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Rahul Bhattacharya – One of the best experts on this subject based on the ideXlab platform.

  • A response Adaptive Design for ordinal categorical responses weighing the cumulative odds ratios
    Biostatistics & Epidemiology, 2019
    Co-Authors: Atanu Biswas, Rahul Bhattacharya, Soumyadeep Das

    Abstract:

    ABSTRACTWeighing the cumulative odds ratios suitably, a two treatment response Adaptive Design for phase III clinical trial is proposed for ordinal categorical responses. Properties of the proposed…

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  • A response Adaptive Design for ordinal categorical responses.
    Journal of biopharmaceutical statistics, 2018
    Co-Authors: Atanu Biswas, Rahul Bhattacharya, Soumyadeep Das

    Abstract:

    A two treatment response Adaptive Design is developed for phase III clinical trials with ordinal categorical treatment outcome using Goodman-Kruskal measure of association. Properties of the proposed Design are studied both empirically and theoretically and the acceptability is further illustrated using two real data-sets; one from a clinical trial with trauma patients and the other from a trial with patients having rheumatoid arthritis.

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  • A new response-Adaptive Design for continuous treatment responses for phase III clinical trials
    Journal of Statistical Planning and Inference, 2011
    Co-Authors: Uttam Bandyopadhyay, Atanu Biswas, Rahul Bhattacharya

    Abstract:

    A new response-Adaptive Design, applicable for general class of continuous response distributions, is proposed. The allocation Design is studied both theoretically and numerically and compared with some existing procedures. The applicability of the proposed procedure is also illustrated using real life data sets.

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Atanu Biswas – One of the best experts on this subject based on the ideXlab platform.

  • A response Adaptive Design for ordinal categorical responses weighing the cumulative odds ratios
    Biostatistics & Epidemiology, 2019
    Co-Authors: Atanu Biswas, Rahul Bhattacharya, Soumyadeep Das

    Abstract:

    ABSTRACTWeighing the cumulative odds ratios suitably, a two treatment response Adaptive Design for phase III clinical trial is proposed for ordinal categorical responses. Properties of the proposed…

    Free Register to Access Article

  • A response Adaptive Design for ordinal categorical responses.
    Journal of biopharmaceutical statistics, 2018
    Co-Authors: Atanu Biswas, Rahul Bhattacharya, Soumyadeep Das

    Abstract:

    A two treatment response Adaptive Design is developed for phase III clinical trials with ordinal categorical treatment outcome using Goodman-Kruskal measure of association. Properties of the proposed Design are studied both empirically and theoretically and the acceptability is further illustrated using two real data-sets; one from a clinical trial with trauma patients and the other from a trial with patients having rheumatoid arthritis.

    Free Register to Access Article

  • A new response-Adaptive Design for continuous treatment responses for phase III clinical trials
    Journal of Statistical Planning and Inference, 2011
    Co-Authors: Uttam Bandyopadhyay, Atanu Biswas, Rahul Bhattacharya

    Abstract:

    A new response-Adaptive Design, applicable for general class of continuous response distributions, is proposed. The allocation Design is studied both theoretically and numerically and compared with some existing procedures. The applicability of the proposed procedure is also illustrated using real life data sets.

    Free Register to Access Article

Mark Chang – One of the best experts on this subject based on the ideXlab platform.

  • Adaptive Design methods in clinical trials – a review.
    Orphanet journal of rare diseases, 2008
    Co-Authors: Shein-chung Chow, Mark Chang

    Abstract:

    In recent years, the use of Adaptive Design methods in clinical research and development based on accrued data has become very popular due to its flexibility and efficiency. Based on adaptations applied, Adaptive Designs can be classified into three categories: prospective, concurrent (ad hoc), and retrospective Adaptive Designs. An Adaptive Design allows modifications made to trial and/or statistical procedures of ongoing clinical trials. However, it is a concern that the actual patient population after the adaptations could deviate from the originally target patient population and consequently the overall type I error (to erroneously claim efficacy for an infective drug) rate may not be controlled. In addition, major adaptations of trial and/or statistical procedures of on-going trials may result in a totally different trial that is unable to address the scientific/medical questions the trial intends to answer. In this article, several commonly considered Adaptive Designs in clinical trials are reviewed. Impacts of ad hoc adaptations (protocol amendments), challenges in by Design (prospective) adaptations, and obstacles of retrospective adaptations are described. Strategies for the use of Adaptive Design in clinical development of rare diseases are discussed. Some examples concerning the development of Velcade intended for multiple myeloma and non-Hodgkin’s lymphoma are given. Practical issues that are commonly encountered when implementing Adaptive Design methods in clinical trials are also discussed.

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  • Adaptive Design method based on sum of p-values.
    Statistics in medicine, 2007
    Co-Authors: Mark Chang

    Abstract:

    Bauer and Kohne proposed an Adaptive Design using Fisher’s combination of independent p-values based on subsamples from different stages (Biometrics 1994; 50(4):1029-1041). Their method provides great flexibility in the selection of statistical methods for hypothesis testing of subsamples. However, the choices for the stopping boundaries are not flexible enough to meet practical needs (Biometrics 2001; 57(3): 886-891). In this paper, an Adaptive Design method is proposed using linear combination of the independent p-values. The method provides great flexibility in the selection of stopping boundaries and no numerical integration is required for the two-stage Designs. The stopping boundaries and p-values can be calculated manually. The operating characteristics of the Adaptive Designs are studied using computer simulations with and without sample size adjustment. Examples are presented for superiority and non-inferiority trials with different endpoints (normal, binary, and survival) under different adaptations. The statistical efficiency of the proposed method is compared with other methods based on conditional power.

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  • Adaptive Design Theory and Implementation using SAS and R
    , 2007
    Co-Authors: Mark Chang

    Abstract:

    Adaptive Design theory and implementation using SAS and R , Adaptive Design theory and implementation using SAS and R , کتابخانه دیجیتال جندی شاپور اهواز

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