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Ying Yuan - One of the best experts on this subject based on the ideXlab platform.
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utpi a utility based toxicity Probability Interval design for phase i ii dose finding trials
Statistics in Medicine, 2021Co-Authors: Haolun Shi, Ying Yuan, Jiguo Cao, Ruitao LinAbstract:Unlike chemotherapy, the maximum tolerated dose (MTD) of molecularly targeted agents and immunotherapy may not pose significant clinical benefit over the lower doses. By simultaneously considering both toxicity and efficacy endpoints, phase I/II trials can identify a more clinically meaningful dose for subsequent phase II trials than traditional toxicity-based phase I trials in terms of risk-benefit tradeoff. To strengthen and simplify the current practice of phase I/II trials, we propose a utility-based toxicity Probability Interval (uTPI) design for finding the optimal biological dose, based on a numerical utility that provides a clinically meaningful, one-dimensional summary representation of the patient's bivariate toxicity and efficacy outcome. The uTPI design does not rely on any parametric specification of the dose-response relationship, and it directly models the dose desirability through a quasi binomial likelihood. Toxicity Probability Intervals are used to screen out overly toxic dose levels, and then the dose escalation/de-escalation decisions are made adaptively by comparing the posterior desirability distributions of the adjacent levels of the current dose. The uTPI design is flexible in accommodating various dose desirability formulations, while only requiring minimum design parameters. It has a clear decision structure such that a dose-assignment decision table can be calculated before the trial starts and can be used throughout the trial, which simplifies the practical implementation of the design. Extensive simulation studies demonstrate that the proposed uTPI design yields desirable as well as robust performance under various scenarios.
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comparative review of novel model assisted designs for phase i clinical trials
Statistics in Medicine, 2018Co-Authors: Heng Zhou, Thomas A Murray, Haitao Pan, Ying YuanAbstract:A number of novel phase I trial designs have been proposed that aim to combine the simplicity of algorithm-based designs with the superior performance of model-based designs, including the modified toxicity Probability Interval, Bayesian optimal Interval, and Keyboard designs. In this article, we review these "model-assisted" designs, contrast their statistical foundations and pros and cons, and compare their operating characteristics with the continual reassessment method. To provide unbiased and reliable results, our comparison is based on 10 000 dose-toxicity scenarios randomly generated using the pseudo-uniform algorithm recently proposed in the literature. The results showed that the continual reassessment method, Bayesian optimal Interval, and Keyboard designs provide comparable, superior operating characteristics, and each outperforms the modified toxicity Probability Interval design. These designs are more likely to correctly select the maximum tolerated dose and less likely to overdose patients.
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accuracy safety and reliability of novel phase i trial designs
Clinical Cancer Research, 2018Co-Authors: Heng Zhou, Ying YuanAbstract:A number of novel model-based and model-assisted designs have been proposed to find the maximum tolerated dose (MTD) in phase I clinical trials, but their differences and relative pros and cons are not clear to many practitioners. We review three model-based designs, including the continual reassessment method (CRM), dose escalation with overdose control (EWOC), and Bayesian logistic regression model (BLRM), and three model-assisted designs, including the modified toxicity Probability Interval (mTPI), Bayesian optimal Interval (BOIN), and keyboard designs. We conduct numerical studies to assess their accuracy, safety and reliability, and the practical implications of various empirical rules used in some designs, such as skipping a dose and imposing overdose control. Our results show that the CRM outperforms EWOC and BLRM with higher accuracy of identifying the MTD. For the CRM, skipping a dose is not recommended as it substantially increases the chance of overdosing patients, while providing limited gain for identifying the MTD. EWOC and BLRM appear excessively conservative. They are safe, but have relatively poor accuracy of finding the MTD. The BOIN and keyboard designs have similar operating characteristics, outperforming the mTPI, but the BOIN is more intuitive and transparent. The BOIN yields competitive performance comparable to the CRM, but is simpler to implement and free of the issue of irrational dose assignment caused by model misspecification, thereby providing an attractive approach for designing phase I trials.
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keyboard a novel bayesian toxicity Probability Interval design for phase i clinical trials
Clinical Cancer Research, 2017Co-Authors: Fangrong Yan, Sumithra J Mandrekar, Ying YuanAbstract:The primary objective of phase I oncology trials is to find the MTD. The 3+3 design is easy to implement but performs poorly in finding the MTD. A newer design, such as the modified toxicity Probability Interval (mTPI) design, provides better accuracy to identify the MTD but tends to overdose patients. We propose the keyboard design, an intuitive Bayesian design that conducts dose escalation and de-escalation based on whether the strongest key, defined as the dosing Interval that most likely contains the current dose, is below or above the target dosing Interval. The keyboard design can be implemented in a simple way, similar to the traditional 3+3 design, but provides more flexibility for choosing the target toxicity rate and cohort size. Our simulation studies demonstrate that compared with the 3+3 design, the keyboard design has favorable operating characteristics in terms of identifying the MTD. Compared with the mTPI design, the keyboard design is safer, with a substantially lower risk of treating patients at overly toxic doses, and has the better precision to identify the MTD, thereby providing a useful upgrade to the mTPI design. Software freely available at http://www.trialdesign.org facilitates the application of the keyboard design. Clin Cancer Res; 23(15); 3994-4003. ©2017 AACRSee related commentary by Paoletti et al., p. 3977.
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bayesian optimal Interval design a simple and well performing design for phase i oncology trials
Clinical Cancer Research, 2016Co-Authors: Ying Yuan, Kenneth R Hess, Susan G Hilsenbeck, Mark R GilbertAbstract:Despite more than two decades of publications that offer more innovative model-based designs, the classical 3+3 design remains the most dominant phase I trial design in practice. In this article, we introduce a new trial design, the Bayesian optimal Interval (BOIN) design. The BOIN design is easy to implement in a way similar to the 3+3 design, but is more flexible for choosing the target toxicity rate and cohort size and yields a substantially better performance that is comparable to that of more complex model-based designs. The BOIN design contains the 3+3 design and the accelerated titration design as special cases, thus linking it to established phase I approaches. A numerical study shows that the BOIN design generally outperforms the 3+3 design and the modified toxicity Probability Interval (mTPI) design. The BOIN design is more likely than the 3+3 design to correctly select the maximum tolerated dose (MTD) and allocate more patients to the MTD. Compared to the mTPI design, the BOIN design has a substantially lower risk of overdosing patients and generally a higher Probability of correctly selecting the MTD. User-friendly software is freely available to facilitate the application of the BOIN design.
Suejane Wang - One of the best experts on this subject based on the ideXlab platform.
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a bayesian Interval dose finding design addressingockham s razor mtpi 2
Contemporary Clinical Trials, 2017Co-Authors: Wentian Guo, Suejane Wang, Shengjie Yang, Henry LynnAbstract:There has been an increasing interest in using Interval-based Bayesian designs for dose finding, one of which is the modified toxicity Probability Interval (mTPI) method. We show that the decision rules in mTPI correspond to an optimal rule under a formal Bayesian decision theoretic framework. However, the Probability models in mTPI are overly sharpened by the Ockham's razor, which, while in general helps with parsimonious statistical inference, leads to undesirable decisions from safety perspective. We propose a new framework that blunts the Ockham's razor, and demonstrate the superior performance of the new method, called mTPI-2. An online web tool is provided for users who can generate the design, conduct clinical trials, and examine operating characteristics of the designs.
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modified toxicity Probability Interval design a safer and more reliable method than the 3 3 design for practical phase i trials
Journal of Clinical Oncology, 2013Co-Authors: Yuan Ji, Suejane WangAbstract:The 3!3 design is the most common choice among clinicians for phase I dose-escalation oncology trials. In recent reviews, more than 95% of phase I trials have been based on the 3!3 design. Given that it is intuitive and its implementation does not require a computer program, clinicians can conduct 3!3 dose escalations in practice with virtually no logistic cost, and trial protocols based on the 3!3 design pass institutional review board and biostatistics reviews quickly. However, the performance of the 3!3 design has rarely been compared with model-based designs in simulation studies with matched sample sizes. In the vast majority of statistical literature, the 3!3 design has been shown to be inferior in identifying true maximum-tolerated doses (MTDs), although the sample size required by the 3!3 design is often orders-of-magnitude smaller than model-based designs. In this article, through comparative simulation studies with matched sample sizes, we demonstrate that the 3!3 design has higher risks of exposing patients to toxic doses above the MTD than the modified toxicity Probability Interval (mTPI) design, a newly developed adaptive method. In addition, compared with the mTPI design, the 3!3 design does not yield higher probabilities in identifying the correct MTD, even when the sample size is matched. Given that the mTPI design is equally transparent, costless to implement with free software, and more flexible in practical situations, we highly encourage its adoption in early dose-escalation studies whenever the 3!3 design is also considered. We provide free software to allow direct comparisons of the 3!3 design with other model-based designs in simulation studies with matched sample sizes. J Clin Oncol 31:1785-1791. © 2013 by American Society of Clinical Oncology
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modified toxicity Probability Interval design a safer and more reliable method than the 3 3 design for practical phase i trials
Journal of Clinical Oncology, 2013Co-Authors: Suejane WangAbstract:The 3 + 3 design is the most common choice among clinicians for phase I dose-escalation oncology trials. In recent reviews, more than 95% of phase I trials have been based on the 3 + 3 design. Given that it is intuitive and its implementation does not require a computer program, clinicians can conduct 3 + 3 dose escalations in practice with virtually no logistic cost, and trial protocols based on the 3 + 3 design pass institutional review board and biostatistics reviews quickly. However, the performance of the 3 + 3 design has rarely been compared with model-based designs in simulation studies with matched sample sizes. In the vast majority of statistical literature, the 3 + 3 design has been shown to be inferior in identifying true maximum-tolerated doses (MTDs), although the sample size required by the 3 + 3 design is often orders-of-magnitude smaller than model-based designs. In this article, through comparative simulation studies with matched sample sizes, we demonstrate that the 3 + 3 design has higher risks of exposing patients to toxic doses above the MTD than the modified toxicity Probability Interval (mTPI) design, a newly developed adaptive method. In addition, compared with the mTPI design, the 3 + 3 design does not yield higher probabilities in identifying the correct MTD, even when the sample size is matched. Given that the mTPI design is equally transparent, costless to implement with free software, and more flexible in practical situations, we highly encourage its adoption in early dose-escalation studies whenever the 3 + 3 design is also considered. We provide free software to allow direct comparisons of the 3 + 3 design with other model-based designs in simulation studies with matched sample sizes.
Wentian Guo - One of the best experts on this subject based on the ideXlab platform.
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r tpi rolling toxicity Probability Interval design to shorten the duration and maintain safety of phase i trials
Journal of Biopharmaceutical Statistics, 2019Co-Authors: Wentian GuoAbstract:To shorten trial duration and improve safety of Phase I trials, we propose R-TPI, a rolling enrollment design that combines the features in model-based designs such as mTPI-2 and rule-based designs...
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a bayesian Interval dose finding design addressingockham s razor mtpi 2
Contemporary Clinical Trials, 2017Co-Authors: Wentian Guo, Suejane Wang, Shengjie Yang, Henry LynnAbstract:There has been an increasing interest in using Interval-based Bayesian designs for dose finding, one of which is the modified toxicity Probability Interval (mTPI) method. We show that the decision rules in mTPI correspond to an optimal rule under a formal Bayesian decision theoretic framework. However, the Probability models in mTPI are overly sharpened by the Ockham's razor, which, while in general helps with parsimonious statistical inference, leads to undesirable decisions from safety perspective. We propose a new framework that blunts the Ockham's razor, and demonstrate the superior performance of the new method, called mTPI-2. An online web tool is provided for users who can generate the design, conduct clinical trials, and examine operating characteristics of the designs.
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toxicity and efficacy Probability Interval design for phase i adoptive cell therapy dose finding clinical trials
Clinical Cancer Research, 2017Co-Authors: James Boyd Whitmore, Wentian GuoAbstract:Recent trials of adoptive cell therapy (ACT), such as the chimeric antigen receptor (CAR) T-cell therapy, have demonstrated promising therapeutic effects for cancer patients. A main issue in the product development is to determine the appropriate dose of ACT. Traditional phase I trial designs for cytotoxic agents explicitly assume that toxicity increases monotonically with dose levels and implicitly assume the same for efficacy to justify dose escalation. ACT usually induces rapid responses, and the monotonic dose-response assumption is unlikely to hold due to its immunobiologic activities. We propose a toxicity and efficacy Probability Interval (TEPI) design for dose finding in ACT trials. This approach incorporates efficacy outcomes to inform dosing decisions to optimize efficacy and safety simultaneously. Rather than finding the maximum tolerated dose (MTD), the TEPI design is aimed at finding the dose with the most desirable outcome for safety and efficacy. The key features of TEPI are its simplicity, flexibility, and transparency, because all decision rules can be prespecified prior to trial initiation. We conduct simulation studies to investigate the operating characteristics of the TEPI design and compare it to existing methods. In summary, the TEPI design is a novel method for ACT dose finding, which possesses superior performance and is easy to use, simple, and transparent. Clin Cancer Res; 23(1); 13-20. ©2016 AACR.
Yuan Ji - One of the best experts on this subject based on the ideXlab platform.
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PoD-TPI: Probability-of-Decision Toxicity Probability Interval Design to Accelerate Phase I Trials
Statistics in Biosciences, 2019Co-Authors: Tianjian Zhou, Yuan JiAbstract:Cohort-based enrollment can slow down dose-finding trials since the outcomes of the previous cohort must be fully evaluated before the next cohort can be enrolled. This results in frequent suspension of patient enrollment. The issue is exacerbated in recent immune oncology trials where toxicity outcomes can take a long time to observe. We propose a novel phase I design, the Probability-of-decision toxicity Probability Interval (PoD-TPI) design, to accelerate phase I trials. PoD-TPI enables dose assignment in real time in the presence of pending toxicity outcomes. With uncertain outcomes, the dose assignment decisions are treated as a random variable, and we calculate the posterior distribution of the decisions. The posterior distribution reflects the variability in the pending outcomes and allows a direct and intuitive evaluation of the confidence of all possible decisions. Optimal decisions are calculated based on 0-1 loss, and extra safety rules are constructed to enforce sufficient protection from exposing patients to risky doses. A new and useful feature of PoD-TPI is that it allows investigators and regulators to balance the trade-off between enrollment speed and making risky decisions by tuning a pair of intuitive design parameters. Through numerical studies, we evaluate the operating characteristics of PoD-TPI and demonstrate that PoD-TPI shortens trial duration and maintains trial safety and efficiency compared to existing time-to-event designs.
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modified toxicity Probability Interval design a safer and more reliable method than the 3 3 design for practical phase i trials
Journal of Clinical Oncology, 2013Co-Authors: Yuan Ji, Suejane WangAbstract:The 3!3 design is the most common choice among clinicians for phase I dose-escalation oncology trials. In recent reviews, more than 95% of phase I trials have been based on the 3!3 design. Given that it is intuitive and its implementation does not require a computer program, clinicians can conduct 3!3 dose escalations in practice with virtually no logistic cost, and trial protocols based on the 3!3 design pass institutional review board and biostatistics reviews quickly. However, the performance of the 3!3 design has rarely been compared with model-based designs in simulation studies with matched sample sizes. In the vast majority of statistical literature, the 3!3 design has been shown to be inferior in identifying true maximum-tolerated doses (MTDs), although the sample size required by the 3!3 design is often orders-of-magnitude smaller than model-based designs. In this article, through comparative simulation studies with matched sample sizes, we demonstrate that the 3!3 design has higher risks of exposing patients to toxic doses above the MTD than the modified toxicity Probability Interval (mTPI) design, a newly developed adaptive method. In addition, compared with the mTPI design, the 3!3 design does not yield higher probabilities in identifying the correct MTD, even when the sample size is matched. Given that the mTPI design is equally transparent, costless to implement with free software, and more flexible in practical situations, we highly encourage its adoption in early dose-escalation studies whenever the 3!3 design is also considered. We provide free software to allow direct comparisons of the 3!3 design with other model-based designs in simulation studies with matched sample sizes. J Clin Oncol 31:1785-1791. © 2013 by American Society of Clinical Oncology
Ruitao Lin - One of the best experts on this subject based on the ideXlab platform.
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utpi a utility based toxicity Probability Interval design for phase i ii dose finding trials
Statistics in Medicine, 2021Co-Authors: Haolun Shi, Ying Yuan, Jiguo Cao, Ruitao LinAbstract:Unlike chemotherapy, the maximum tolerated dose (MTD) of molecularly targeted agents and immunotherapy may not pose significant clinical benefit over the lower doses. By simultaneously considering both toxicity and efficacy endpoints, phase I/II trials can identify a more clinically meaningful dose for subsequent phase II trials than traditional toxicity-based phase I trials in terms of risk-benefit tradeoff. To strengthen and simplify the current practice of phase I/II trials, we propose a utility-based toxicity Probability Interval (uTPI) design for finding the optimal biological dose, based on a numerical utility that provides a clinically meaningful, one-dimensional summary representation of the patient's bivariate toxicity and efficacy outcome. The uTPI design does not rely on any parametric specification of the dose-response relationship, and it directly models the dose desirability through a quasi binomial likelihood. Toxicity Probability Intervals are used to screen out overly toxic dose levels, and then the dose escalation/de-escalation decisions are made adaptively by comparing the posterior desirability distributions of the adjacent levels of the current dose. The uTPI design is flexible in accommodating various dose desirability formulations, while only requiring minimum design parameters. It has a clear decision structure such that a dose-assignment decision table can be calculated before the trial starts and can be used throughout the trial, which simplifies the practical implementation of the design. Extensive simulation studies demonstrate that the proposed uTPI design yields desirable as well as robust performance under various scenarios.
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Bayesian optimal Interval design for dose finding in drug-combination trials:
Statistical methods in medical research, 2015Co-Authors: Ruitao Lin, Guosheng YinAbstract:Interval designs have recently attracted enormous attention due to their simplicity and desirable properties. We develop a Bayesian optimal Interval design for dose finding in drug-combination trials. To determine the next dose combination based on the cumulative data, we propose an allocation rule by maximizing the posterior Probability that the toxicity rate of the next dose falls inside a prespecified Probability Interval. The entire dose-finding procedure is nonparametric (model-free), which is thus robust and also does not require the typical "nonparametric" prephase used in model-based designs for drug-combination trials. The proposed two-dimensional Interval design enjoys convergence properties for large samples. We conduct simulation studies to demonstrate the finite-sample performance of the proposed method under various scenarios and further make a modication to estimate toxicity contours by parallel dose-finding paths. Simulation results show that on average the performance of the proposed design is comparable with model-based designs, but it is much easier to implement.