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  1. Article: BayesESS: A tool for quantifying the impact of parametric priors in Bayesian analysis.

    Song, Jaejoon / Morita, Satoshi / Kuo, Ying-Wei / Lee, J Jack

    SoftwareX

    2023  Volume 22

    Abstract: Bayesian inference has become an attractive choice for scientists seeking to incorporate prior knowledge into their modeling framework. While the R community has been an important contributor in facilitating Bayesian statistical analyses, software to ... ...

    Abstract Bayesian inference has become an attractive choice for scientists seeking to incorporate prior knowledge into their modeling framework. While the R community has been an important contributor in facilitating Bayesian statistical analyses, software to evaluate the impact of prior knowledge to such modeling framework has been lacking. In this article, we present BayesESS, a comprehensive, free, and open source R package for quantifying the impact of parametric priors in Bayesian analysis. We also introduce an accompanying web-based application for estimating and visualizing Bayesian effective sample size for purposes of conducting or planning Bayesian analyses.
    Language English
    Publishing date 2023-03-26
    Publishing country Netherlands
    Document type Journal Article
    ZDB-ID 2819369-6
    ISSN 2352-7110
    ISSN 2352-7110
    DOI 10.1016/j.softx.2023.101358
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: BOIN Suite: A Software Platform to Design and Implement Novel Early-Phase Clinical Trials.

    Zhou, Yanhong / Lin, Ruitao / Kuo, Ying-Wei / Lee, J Jack / Yuan, Ying

    JCO clinical cancer informatics

    2021  Volume 5, Page(s) 91–101

    Abstract: Purpose: Using novel Bayesian adaptive designs has great potential to improve the efficiency of early-phase clinical trials. A major barrier for clinical researchers to adopt novel designs is the lack of easy-to-use software. Our purpose is to develop a ...

    Abstract Purpose: Using novel Bayesian adaptive designs has great potential to improve the efficiency of early-phase clinical trials. A major barrier for clinical researchers to adopt novel designs is the lack of easy-to-use software. Our purpose is to develop a user-friendly software platform to implement novel clinical trial designs that address various challenges in early-phase dose-finding trials.
    Methods: We used
    Results: We developed a web-based software suite, called Bayesian optimal interval (BOIN) suite, which includes R Shiny applications to handle various clinical settings, including single-agent phase I trials with and without prior information, trials with late-onset toxicity, trials to find the optimal biological dose based on risk-benefit trade-off, and drug combination trials to find a single maximum tolerated dose (MTD) or the MTD contour. The applications are built using the same software architecture to ensure the best and a uniform user experience, and they are developed using a proven software development standard operating procedure to ensure accuracy, robustness, and reproducibility. The suite is freely available with internet access and a web browser without the need of installing any other software.
    Conclusion: The BOIN suite allows clinical researchers to design various types of early-phase clinical trials under a unified framework. This work is extremely important because it not only advances the clinical research and drug development by facilitating the use of novel trial designs with optimal performance but also enhances collaborations between biostatisticians and clinicians by disseminating novel statistical methodology to broader scientific communities through user-friendly software. The BOIN suite establishes a KISS principle: keep it simple, but smart.
    MeSH term(s) Bayes Theorem ; Clinical Trials as Topic ; Computer Simulation ; Dose-Response Relationship, Drug ; Humans ; Models, Statistical ; Neoplasms ; Reproducibility of Results ; Software
    Language English
    Publishing date 2021-01-12
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural
    ISSN 2473-4276
    ISSN (online) 2473-4276
    DOI 10.1200/CCI.20.00122
    Database MEDical Literature Analysis and Retrieval System OnLINE

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