, conducted on two populations or regions. For this purpose, we extended and adapted the commensurability criterion, initially proposed by Ollier et al. (2019), in the setting of completed phase I clinical trials. We evaluated their performance using three synthetic sets, built as examples, and six case studies found in the literature. Visualization plots and guidelines on the way to interpret the results are proposed.
Collapse
|
10
|
Zheng H, Hampson LV, Jaki T. Bridging across patient subgroups in phase I oncology trials that incorporate animal data. Stat Methods Med Res 2021; 30:1057-1071. [PMID: 33501882 PMCID: PMC8129464 DOI: 10.1177/0962280220986580] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
In this paper, we develop a general Bayesian hierarchical model for bridging across patient subgroups in phase I oncology trials, for which preliminary information about the dose-toxicity relationship can be drawn from animal studies. Parameters that re-scale the doses to adjust for intrinsic differences in toxicity, either between animals and humans or between human subgroups, are introduced to each dose-toxicity model. Appropriate priors are specified for these scaling parameters, which capture the magnitude of uncertainty surrounding the animal-to-human translation and bridging assumption. After mapping data onto a common, 'average' human dosing scale, human dose-toxicity parameters are assumed to be exchangeable either with the standardised, animal study-specific parameters, or between themselves across human subgroups. Random-effects distributions are distinguished by different covariance matrices that reflect the between-study heterogeneity in animals and humans. Possibility of non-exchangeability is allowed to avoid inferences for extreme subgroups being overly influenced by their complementary data. We illustrate the proposed approach with hypothetical examples, and use simulation to compare the operating characteristics of trials analysed using our Bayesian model with several alternatives. Numerical results show that the proposed approach yields robust inferences, even when data from multiple sources are inconsistent and/or the bridging assumptions are incorrect.
Collapse
Affiliation(s)
- Haiyan Zheng
- Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, UK.,Department of Mathematics and Statistics, Lancaster University, Lancashire, UK
| | - Lisa V Hampson
- Advanced Methodology and Data Science, Novartis Pharma AG, Basel, Switzerland
| | - Thomas Jaki
- Department of Mathematics and Statistics, Lancaster University, Lancashire, UK.,MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| |
Collapse
|
11
|
Li Y, Yuan Y. PA-CRM: A continuous reassessment method for pediatric phase I oncology trials with concurrent adult trials. Biometrics 2020; 76:1364-1373. [PMID: 31950483 DOI: 10.1111/biom.13217] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2019] [Revised: 12/17/2019] [Accepted: 12/31/2019] [Indexed: 11/29/2022]
Abstract
Pediatric phase I trials are usually carried out after the adult trial testing the same agent has started, but not completed yet. As the pediatric trial progresses, in light of the accrued interim data from the concurrent adult trial, the pediatric protocol often is amended to modify the original pediatric dose escalation design. In practice, this is done frequently in an ad hoc way, interrupting patient accrual and slowing down the trial. We developed a pediatric-continuous reassessment method (PA-CRM) to streamline this process, providing a more efficient and rigorous method to find the maximum tolerated dose for pediatric phase I oncology trials. We use a discounted joint likelihood of the adult and pediatric data, with a discount parameter controlling information borrowing between pediatric and adult trials. According to the interim adult and pediatric data, the discount parameter is adaptively updated using the Bayesian model averaging method. Numerical study shows that the PA-CRM improves the efficiency and accuracy of the pediatric trial and is robust to various model assumptions.
Collapse
Affiliation(s)
- Yimei Li
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania & The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Ying Yuan
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas
| |
Collapse
|
12
|
Ollier A, Morita S, Ursino M, Zohar S. An adaptive power prior for sequential clinical trials - Application to bridging studies. Stat Methods Med Res 2019; 29:2282-2294. [PMID: 31729275 PMCID: PMC7433690 DOI: 10.1177/0962280219886609] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
During drug evaluation trials, information from clinical trials previously conducted on another population, indications or schedules may be available. In these cases, it might be desirable to share information by efficiently using the available resources. In this work, we developed an adaptive power prior with a commensurability parameter for using historical or external information. It allows, at each stage, full borrowing when the data are not in conflict, no borrowing when the data are in conflict or "tuned" borrowing when the data are in between. We propose to apply our adaptive power prior method to bridging studies between Caucasians and Asians, and we focus on the sequential adaptive allocation design, although other design settings can be used. We weight the prior information in two steps: the effective sample size approach is used to set the maximum desirable amount of information to be shared from historical data at each step of the trial; then, in a sort of Empirical Bayes approach, a commensurability parameter is chosen using a measure of distribution distance. This approach avoids elicitation and computational issues regarding the usual Empirical Bayes approach. We propose several versions of our method, and we conducted an extensive simulation study evaluating the robustness and sensitivity to prior choices.
Collapse
Affiliation(s)
- Adrien Ollier
- Centre de Recherche des Cordeliers, INSERM, Sorbonne Université, USPC, Université de Paris, Paris, France
| | - Satoshi Morita
- Department of Biomedical Statistics and Bioinformatics, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Moreno Ursino
- Centre de Recherche des Cordeliers, INSERM, Sorbonne Université, USPC, Université de Paris, Paris, France
| | - Sarah Zohar
- Centre de Recherche des Cordeliers, INSERM, Sorbonne Université, USPC, Université de Paris, Paris, France
| |
Collapse
|
13
|
Cotterill A, Jaki T. Dose-escalation strategies which use subgroup information. Pharm Stat 2018; 17:414-436. [PMID: 29900666 PMCID: PMC6175353 DOI: 10.1002/pst.1860] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2017] [Revised: 01/30/2018] [Accepted: 02/26/2018] [Indexed: 12/04/2022]
Abstract
Dose-escalation trials commonly assume a homogeneous trial population to identify a single recommended dose of the experimental treatment for use in future trials. Wrongly assuming a homogeneous population can lead to a diluted treatment effect. Equally, exclusion of a subgroup that could in fact benefit from the treatment can cause a beneficial treatment effect to be missed. Accounting for a potential subgroup effect (ie, difference in reaction to the treatment between subgroups) in dose-escalation can increase the chance of finding the treatment to be efficacious in a larger patient population. A standard Bayesian model-based method of dose-escalation is extended to account for a subgroup effect by including covariates for subgroup membership in the dose-toxicity model. A stratified design performs well but uses available data inefficiently and makes no inferences concerning presence of a subgroup effect. A hypothesis test could potentially rectify this problem but the small sample sizes result in a low-powered test. As an alternative, the use of spike and slab priors for variable selection is proposed. This method continually assesses the presence of a subgroup effect, enabling efficient use of the available trial data throughout escalation and in identifying the recommended dose(s). A simulation study, based on real trial data, was conducted and this design was found to be both promising and feasible.
Collapse
Affiliation(s)
- Amy Cotterill
- Cancer Research UK Clinical Trials Unit, Institute of Cancer and Genomic SciencesUniversity of BirminghamBirminghamUK
| | - Thomas Jaki
- Medical and Pharmaceutical Statistics Research Unit, Department of Mathematics and StatisticsLancaster UniversityLancasterUK
| |
Collapse
|
14
|
Takeda K, Morita S. Bayesian dose-finding phase I trial design incorporating historical data from a preceding trial. Pharm Stat 2018; 17:372-382. [PMID: 29372582 DOI: 10.1002/pst.1850] [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: 10/16/2017] [Accepted: 12/18/2017] [Indexed: 11/08/2022]
Abstract
We consider the problem of incorporating historical data from a preceding trial to design and conduct a subsequent dose-finding trial in a possibly different population of patients. In oncology, for example, after a phase I dose-finding trial is completed in Caucasian patients, investigators often conduct a further phase I trial to determine the maximum tolerated dose in Asian patients. This may be due to concerns about possible differences in treatment tolerability between populations. In this study, we propose to adaptively incorporate historical data into prior distributions assumed in a new dose-finding trial. Our proposed approach aims to appropriately borrow strength from a previous trial to improve the maximum tolerated dose determination in another patient population. We define a "historical-to-current (H-C)" parameter representing the degree of borrowing based on a retrospective analysis of previous trial data. In simulation studies, we examine the operating characteristics of the proposed method in comparison with 3 alternative approaches and assess how the H-C parameter functions across a variety of realistic settings.
Collapse
Affiliation(s)
- Kentaro Takeda
- Data Science, Astellas Pharma Global Development, Inc., Illinois, USA
| | - Satoshi Morita
- Department of Biomedical Statistics and Bioinformatics, Kyoto University Graduate School of Medicine, Kyoto, Japan
| |
Collapse
|
15
|
Cunanan KM, Koopmeiners JS. Hierarchical models for sharing information across populations in phase I dose-escalation studies. Stat Methods Med Res 2017; 27:3447-3459. [DOI: 10.1177/0962280217703812] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The primary goal of a phase I clinical trial in oncology is to evaluate the safety of a novel treatment and identify the maximum tolerated dose, defined as the maximum dose with a toxicity rate below some pre-specified threshold. Researchers are often interested in evaluating the performance of a novel treatment in multiple patient populations, which may require multiple phase I trials if the treatment is to be used with background standard-of-care that varies by population. An alternate approach is to run parallel trials but combine the data through a hierarchical model that allows for a different maximum tolerated dose in each population but shares information across populations to achieve a more accurate estimate of the maximum tolerated dose. In this manuscript, we discuss hierarchical extensions of three commonly used models for the dose–toxicity relationship in phase I oncology trials. We then propose three dose-finding guidelines for phase I oncology trials using hierarchical modeling. The proposed guidelines allow us to fully realize the benefits of hierarchical modeling while achieving a similar toxicity profile to standard phase I designs. Finally, we evaluate the operating characteristics of a phase I clinical trial using the proposed hierarchical models and dose-finding guidelines by simulation. Our simulation results suggest that incorporating hierarchical modeling in phase I dose-escalation studies will increase the probability of correctly identifying the maximum tolerated dose and the number of patients treated at the maximum tolerated dose, while decreasing the rate of dose-limiting toxicities and number of patients treated above the maximum tolerated dose, in most cases.
Collapse
Affiliation(s)
- Kristen M Cunanan
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Joseph S Koopmeiners
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| |
Collapse
|
16
|
Wilhelm-Benartzi CS, Mt-Isa S, Fiorentino F, Brown R, Ashby D. Challenges and methodology in the incorporation of biomarkers in cancer clinical trials. Crit Rev Oncol Hematol 2017; 110:49-61. [PMID: 28109405 DOI: 10.1016/j.critrevonc.2016.12.008] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2016] [Revised: 10/28/2016] [Accepted: 12/12/2016] [Indexed: 12/14/2022] Open
Abstract
Biomarkers can be used to establish more homogeneous groups using the genetic makeup of the tumour to inform the selection of treatment for each individual patient. However, proper preclinical work and stringent validation are needed before taking forward biomarkers into confirmatory studies. Despite the challenges, incorporation of biomarkers into clinical trials could better target appropriate patients, and potentially be lifesaving. The authors conducted a systematic review to describe marker-based and adaptive design methodology for their integration in clinical trials, and to further describe the associated practical challenges. Studies published between 1990 to November 2015 were searched on PubMed. Titles, abstracts and full text articles were reviewed to identify relevant studies. Of the 4438 studies examined, 57 studies were included. The authors conclude that the proposed approaches may readily help researchers to design biomarker trials, but novel approaches are still needed.
Collapse
Affiliation(s)
- Charlotte S Wilhelm-Benartzi
- CRUK Imperial Centre, Department of Surgery and Cancer, Imperial College London, UK; Imperial Clinical Trials Unit, School of Public Health, Imperial College London, UK.
| | - Shahrul Mt-Isa
- Imperial Clinical Trials Unit, School of Public Health, Imperial College London, UK
| | - Francesca Fiorentino
- Imperial Clinical Trials Unit, School of Public Health, Imperial College London, UK
| | - Robert Brown
- Epigenetics Unit, Department of Surgery and Cancer, Imperial College London, UK
| | - Deborah Ashby
- Imperial Clinical Trials Unit, School of Public Health, Imperial College London, UK
| |
Collapse
|
17
|
Morita S, Thall PF, Takeda K. A simulation study of methods for selecting subgroup-specific doses in phase 1 trials. Pharm Stat 2017; 16:143-156. [PMID: 28111916 DOI: 10.1002/pst.1797] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2016] [Revised: 09/20/2016] [Accepted: 11/04/2016] [Indexed: 01/27/2023]
Abstract
Patient heterogeneity may complicate dose-finding in phase 1 clinical trials if the dose-toxicity curves differ between subgroups. Conducting separate trials within subgroups may lead to infeasibly small sample sizes in subgroups having low prevalence. Alternatively,it is not obvious how to conduct a single trial while accounting for heterogeneity. To address this problem,we consider a generalization of the continual reassessment method on the basis of a hierarchical Bayesian dose-toxicity model that borrows strength between subgroups under the assumption that the subgroups are exchangeable. We evaluate a design using this model that includes subgroup-specific dose selection and safety rules. A simulation study is presented that includes comparison of this method to 3 alternative approaches,on the basis of nonhierarchical models,that make different types of assumptions about within-subgroup dose-toxicity curves. The simulations show that the hierarchical model-based method is recommended in settings where the dose-toxicity curves are exchangeable between subgroups. We present practical guidelines for application and provide computer programs for trial simulation and conduct.
Collapse
Affiliation(s)
- Satoshi Morita
- Department of Biomedical Statistics and Bioinformatics, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Peter F Thall
- Department of Biostatistics, The University of Texas, M. D. Anderson Cancer Center, Houston, TX, USA
| | - Kentaro Takeda
- Department of Biostatistics and Epidemiology, Yokohama City University, Yokohama, Japan.,Biostatistics Group,Data Science,Global Development, Astellas Pharma INC, Tokyo, Japan
| |
Collapse
|
18
|
Petit C, Samson A, Morita S, Ursino M, Guedj J, Jullien V, Comets E, Zohar S. Unified approach for extrapolation and bridging of adult information in early-phase dose-finding paediatric studies. Stat Methods Med Res 2016; 27:1860-1877. [PMID: 27705884 PMCID: PMC5958415 DOI: 10.1177/0962280216671348] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
The number of trials conducted and the number of patients per trial are typically small in paediatric clinical studies. This is due to ethical constraints and the complexity of the medical process for treating children. While incorporating prior knowledge from adults may be extremely valuable, this must be done carefully. In this paper, we propose a unified method for designing and analysing dose-finding trials in paediatrics, while bridging information from adults. The dose-range is calculated under three extrapolation options, linear, allometry and maturation adjustment, using adult pharmacokinetic data. To do this, it is assumed that target exposures are the same in both populations. The working model and prior distribution parameters of the dose–toxicity and dose–efficacy relationships are obtained using early-phase adult toxicity and efficacy data at several dose levels. Priors are integrated into the dose-finding process through Bayesian model selection or adaptive priors. This calibrates the model to adjust for misspecification, if the adult and pediatric data are very different. We performed a simulation study which indicates that incorporating prior adult information in this way may improve dose selection in children.
Collapse
Affiliation(s)
- Caroline Petit
- 1 INSERM, UMRS 1138, CRC, Team 22, University of Paris 5, University of Paris 6, Paris, France
| | - Adeline Samson
- 2 LJK, UMR CNRS 5224, University of Grenoble Alpes, Grenoble, France
| | - Satoshi Morita
- 3 Department of Biomedical Statistics and Bioinformatics, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Moreno Ursino
- 1 INSERM, UMRS 1138, CRC, Team 22, University of Paris 5, University of Paris 6, Paris, France
| | - Jérémie Guedj
- 4 INSERM, IAME, UMR 1137, Université Paris Diderot, Sorbonne Paris Cité, Paris, France
| | - Vincent Jullien
- 5 Pharmacology Department, Hôpital Européen Georges Pompidou, Paris Descartes University, INSERM U1129, Paris, France
| | - Emmanuelle Comets
- 4 INSERM, IAME, UMR 1137, Université Paris Diderot, Sorbonne Paris Cité, Paris, France.,6 INSERM CIC 1414, Université de Rennes 1, Rennes
| | - Sarah Zohar
- 1 INSERM, UMRS 1138, CRC, Team 22, University of Paris 5, University of Paris 6, Paris, France
| |
Collapse
|
19
|
Salter A, Morgan C, Aban IB. Implementation of a two-group likelihood time-to-event continual reassessment method using SAS. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2015; 121:189-196. [PMID: 26122068 DOI: 10.1016/j.cmpb.2015.06.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/09/2015] [Revised: 05/08/2015] [Accepted: 06/02/2015] [Indexed: 06/04/2023]
Abstract
BACKGROUND AND OBJECTIVES Dose finding trials using model-based methods have the ability to handle the increasingly complex landscape being seen in clinical trials. Issues such as patient heterogeneity in trial populations are important to address in the designing of a trial in addition to the inclusion/exclusion criteria. Designs accommodating patient heterogeneity have been described using the continual reassessment method (CRM) and time-to-event CRM (TITE-CRM), yet, the implementation of these trials in practice have been limited. These methods and other model-based methods generally need statisticians to help design and conduct these trials. However, the statistical programs which facilitate the use of these methods, currently available focus on estimation in the one-sample case. METHODS A SAS program to accommodate two groups using the TITE-CRM and likelihood estimation has been developed. The program consists of macros that assist with the planning and implementation of a trial accounting for patient heterogeneity. RESULTS Description of the program is given as well as examples using the programs. For planning purposes, an example will be provided showing how the program can be used to guide sample size estimates for the trial. CONCLUSIONS This program provides researchers with a valuable tool for designing dose-finding studies to account for the presence of patient heterogeneity and conduct a trial using a hypothetical example.
Collapse
Affiliation(s)
- Amber Salter
- Department of Biostatistics, University of Alabama at Birmingham, School of Public Health, 1665 University Blvd., Room 327, Birmingham, AL 35294-0022, USA.
| | - Charity Morgan
- Department of Biostatistics, University of Alabama at Birmingham, School of Public Health, 1665 University Blvd., Room 327, Birmingham, AL 35294-0022, USA
| | - Inmaculada B Aban
- Department of Biostatistics, University of Alabama at Birmingham, School of Public Health, 1665 University Blvd., Room 327, Birmingham, AL 35294-0022, USA
| |
Collapse
|
20
|
Broglio KR, Sandalic L, Albertson T, Berry SM. Bayesian dose escalation in oncology with sharing of information between patient populations. Contemp Clin Trials 2015; 44:56-63. [DOI: 10.1016/j.cct.2015.07.003] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2015] [Revised: 07/01/2015] [Accepted: 07/03/2015] [Indexed: 10/23/2022]
|