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  1. Book ; Online ; E-Book: Model-assisted Bayesian designs for dose finding and optimization

    Yuan, Ying / Lin, Ruitao / Lee, J. Jack

    methods and applications

    (Chapman & Hall/CRC biostatistics series)

    2023  

    Abstract: Bayesian adaptive designs provide a critical approach to improve the efficiency and success rate of drug development that has been embraced by the US Food and Drug Administration (FDA). This is particularly important for early phase trials as they forms ...

    Author's details Ying Yuan, Ruitao Lin, J. Jack Lee
    Series title Chapman & Hall/CRC biostatistics series
    Abstract "Bayesian adaptive designs provide a critical approach to improve the efficiency and success rate of drug development that has been embraced by the US Food and Drug Administration (FDA). This is particularly important for early phase trials as they forms the basis for the development and success of subsequent phase II and III trials. The objective of this book is to describes the state-of-the-art model-assisted designs to faciliate and accelerate the use of novel adaptive designs for early phase clinical trials. Model-assisted designs possess avant-garde features where superiority meets simplicity. Model-assisted designs enjoy exceptional performance comparable to more complicated model-based adaptive designs, yet their decision rules often can be pre-tabulated and included in the protocol-making implementation as simple as conventional algorithm-based designs. An example is the Bayesian optimal interval (BOIN) design, the first dose-finding design to receive the fit-for-purpose designation from the FDA. This designation underscores the regulatory agency's support of the use of the novel adaptive design to improve drug development. Features Represents the first book to provide comprehensive coverage of model-assisted designs for various types of dose-finding and optimization clinical trials Describes the up-to-date theory and practice for model-assisted designs Presents many practical challenges and issues arising from early-phase clinical trials Illustrates with many real trial applications Offers numerous tips and guidance on designing dose finding and optimization trials Provides step-by-step illustration of using software to design trials Develops a companion website (www.trialdesign.org) to provide easy-to-use software to assist learning and implementing model-assisted designs Written by internationally recognized research leaders who pioneered model-assisted designs from the University of Texas MD Anderson Cancer Center, this book shows how model-assisted designs can greatly improve the efficiency and simplify the conduct of early-phase dose finding and optimization trials. It should therefore be a very useful practical reference for biostatisticians, clinicians working in clinical trials, and drug regulatory professionals, as well as graduate students of biostatistics. Novel model-assisted designs showcase the new KISS principle: Keep it simple and smart!"--
    MeSH term(s) Drug Development/methods ; Drug Design/methods ; Drug Dosage Calculations ; Models, Statistical ; Bayes Theorem ; Clinical Trials, Phase I as Topic
    Keywords Pharmaceutical arithmetic ; Drug development/Statistical methods ; Drug development
    Subject code 615.1/9
    Language English
    Dates of publication 2023-2023
    Size 1 online resource (xiii, 219 pages) :, illustrations.
    Edition First edition.
    Publisher CRC Press
    Publishing place Boca Raton, Florida ; London ; New York
    Document type Book ; Online ; E-Book
    Remark Zugriff für angemeldete ZB MED-Nutzerinnen und -Nutzer
    ISBN 0-429-05278-2 ; 0-429-62683-5 ; 0-367-14624-X ; 978-0-429-05278-1 ; 978-0-429-62683-8 ; 978-0-367-14624-5
    Database ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

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  2. Article ; Online: A Collection of Statistical Methods for Precision Oncology.

    Lu, Ying / Lee, J Jack

    JCO precision oncology

    2022  Volume 3, Page(s) 1–3

    Language English
    Publishing date 2022-01-31
    Publishing country United States
    Document type Journal Article
    ISSN 2473-4284
    ISSN (online) 2473-4284
    DOI 10.1200/PO.19.00308
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. 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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  4. Book ; Online: Distillation Decision Tree

    Lu, Xuetao / Lee, J. Jack

    2022  

    Abstract: Machine learning models, particularly the black-box models, are widely favored for their outstanding predictive capabilities. However, they often face scrutiny and criticism due to the lack of interpretability. Paradoxically, their strong predictive ... ...

    Abstract Machine learning models, particularly the black-box models, are widely favored for their outstanding predictive capabilities. However, they often face scrutiny and criticism due to the lack of interpretability. Paradoxically, their strong predictive capabilities suggest a deep understanding about the underlying data, implying significant potential for interpretation. Leveraging the emerging concept of knowledge distillation, we introduced the method of distillation decision tree (DDT). This method enables the distillation of knowledge about the data from a black-box model into a decision tree, thereby facilitating the interpretation of the black-box model. Constructed through the knowledge distillation process, the interpretability of DDT relies significantly on the stability of its structure. We establish the theoretical foundations for the structural stability of DDT, demonstrating that its structure can achieve stability under mild assumptions. Furthermore, we develop algorithms for efficient construction of (hybrid) DDTs. A comprehensive simulation study validates DDT's ability to provide accurate and reliable interpretations. Additionally, we explore potential application scenarios and provide corresponding case studies to illustrate how DDT can be applied to real-world problems.
    Keywords Statistics - Methodology ; Computer Science - Machine Learning
    Subject code 006
    Publishing date 2022-06-09
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Article ; Online: Bayesian cluster hierarchical model for subgroup borrowing in the design and analysis of basket trials with binary endpoints.

    Chen, Nan / Lee, J Jack

    Statistical methods in medical research

    2020  Volume 29, Issue 9, Page(s) 2717–2732

    Abstract: Master protocol designs are often proposed to improve the efficiency of drug development with multiple subgroups. In the basket trial design, different subgroups can have similar biological pathogenesis pathways. Hence, a target therapy can result in ... ...

    Abstract Master protocol designs are often proposed to improve the efficiency of drug development with multiple subgroups. In the basket trial design, different subgroups can have similar biological pathogenesis pathways. Hence, a target therapy can result in similar responses. A good information sharing strategy between different subgroups can potentially improve the efficiency of evaluating treatment efficacy. In traditional hierarchical models, based on the exchangeability assumption, all subgroups are placed into the same sharing pool for cross subgroup information sharing. However, due to the heterogeneity between subgroups, there can be large differences in drug efficacy. Under such cases, strong borrowing across all subgroups is not suitable and no borrowing can be inefficient, because the treatment effect is analyzed in each subgroup separately. We propose a Bayesian cluster hierarchical model (BCHM) to improve the operating characteristics of estimating the treatment effect in multiple subgroups in basket trials. Bayesian nonparametric method is applied to dynamically calculate the number of clusters by conducting a multiple cluster classification based on subgroup outcomes. A hierarchical model is used to compute the posterior probability of the treatment effect, with the borrowing strength determined by the Bayesian nonparametric clustering and the similarities between subgroups. We apply the BCHM to clinical trials with binary endpoints. For treatment effect estimation, the BCHM yields lower mean squared error values, when compared to the independent analyses. In scenarios with a heterogeneous treatment effect, the BCHM provides lower mean squared error values compared to traditional hierarchical models. In addition, we can construct a loss function to optimize the design parameters. BCHM provides a balanced approach and smart borrowing, which yields better results in assessing the treatment effect in different scenarios compared to other conventional methods.
    MeSH term(s) Bayes Theorem ; Cluster Analysis ; Drug Development ; Probability ; Treatment Outcome
    Language English
    Publishing date 2020-03-17
    Publishing country England
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
    ZDB-ID 1136948-6
    ISSN 1477-0334 ; 0962-2802
    ISSN (online) 1477-0334
    ISSN 0962-2802
    DOI 10.1177/0962280220910186
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article ; Online: Principles and Reporting of Bayesian Trials.

    Lee, J Jack / Yin, Guosheng

    Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer

    2020  Volume 16, Issue 1, Page(s) 30–36

    Abstract: Bayesian clinical trials are becoming popular owing to their adaptive, flexible, and versatile nature. Such trials typically require specification of the prior distribution and construction of the likelihood function; subsequently, inference is made on ... ...

    Abstract Bayesian clinical trials are becoming popular owing to their adaptive, flexible, and versatile nature. Such trials typically require specification of the prior distribution and construction of the likelihood function; subsequently, inference is made on the basis of the posterior distribution. In comparison with frequentist trial designs, there are less established guidelines on how to report Bayesian trials. We provide a general overview on key components of the design, conduct, and analysis of Bayesian trials and elaborate on the reporting guidelines dos and don'ts.
    MeSH term(s) Bayes Theorem ; Humans ; Lung Neoplasms ; Research Design
    Language English
    Publishing date 2020-10-24
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
    ZDB-ID 2432037-7
    ISSN 1556-1380 ; 1556-0864
    ISSN (online) 1556-1380
    ISSN 1556-0864
    DOI 10.1016/j.jtho.2020.10.010
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article ; Online: Commentary on Hey and Kimmelman.

    Lee, J Jack

    Clinical trials (London, England)

    2015  Volume 12, Issue 2, Page(s) 110–112

    MeSH term(s) Humans ; Randomized Controlled Trials as Topic/ethics ; Research Design
    Language English
    Publishing date 2015-02-03
    Publishing country England
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Comment
    ZDB-ID 2138796-5
    ISSN 1740-7753 ; 1740-7745
    ISSN (online) 1740-7753
    ISSN 1740-7745
    DOI 10.1177/1740774514568875
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Article ; Online: The use of local and nonlocal priors in Bayesian test-based monitoring for single-arm phase II clinical trials.

    Zhou, Yanhong / Lin, Ruitao / Lee, J Jack

    Pharmaceutical statistics

    2021  Volume 20, Issue 6, Page(s) 1183–1199

    Abstract: Bayesian sequential monitoring is widely used in adaptive phase II studies where the objective is to examine whether an experimental drug is efficacious. Common approaches for Bayesian sequential monitoring are based on posterior or predictive ... ...

    Abstract Bayesian sequential monitoring is widely used in adaptive phase II studies where the objective is to examine whether an experimental drug is efficacious. Common approaches for Bayesian sequential monitoring are based on posterior or predictive probabilities and Bayesian hypothesis testing procedures using Bayes factors. In the first part of the paper, we briefly show the connections between test-based (TB) and posterior probability-based (PB) sequential monitoring approaches. Next, we extensively investigate the choice of local and nonlocal priors for the TB monitoring procedure. We describe the pros and cons of different priors in terms of operating characteristics. We also develop a user-friendly Shiny application to implement the TB design.
    MeSH term(s) Bayes Theorem ; Humans ; Probability ; Research Design
    Language English
    Publishing date 2021-05-19
    Publishing country England
    Document type Clinical Trial, Phase II ; Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
    ZDB-ID 2083706-9
    ISSN 1539-1612 ; 1539-1604
    ISSN (online) 1539-1612
    ISSN 1539-1604
    DOI 10.1002/pst.2139
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  9. Article ; Online: A Randomized Clinical Trial on Anterior Approach vs Conventional Hepatectomy for Resection of Colorectal Liver Metastasis-To Terminate or Not to Terminate the Study.

    Gupta, Divya / Lee, J Jack / Lin, Albert Y

    JAMA surgery

    2021  Volume 156, Issue 9, Page(s) 893–894

    MeSH term(s) Colorectal Neoplasms/surgery ; Hepatectomy ; Humans ; Liver Neoplasms/surgery
    Language English
    Publishing date 2021-05-25
    Publishing country United States
    Document type Journal Article ; Comment
    ZDB-ID 2701841-6
    ISSN 2168-6262 ; 2168-6254
    ISSN (online) 2168-6262
    ISSN 2168-6254
    DOI 10.1001/jamasurg.2021.1800
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Article ; Online: Evaluating Bayesian adaptive randomization procedures with adaptive clip methods for multi-arm trials.

    May Lee, Kim / Lee, J Jack

    Statistical methods in medical research

    2021  Volume 30, Issue 5, Page(s) 1273–1287

    Abstract: Bayesian adaptive randomization is a heuristic approach that aims to randomize more patients to the putatively superior arms based on the trend of the accrued data in a trial. Many statistical aspects of this approach have been explored and compared with ...

    Abstract Bayesian adaptive randomization is a heuristic approach that aims to randomize more patients to the putatively superior arms based on the trend of the accrued data in a trial. Many statistical aspects of this approach have been explored and compared with other approaches; yet only a limited number of works has focused on improving its performance and providing guidance on its application to real trials. An undesirable property of this approach is that the procedure would randomize patients to an inferior arm in some circumstances, which has raised concerns in its application. Here, we propose an adaptive clip method to rectify the problem by incorporating a data-driven function to be used in conjunction with Bayesian adaptive randomization procedure. This function aims to minimize the chance of assigning patients to inferior arms during the early time of the trial. Moreover, we propose a utility approach to facilitate the selection of a randomization procedure. A cost that reflects the penalty of assigning patients to the inferior arm(s) in the trial is incorporated into our utility function along with all patients benefited from the trial, both within and beyond the trial. We illustrate the selection strategy for a wide range of scenarios.
    MeSH term(s) Bayes Theorem ; Computer Simulation ; Humans ; Random Allocation ; Research Design ; Surgical Instruments
    Language English
    Publishing date 2021-03-10
    Publishing country England
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
    ZDB-ID 1136948-6
    ISSN 1477-0334 ; 0962-2802
    ISSN (online) 1477-0334
    ISSN 0962-2802
    DOI 10.1177/0962280221995961
    Database MEDical Literature Analysis and Retrieval System OnLINE

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