A robust two-stage design identifying the optimal biological dose for phase I/II clinical trials
15 Jan 2017-
TL;DR: A robust two-stage design to identify the optimal biological dose for phase I/II clinical trials evaluating both toxicity and efficacy outcomes is proposed and has desirable operating characteristics across different shapes of the underlying true dose-toxicity and dose-efficacy curves.
Abstract: We propose a robust two-stage design to identify the optimal biological dose for phase I/II clinical trials evaluating both toxicity and efficacy outcomes. In the first stage of dose finding, we use the Bayesian model averaging continual reassessment method to monitor the toxicity outcomes and adopt an isotonic regression method based on the efficacy outcomes to guide dose escalation. When the first stage ends, we use the Dirichlet-multinomial distribution to jointly model the toxicity and efficacy outcomes and pick the candidate doses based on a three-dimensional volume ratio. The selected candidate doses are then seamlessly advanced to the second stage for dose validation. Both toxicity and efficacy outcomes are continuously monitored so that any overly toxic and/or less efficacious dose can be dropped from the study as the trial continues. When the phase I/II trial ends, we select the optimal biological dose as the dose obtaining the minimal value of the volume ratio within the candidate set. An advantage of the proposed design is that it does not impose a monotonically increasing assumption on the shape of the dose-efficacy curve. We conduct extensive simulation studies to examine the operating characteristics of the proposed design. The simulation results show that the proposed design has desirable operating characteristics across different shapes of the underlying true dose-toxicity and dose-efficacy curves. The software to implement the proposed design is available upon request. Copyright © 2016 John Wiley & Sons, Ltd.
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TL;DR: This paper uses the continuation-ratio (CR) model to characterize the trinomial response outcomes and the cause-specific hazard rate method to model the competing-risk survival outcomes and develops a Bayesian data augmentation method to impute the missing data from the observations.
Abstract: Early-phase dose-finding clinical trials are often subject to the issue of late-onset outcomes. In phase I/II clinical trials, the issue becomes more intractable because toxicity and efficacy can be competing risk outcomes such that the occurrence of the first outcome will terminate the other one. In this paper, we propose a novel Bayesian adaptive phase I/II clinical trial design to address the issue of late-onset competing risk outcomes. We use the continuation-ratio model to characterize the trinomial response outcomes and the cause-specific hazard rate method to model the competing-risk survival outcomes. We treat the late-onset outcomes as missing data and develop a Bayesian data augmentation method to impute the missing data from the observations. We also propose an adaptive dose-finding algorithm to allocate patients and identify the optimal biological dose during the trial. Simulation studies show that the proposed design yields desirable operating characteristics.
15 citations
TL;DR: The model‐assisted TITE‐BOIN12 design is proposed to find OBD with late‐onset toxicity and efficacy, and two approaches are considered, Bayesian data augmentation and an approximated likelihood method, to enable real‐time decision making when some patients'oxicity and efficacy outcomes are pending.
Abstract: In the era of immunotherapies and targeted therapies, the focus of early phase clinical trials has shifted from finding the maximum tolerated dose to identifying the optimal biological dose (OBD), which maximizes the toxicity‐efficacy trade‐off. One major impediment to using adaptive designs to find OBD is that efficacy or/and toxicity are often late‐onset, hampering the designs' real‐time decision rules for treating new patients. To address this issue, we propose the model‐assisted TITE‐BOIN12 design to find OBD with late‐onset toxicity and efficacy. As an extension of the BOIN12 design, the TITE‐BOIN12 design also uses utility to quantify the toxicity‐efficacy trade‐off. We consider two approaches, Bayesian data augmentation and an approximated likelihood method, to enable real‐time decision making when some patients' toxicity and efficacy outcomes are pending. Extensive simulations show that, compared to some existing designs, TITE‐BOIN12 significantly shortens the trial duration while having comparable or higher accuracy to identify OBD and a lower risk of overdosing patients. To facilitate the use of the TITE‐BOIN12 design, we develop a user‐friendly software freely available at http://www.trialdesign.org.
13 citations
TL;DR: Characteristics of recent and ongoing trials testing immuno-oncology drugs with unconventional design are reviewed, and trends and critical aspects are highlighted.
Abstract: The rapid rise to fame of immuno-oncology (IO) drugs has generated unprecedented interest in the industry, patients and doctors, and has had a major impact in the treatment of most cancers. An interesting aspect in the clinical development of many IO agents is the increasing reliance on nonconventional trial design, including the so-called 'master protocols' that incorporate various adaptive features and often heavily rely on biomarkers to select patient populations most likely to benefit. These novel designs promise to maximize the clinical benefit that can be reaped from clinical research, but are not without costs. Their acceptance as solid evidence basis for use outside of the research context requires profound cultural changes by multiple stakeholders, including regulatory bodies, decision-makers, statisticians, researchers, doctors and, most importantly, patients. Here we review characteristics of recent and ongoing trials testing IO drugs with unconventional design, and we highlight trends and critical aspects.
12 citations
TL;DR: Two model-assisted designs are proposed: the toxicity and efficacy probability interval-2 (TEPI-2) design and the utility-based interval (UBI) design that incorporate theoxicity and efficacy outcomes simultaneously and identify a dose that has high probability of acceptable efficacy with manageable toxicity.
Abstract: Conventional dose finding designs in oncology drug development target on the identification of the maximum tolerated dose (MTD), with the assumption that the MTD has the most potential of clinical activity among those identified tolerable dose levels. However, immuno-oncology (I-O) and cell therapy area, may lack dose-efficacy monotonicity, posing significant challenges in the statistical designs for dose finding trials. A desirable design should empower the trial to identify the right dose level with tolerable toxicity and acceptable efficacy. Such dose is called as optimal biological dose (OBD), which is more appropriate to be considered as the primary objective of the first-in-human trial in I-O and cell therapy than MTD. We propose two model-assisted designs in this setting: the toxicity and efficacy probability interval-2 (TEPI-2) design and the utility-based interval (UBI) design that incorporate the toxicity and efficacy outcomes simultaneously and identify a dose that has high probability of acceptable efficacy with manageable toxicity. The proposed designs can generate decision tables before trial starts to facilitate practical and easy-to-implement applications. Through simulation studies, our proposed novel designs demonstrate superior performance in accuracy, efficiency, and safety. Additionally, they can reduce the number of patients and shorten clinical development timeline. We also illustrate the advantages of proposed methods by redesigning a CAR T-cell therapy phase I clinical trial for multiple myeloma and summarize our recommendations in the discussion section.
11 citations
TL;DR: The generalized Bayesian optimal interval design for dose‐finding accounting for efficacy and toxicity grades is proposed, named “gBOIN‐ET” design, which is model‐assisted, simple, and straightforward to implement in actual oncology dose-finding trials than model‐based approaches.
Abstract: One of the primary objectives of an oncology dose‐finding trial for novel therapies, such as molecular targeted agents and immune‐oncology therapies, is to identify an optimal dose (OD) that is tolerable and therapeutically beneficial for subjects in subsequent clinical trials. These new therapeutic agents appear more likely to induce multiple low‐ or moderate‐grade toxicities than dose‐limiting toxicities. Besides, efficacy should be evaluated as an overall response and stable disease in solid tumors and the difference between complete remission and partial remission in lymphoma. This paper proposes the generalized Bayesian optimal interval design for dose‐finding accounting for efficacy and toxicity grades. The new design, named “gBOIN‐ET” design, is model‐assisted, simple, and straightforward to implement in actual oncology dose‐finding trials than model‐based approaches. These characteristics are quite valuable in practice. A simulation study shows that the gBOIN‐ET design has advantages compared with the other model‐assisted designs in the percentage of correct OD selection and the average number of patients allocated to the ODs across various realistic settings.
8 citations
References
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TL;DR: A new approach to the design and analysis of Phase 1 clinical trials in cancer and a particularly simple model is looked at that enables the use of models whose only requirements are that locally they reasonably well approximate the true probability of toxic response.
Abstract: This paper looks at a new approach to the design and analysis of Phase 1 clinical trials in cancer. The basic idea and motivation behind the approach stem from an attempt to reconcile the needs of dose-finding experimentation with the ethical demands of established medical practice. It is argued that for these trials the particular shape of the dose toxicity curve is of little interest. Attention focuses rather on identifying a dose with a given targeted toxicity level and on concentrating experimentation at that which all current available evidence indicates to be the best estimate of this level. Such an approach not only makes an explicit attempt to meet ethical requirements but also enables the use of models whose only requirements are that locally (i.e., around the dose corresponding to the targeted toxicity level) they reasonably well approximate the true probability of toxic response. Although a large number of models could be contemplated, we look at a particularly simple one. Extensive simulations show the model to have real promise.
1,402 citations
TL;DR: In Monte Carlo simulations, two two-stage designs are found to provide reduced bias in maximum likelihood estimation of the MTD in less than ideal dose-response settings and several designs to be nearly as conservative as the standard design in terms of the proportion of patients entered at higher dose levels.
Abstract: The Phase I clinical trial is a study intended to estimate the so-called maximum tolerable dose (MTD) of a new drug. Although there exists more or less a standard type of design for such trials, its development has been largely ad hoc. As usually implemented, the trial design has no intrinsic property that provides a generally satisfactory basis for estimation of the MTD. In this paper, the standard design and several simple alternatives are compared with regard to the conservativeness of the design and with regard to point and interval estimation of an MTD (33rd percentile) with small sample sizes. Using a Markov chain representation, we found several designs to be nearly as conservative as the standard design in terms of the proportion of patients entered at higher dose levels. In Monte Carlo simulations, two two-stage designs are found to provide reduced bias in maximum likelihood estimation of the MTD in less than ideal dose-response settings. Of the three methods considered for determining confidence intervals--the delta method, a method based on Fieller's theorem, and a likelihood ratio method--none was able to provide both usefully narrow intervals and coverage probabilities close to nominal.
816 citations
"A robust two-stage design identifyi..." refers background in this paper
...Traditional dose-finding designs for cytotoxic agents, including the commonly used 3+3 design [7] and continual reassessment method (CRM) [8], assume that both efficacy and toxicity outcomes increase monotonically with the dose....
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TL;DR: Dose escalation methods for phase I trials are reviewed, including the rule-based and model-based dose escalation methods that have been developed to evaluate new anticancer agents and specific methods for drug combinations as well as methods that use a time-to-event endpoint or both toxicity and efficacy as endpoints.
Abstract: Phase I clinical trials are an essential step in the development of anticancer drugs. The main goal of these studies is to establish the recommended dose and/or schedule of new drugs or drug combinations for phase II trials. The guiding principle for dose escalation in phase I trials is to avoid exposing too many patients to subtherapeutic doses while preserving safety and maintaining rapid accrual. Here we review dose escalation methods for phase I trials, including the rule-based and model-based dose escalation methods that have been developed to evaluate new anticancer agents. Toxicity has traditionally been the primary endpoint for phase I trials involving cytotoxic agents. However, with the emergence of molecularly targeted anticancer agents, potential alternative endpoints to delineate optimal biological activity, such as plasma drug concentration and target inhibition in tumor or surrogate tissues, have been proposed along with new trial designs. We also describe specific methods for drug combinations as well as methods that use a time-to-event endpoint or both toxicity and efficacy as endpoints. Finally, we present the advantages and drawbacks of the various dose escalation methods and discuss specific applications of the methods in developmental oncotherapeutics.
761 citations
"A robust two-stage design identifyi..." refers background in this paper
...As a result, the toxicity of MTAs can be minimal within the therapeutic dose range, and the dose-efficacy curves of MTAs may not follow monotonic patterns [9, 10, 11, 12]....
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TL;DR: Computer simulations show that the new adaptive Bayesian method for dose‐finding in phase I/II clinical trials based on trade‐offs between the probabilities of treatment efficacy and toxicity has high probabilities of making correct decisions and treats most patients at doses with desirable efficacy–toxicity trade-offs.
Abstract: We present an adaptive Bayesian method for dose-finding in phase I/II clinical trials based on trade-offs between the probabilities of treatment efficacy and toxicity. The method accommodates either trinary or bivariate binary outcomes, as well as efficacy probabilities that possibly are nonmonotone in dose. Doses are selected for successive patient cohorts based on a set of efficacy-toxicity trade-off contours that partition the two-dimensional outcome probability domain. Priors are established by solving for hyperparameters that optimize the fit of the model to elicited mean outcome probabilities. For trinary outcomes, the new algorithm is compared to the method of Thall and Russell (1998, Biometrics 54, 251-264) by application to a trial of rapid treatment for ischemic stroke. The bivariate binary outcome case is illustrated by a trial of graft-versus-host disease treatment in allogeneic bone marrow transplantation. Computer simulations show that, under a wide rage of dose-outcome scenarios, the new method has high probabilities of making correct decisions and treats most patients at doses with desirable efficacy-toxicity trade-offs.
420 citations
TL;DR: A family of statistical models is presented for bivariate, discrete response to a regressor when both components of the response have ordered categories.
Abstract: SUMMARY A family of statistical models is presented for bivariate, discrete response to a regressor when both components of the response have ordered categories. Association between components is expressed in terms of global cross-ratios, cross-product ratios of quadrant probabilities, for each double dichotomy of the response table of probabilities into quadrants (Pearson and Heron, 1913, Biometrika 9, 159-315). These models are extensions to the work of Plackett (1965, Journal of the American Statistical Association 60, 516-522) and Mantel and Brown (1973, Biometrics 29, 649-665). The marginal cumulative probabilities may satisfy linear logistic or other generalized linear models (McCullagh, 1980, Journal of the Royal Statistical Society, Series B 42, 109-142). An analysis of patients' postoperative pain level and medication frequency illustrates these methods.
292 citations
"A robust two-stage design identifyi..." refers methods in this paper
...We used the global cross-ratio model [27] to simulate the association between the toxicity and efficacy outcomes with the ratio fixed at 1....
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