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      2D (2 Dimensional) TEQR design for Determining the optimal Dose for safety and efficacy

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          Abstract

          Designs, such as the Eff-Tox, OBD (optimal biological dose), STEIN (simple efficacy toxicity interval), and TEPI (toxicity efficacy probability interval) designs, have been proposed to determine the optimal dose of a new oncology drug using both efficacy and toxicity. The goal of these designs is to select the optimal drug dose for further phase trials more accurately than dose finding designs that only consider toxicity, such as the 3 + 3, TEQR (toxicity equivalence range), mTPI (modified toxicity probability interval), and EWOC (escalation with overdose control) designs. We propose a new frequentist design for optimal dose selection, the 2D TEQR design, that is easier to understand and simpler to implement than the TEPI, Eff-Tox, STEIN and OBD designs, as it is based on the empirical or observed toxicity and efficacy rates and does not require specialized computations. We compare the performance of this new design with those of the TEPI, STEIN, Eff-Tox and OBD Isotonic designs. Although for the same sample size and cohort size, the frequentist 2D TEQR design is less accurate than the Bayesian TEPI design and also the STEIN design in selecting the optimal dose, the accuracy of optimal dose selection of the 2D TEQR design can be increased, in many cases, with a moderate increase in cohort size. The 2D TEQR design is as accurate as or more accurate than the Eff-Tox design in optimal dose selection, and better than the OBD Isotonic design, unless there is a clear peak in the true response rates, in which case the OBD Isotonic design performs better than the other designs.

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          Most cited references24

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          Dose-finding based on efficacy-toxicity trade-offs.

          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.
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            A modified toxicity probability interval method for dose-finding trials.

            Building on earlier work, the toxicity probability interval (TPI) method, we present a modified TPI (mTPI) design that is calibration-free for phase I trials. Our goal is to improve the trial conduct and provide more effective designs while maintaining the simplicity of the original TPI design. Like the TPI method, the mTPI consists of a practical dose-finding scheme guided by the posterior inference for a simple Bayesian model. However, the new method proposes improved dose-finding decision rules based on a new statistic, the unit probability mass (UPM). For a given interval and a probability distribution, the UPM is defined as the ratio of the probability mass of the interval to the length of the interval. The improvement through the use of the UPM for dose finding is threefold: (1) the mTPI method appears to be safer than the TPI method in that it puts fewer patients on toxic doses; (2) the mTPI method eliminates the need for calibrating two key parameters, which is required in the TPI method and is a known difficult issue; and (3) the mTPI method corresponds to the Bayes rule under a decision theoretic framework and possesses additional desirable large- and small-sample properties. The proposed method is applicable to dose-finding trials with a binary toxicity endpoint. The new method mTPI is essentially calibration free and exhibits improved performance over the TPI method. These features make the mTPI a desirable choice for the design of practical trials.
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              Bayesian dose-finding in phase I/II clinical trials using toxicity and efficacy odds ratios.

              A Bayesian adaptive design is proposed for dose-finding in phase I/II clinical trials to incorporate the bivariate outcomes, toxicity and efficacy, of a new treatment. Without specifying any parametric functional form for the drug dose-response curve, we jointly model the bivariate binary data to account for the correlation between toxicity and efficacy. After observing all the responses of each cohort of patients, the dosage for the next cohort is escalated, deescalated, or unchanged according to the proposed odds ratio criteria constructed from the posterior toxicity and efficacy probabilities. A novel class of prior distributions is proposed through logit transformations which implicitly imposes a monotonic constraint on dose toxicity probabilities and correlates the probabilities of the bivariate outcomes. We conduct simulation studies to evaluate the operating characteristics of the proposed method. Under various scenarios, the new Bayesian design based on the toxicity-efficacy odds ratio trade-offs exhibits good properties and treats most patients at the desirable dose levels. The method is illustrated with a real trial design for a breast medical oncology study.
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                Author and article information

                Contributors
                Journal
                Contemp Clin Trials Commun
                Contemp Clin Trials Commun
                Contemporary Clinical Trials Communications
                Elsevier
                2451-8654
                12 October 2019
                December 2019
                12 October 2019
                : 16
                : 100461
                Affiliations
                [a ]Celgene, 300 Connell Drive, Berkeley Heights, NJ, 07922, USA
                [b ]New London, CT, 06320, USA
                [c ]Juno Therapeutics, A Celgene Company, Seattle, WA, 98109, USA
                [d ]Boston University, School of Public Health, Boston, MA, 02118, USA
                Author notes
                Article
                S2451-8654(19)30223-6 100461
                10.1016/j.conctc.2019.100461
                6881644
                06657b65-79a0-4ee2-986b-e8a5632838f9
                © 2019 The Author(s)

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 27 April 2019
                : 25 September 2019
                : 9 October 2019
                Categories
                Article

                early phase oncology design,2d teqr design,obd isotonic and eff-tox designs,optimal dose for safety and efficacy

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