Home  About us  Contact
 

Adaptive Design (adaptive + design)

Distribution by Scientific Domains
Mathematics and Statistics100%

Terms modified by Adaptive Design

  • adaptive design methods
    		•

    Selected Abstracts

    Response Adaptive Designs with a Variance-penalized Criterion

    BIOMETRICAL JOURNAL, Issue 5 2009
    Yanqing Yi
    Abstract We consider a response adaptive design of clinical trials with a variance-penalized criterion. It is shown that this criterion evaluates the performance of a response adaptive design based on both the number of patients assigned to the better treatment and the power of the statistical test. A new proportion of treatment allocation is proposed and the doubly biased coin procedure is used to target the proposed proportion. Under reasonable assumptions, the proposed design is demonstrated to generate an asymptotic variance of allocation proportions, which is smaller than that of the drop-the-loser design. Simulation comparisons of the proposed design with some existing designs are presented. [source]

    Editorial , Adaptive Designs: Expectations are High

    BIOMETRICAL JOURNAL, Issue 4 2006
    Joachim Röhmel
    No abstract is available for this article. [source]

    Challenges in Implementing Adaptive Designs: Comments on the Viewpoints Expressed by Regulatory Statisticians

    BIOMETRICAL JOURNAL, Issue 4 2006
    Paul Gallo
    Abstract This is a discussion of the following three papers appearing in this special issue on adaptive designs: ,FDA's critical path initiative: A perspective on contributions of biostatistics' by Robert T. O'Neill; ,A regulatory view on adaptive/flexible clinical trial design' by H. M. James Hung, Robert T. O'Neill, Sue-Jane Wang and John Lawrence; and ,Confirmatory clinical trials with an adaptive design' by Armin Koch. (© 2006 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim) [source]

    A Comparison of Procedures for Adaptive Choice of Location Tests in Flexible Two-Stage Designs

    BIOMETRICAL JOURNAL, Issue 3 2003
    Tim Friede
    Abstract Although linear rank statistics for the two-sample problem are distribution free tests, their power depends on the distribution of the data. In the planning phase of an experiment, researchers are often uncertain about the shape of this distribution and so the choice of test statistic for the analysis and the determination of the required sample size are based on vague information. Adaptive designs with interim analysis can potentially overcome both problems. And in particular, adaptive tests based on a selector statistic are a solution to the first. We investigate whether adaptive tests can be usefully implemented in flexible two-stage designs to gain power. In a simulation study, we compare several methods for choosing a test statistic for the second stage of an adaptive design based on interim data with the procedure that applies adaptive tests in both stages. We find that the latter is a sensible approach that leads to the best results in most situations considered here. The different methods are illustrated using a clinical trial example. [source]

    An optimal adaptive design to address local regulations in global clinical trials,

    PHARMACEUTICAL STATISTICS: THE JOURNAL OF APPLIED STATISTICS IN THE PHARMACEUTICAL INDUSTRY, Issue 3 2010
    Xiaolong Luo
    Abstract After multi-regional clinical trials (MRCTs) have demonstrated overall significant effects, evaluation for a region-specific effect is often important. Recent guidance (see, e.g. 1) from regulatory authorities regarding evaluation for possible country-specific effects has led to research on statistical designs that incorporate such evaluations in MRCTs. These statistical designs are intended to use the MRCTs to address the requirements for global registration of a medicinal product. Adding a regional requirement could change the probability for declaring positive effect for the region when there is indeed no treatment difference as well as when there is in fact a true difference within the region. In this paper, we first quantify those probability structures based on the guidance issued by the Ministry of Health, Labour and Welfare (MHLW) of Japan. An adaptive design is proposed to consider those probabilities and to optimize the efficiency for regional objectives. This two-stage approach incorporates comprehensive global objectives into an integrated study design and may mitigate the need for a separate local bridging study. A procedure is used to optimize region-specific enrollment based on an objective function. The overall sample size requirement is assessed. We will use simulation analyses to illustrate the performance of the proposed study design. Copyright © 2010 John Wiley & Sons, Ltd. [source]

    Response Adaptive Designs with a Variance-penalized Criterion

    BIOMETRICAL JOURNAL, Issue 5 2009
    Yanqing Yi
    Abstract We consider a response adaptive design of clinical trials with a variance-penalized criterion. It is shown that this criterion evaluates the performance of a response adaptive design based on both the number of patients assigned to the better treatment and the power of the statistical test. A new proportion of treatment allocation is proposed and the doubly biased coin procedure is used to target the proposed proportion. Under reasonable assumptions, the proposed design is demonstrated to generate an asymptotic variance of allocation proportions, which is smaller than that of the drop-the-loser design. Simulation comparisons of the proposed design with some existing designs are presented. [source]

    An Adaptive Two-stage Design with Treatment Selection Using the Conditional Error Function Approach

    BIOMETRICAL JOURNAL, Issue 4 2006
    Jixian Wang
    Abstract As an approach to combining the phase II dose finding trial and phase III pivotal trials, we propose a two-stage adaptive design that selects the best among several treatments in the first stage and tests significance of the selected treatment in the second stage. The approach controls the type I error defined as the probability of selecting a treatment and claiming its significance when the selected treatment is indifferent from placebo, as considered in Bischoff and Miller (2005). Our approach uses the conditional error function and allows determining the conditional type I error function for the second stage based on information observed at the first stage in a similar way to that for an ordinary adaptive design without treatment selection. We examine properties such as expected sample size and stage-2 power of this design with a given type I error and a maximum stage-2 sample size under different hypothesis configurations. We also propose a method to find the optimal conditional error function of a simple parametric form to improve the performance of the design and have derived optimal designs under some hypothesis configurations. Application of this approach is illustrated by a hypothetical example. (© 2006 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim) [source]

    A Comparison of Procedures for Adaptive Choice of Location Tests in Flexible Two-Stage Designs

    BIOMETRICAL JOURNAL, Issue 3 2003
    Tim Friede
    Abstract Although linear rank statistics for the two-sample problem are distribution free tests, their power depends on the distribution of the data. In the planning phase of an experiment, researchers are often uncertain about the shape of this distribution and so the choice of test statistic for the analysis and the determination of the required sample size are based on vague information. Adaptive designs with interim analysis can potentially overcome both problems. And in particular, adaptive tests based on a selector statistic are a solution to the first. We investigate whether adaptive tests can be usefully implemented in flexible two-stage designs to gain power. In a simulation study, we compare several methods for choosing a test statistic for the second stage of an adaptive design based on interim data with the procedure that applies adaptive tests in both stages. We find that the latter is a sensible approach that leads to the best results in most situations considered here. The different methods are illustrated using a clinical trial example. [source]

    A Latent Contingency Table Approach to Dose Finding for Combinations of Two Agents

    BIOMETRICS, Issue 3 2009
    Guosheng Yin
    Summary Two-agent combination trials have recently attracted enormous attention in oncology research. There are several strong motivations for combining different agents in a treatment: to induce the synergistic treatment effect, to increase the dose intensity with nonoverlapping toxicities, and to target different tumor cell susceptibilities. To accommodate this growing trend in clinical trials, we propose a Bayesian adaptive design for dose finding based on latent 2 × 2 tables. In the search for the maximum tolerated dose combination, we continuously update the posterior estimates for the unknown parameters associated with marginal probabilities and the correlation parameter based on the data from successive patients. By reordering the dose toxicity probabilities in the two-dimensional space, we assign each coming cohort of patients to the most appropriate dose combination. We conduct extensive simulation studies to examine the operating characteristics of the proposed method under various practical scenarios. Finally, we illustrate our dose-finding procedure with a clinical trial of agent combinations at M. D. Anderson Cancer Center. [source]

    Challenges in Implementing Adaptive Designs: Comments on the Viewpoints Expressed by Regulatory Statisticians

    BIOMETRICAL JOURNAL, Issue 4 2006
    Paul Gallo
    Abstract This is a discussion of the following three papers appearing in this special issue on adaptive designs: ,FDA's critical path initiative: A perspective on contributions of biostatistics' by Robert T. O'Neill; ,A regulatory view on adaptive/flexible clinical trial design' by H. M. James Hung, Robert T. O'Neill, Sue-Jane Wang and John Lawrence; and ,Confirmatory clinical trials with an adaptive design' by Armin Koch. (© 2006 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim) [source]

    Opening the Adaptive Toolbox

    BIOMETRICAL JOURNAL, Issue 4 2006
    Janet Wittes
    Abstract This is a discussion of the following three papers appearing in this special issue on adaptive designs: ,A regulatory view on adaptive/flexible clinical trial design' by H. M. James Hung, Robert T. O'Neill, Sue-Jane Wang and John Lawrence; ,Confirmatory clinical trials with an adaptive design' by Armin Koch; and ,FDA's critical path initiative: A perspective on contributions of biostatistics' by Robert T. O'Neill. (© 2006 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim) [source]

    A Consultant's Perspective on the Regulatory Hurdles to Adaptive Trials

    BIOMETRICAL JOURNAL, Issue 4 2006
    Cyrus R. Mehta
    Abstract This is a discussion of the following two papers appearing in this special issue on adaptive designs: ,A regulatory view on adaptive/flexible clinical trial design' by H. M. James Hung, Robert T. O'Neill, Sue-Jane Wang and John Lawrence and ,Confirmatory clinical trials with an adaptive design' by Armin Koch. (© 2006 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim) [source]

    Challenges in Implementing Adaptive Designs: Comments on the Viewpoints Expressed by Regulatory Statisticians

    BIOMETRICAL JOURNAL, Issue 4 2006
    Paul Gallo
    Abstract This is a discussion of the following three papers appearing in this special issue on adaptive designs: ,FDA's critical path initiative: A perspective on contributions of biostatistics' by Robert T. O'Neill; ,A regulatory view on adaptive/flexible clinical trial design' by H. M. James Hung, Robert T. O'Neill, Sue-Jane Wang and John Lawrence; and ,Confirmatory clinical trials with an adaptive design' by Armin Koch. (© 2006 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim) [source]

    Opening the Adaptive Toolbox

    BIOMETRICAL JOURNAL, Issue 4 2006
    Janet Wittes
    Abstract This is a discussion of the following three papers appearing in this special issue on adaptive designs: ,A regulatory view on adaptive/flexible clinical trial design' by H. M. James Hung, Robert T. O'Neill, Sue-Jane Wang and John Lawrence; ,Confirmatory clinical trials with an adaptive design' by Armin Koch; and ,FDA's critical path initiative: A perspective on contributions of biostatistics' by Robert T. O'Neill. (© 2006 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim) [source]

    A Consultant's Perspective on the Regulatory Hurdles to Adaptive Trials

    BIOMETRICAL JOURNAL, Issue 4 2006
    Cyrus R. Mehta
    Abstract This is a discussion of the following two papers appearing in this special issue on adaptive designs: ,A regulatory view on adaptive/flexible clinical trial design' by H. M. James Hung, Robert T. O'Neill, Sue-Jane Wang and John Lawrence and ,Confirmatory clinical trials with an adaptive design' by Armin Koch. (© 2006 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim) [source]

    Combining Treatment Selection and Definitive Testing

    BIOMETRICAL JOURNAL, Issue 4 2006
    Michael A. Proschan
    Abstract This is a discussion of the following two papers appearing in this special issue on adaptive designs: ,An adaptive hierarchical test procedure for selecting safe and efficient treatments' by Franz König, Peter Bauer and Werner Brannath, and ,An adaptive two-stage design with treatment selection using the conditional error function approach' by Jixian Wang. (© 2006 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim) [source]

    Sample Size Reassessment in Adaptive Clinical Trials Using a Bias Corrected Estimate

    BIOMETRICAL JOURNAL, Issue 7 2003
    Silke Coburger
    Abstract Point estimation in group sequential and adaptive trials is an important issue in analysing a clinical trial. Most literature in this area is only concerned with estimation after completion of a trial. Since adaptive designs allow reassessment of sample size during the trial, reliable point estimation of the true effect when continuing the trial is additionally needed. We present a bias adjusted estimator which allows a more exact sample size determination based on the conditional power principle than the naive sample mean does. [source]