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  1. Book ; Online ; E-Book: Bayesian precision medicine

    Thall, Peter F.

    (Chapman & Hall/CRC Biostatistics series)

    2024  

    Author's details Peter F. Thall
    Series title Chapman & Hall/CRC Biostatistics series
    Language English
    Size 1 Online-Ressource (xi, 317 Seiten), Illustrationen, Diagramme
    Edition First edition
    Publisher CRC Press Taylor & Francis
    Publishing place Boca Raton, FL
    Publishing country United States
    Document type Book ; Online ; E-Book
    Remark Zugriff für angemeldete ZB MED-Nutzerinnen und -Nutzer
    HBZ-ID HT030723329
    ISBN 9781040026717 ; 978-1-003-47425-8 ; 9781032754468 ; 9781032754963 ; 1040026710 ; 1-003-47425-X ; 103275446X ; 1032754966
    Database ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

    Full text online

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  2. Book ; Online ; E-Book: Statistical Remedies for Medical Researchers

    Thall, Peter F.

    (Springer Series in Pharmaceutical Statistics,)

    2020  

    Abstract: This book illustrates numerous statistical practices that are commonly used by medical researchers, but which have severe flaws that may not be obvious. For each example, it provides one or more alternative statistical methods that avoid misleading or ... ...

    Author's details by Peter F. Thall
    Series title Springer Series in Pharmaceutical Statistics,
    Abstract This book illustrates numerous statistical practices that are commonly used by medical researchers, but which have severe flaws that may not be obvious. For each example, it provides one or more alternative statistical methods that avoid misleading or incorrect inferences being made. The technical level is kept to a minimum to make the book accessible to non-statisticians. At the same time, since many of the examples describe methods used routinely by medical statisticians with formal statistical training, the book appeals to a broad readership in the medical research community.
    Keywords Statistics  ; Pharmacy ; Biostatistics ; Pharmaceutical technology ; Statistics for Life Sciences, Medicine, Health Sciences ; Drug Safety and Pharmacovigilance ; Pharmaceutical Sciences/Technology ; Bayesian Inference ; Statistical Theory and Methods
    Subject code 610.727
    Language English
    Size 1 online resource (XI, 291 p. 42 illus., 12 illus. in color.)
    Edition 1st ed. 2020.
    Publisher Springer International Publishing ; Imprint: Springer
    Publishing place Cham
    Document type Book ; Online ; E-Book
    Remark Zugriff für angemeldete ZB MED-Nutzerinnen und -Nutzer
    ISBN 3-030-43714-0 ; 3-030-43713-2 ; 978-3-030-43714-5 ; 978-3-030-43713-8
    DOI 10.1007/978-3-030-43714-5
    Database ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

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  3. Article: Adaptive Enrichment Designs in Clinical Trials.

    Thall, Peter F

    Annual review of statistics and its application

    2022  Volume 8, Issue 1, Page(s) 393–411

    Abstract: Adaptive enrichment designs for clinical trials may include rules that use interim data to identify treatment-sensitive patient subgroups, select or compare treatments, or change entry criteria. A common setting is a trial to compare a new biologically ... ...

    Abstract Adaptive enrichment designs for clinical trials may include rules that use interim data to identify treatment-sensitive patient subgroups, select or compare treatments, or change entry criteria. A common setting is a trial to compare a new biologically targeted agent to standard therapy. An enrichment design's structure depends on its goals, how it accounts for patient heterogeneity and treatment effects, and practical constraints. This article first covers basic concepts, including treatment-biomarker interaction, precision medicine, selection bias, and sequentially adaptive decision making, and briefly describes some different types of enrichment. Numerical illustrations are provided for qualitatively different cases involving treatment-biomarker interactions. Reviews are given of adaptive signature designs; a Bayesian design that uses a random partition to identify treatment-sensitive biomarker subgroups and assign treatments; and designs that enrich superior treatment sample sizes overall or within subgroups, make subgroup-specific decisions, or include outcome-adaptive randomization.
    Language English
    Publishing date 2022-10-03
    Publishing country United States
    Document type Journal Article
    ISSN 2326-8298
    ISSN 2326-8298
    DOI 10.1146/annurev-statistics-040720-032818
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  4. Article ; Online: A generalized phase 1-2-3 design integrating dose optimization with confirmatory treatment comparison.

    Zang, Yong / Thall, Peter F / Yuan, Ying

    Biometrics

    2024  Volume 80, Issue 1

    Abstract: ... evaluation is proposed. The design extends and modifies the design of Chapple and Thall (2019), denoted by CT ...

    Abstract A generalized phase 1-2-3 design, Gen 1-2-3, that includes all phases of clinical treatment evaluation is proposed. The design extends and modifies the design of Chapple and Thall (2019), denoted by CT. Both designs begin with a phase 1-2 trial including dose acceptability and optimality criteria, and both select an optimal dose for phase 3. The Gen 1-2-3 design has the following key differences. In stage 1, it uses phase 1-2 criteria to identify a set of candidate doses rather than 1 dose. In stage 2, which is intermediate between phase 1-2 and phase 3, it randomizes additional patients fairly among the candidate doses and an active control treatment arm and uses survival time data from both stage 1 and stage 2 patients to select an optimal dose. It then makes a Go/No Go decision of whether or not to conduct phase 3 based on the predictive probability that the selected optimal dose will provide a specified substantive improvement in survival time over the control. A simulation study shows that the Gen 1-2-3 design has desirable operating characteristics compared to the CT design and 2 conventional designs.
    MeSH term(s) Humans ; Clinical Protocols ; Computer Simulation ; Dose-Response Relationship, Drug ; Probability ; Research Design ; Clinical Trials, Phase I as Topic ; Clinical Trials, Phase II as Topic ; Clinical Trials, Phase III as Topic
    Language English
    Publishing date 2024-02-16
    Publishing country United States
    Document type Journal Article
    ZDB-ID 213543-7
    ISSN 1541-0420 ; 0099-4987 ; 0006-341X
    ISSN (online) 1541-0420
    ISSN 0099-4987 ; 0006-341X
    DOI 10.1093/biomtc/ujad022
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  5. Article ; Online: Bayesian Utility-Based Designs for Subgroup-Specific Treatment Comparison and Early-Phase Dose Optimization in Oncology Clinical Trials.

    Thall, Peter F

    JCO precision oncology

    2019  Volume 3

    Abstract: Purpose: Despite the fact that almost any sample of patients with a particular disease is heterogeneous, most clinical trial designs ignore the possibility that treatment or dose effects may differ between prognostic or biologically defined subgroups. ... ...

    Abstract Purpose: Despite the fact that almost any sample of patients with a particular disease is heterogeneous, most clinical trial designs ignore the possibility that treatment or dose effects may differ between prognostic or biologically defined subgroups. This article reviews two clinical trial designs that make subgroup-specific decisions and compares each to a simpler design that ignores patient heterogeneity. The purpose is to illustrate the benefits of accounting prospectively for treatment-subgroup interactions and how utilities may be used to quantify risk-benefit trade-offs.
    Methods: Two Bayesian clinical trial designs that perform subgroup-specific decision making and inference based on elicited utilities of patient outcomes are reviewed. The first is a randomized comparative trial of nutritional prehabilitation for patients undergoing esophageal resection that has two prognostic subgroups and is based on postoperative morbidity score. The second is a sequentially adaptive trial of natural killer cells for treating hematologic malignancies that is based on five time-to-event outcomes and that performs safety monitoring and optimizes cell dose within six disease subgroups. Computer simulations under a range of different scenarios are presented for each design to establish its operating characteristics and compare it to a more conventional design that ignores patient heterogeneity.
    Results: Each design has attractive operating characteristics, is greatly superior to a simplified design that ignores patient subgroups, is robust to deviations from its assumed statistical model, and is feasible to use for conducting trials.
    Conclusion: Bayesian designs that make subgroup-specific decisions in randomized comparative trials or sequentially adaptive early-phase dose-finding trials are superior to designs that ignore patient heterogeneity. Using elicited utilities of complex patient outcomes to quantify risk-benefit trade-offs provides a practical and ethical basis for decision making and treatment evaluation in clinical trials.
    Language English
    Publishing date 2019-10-24
    Publishing country United States
    Document type Journal Article ; Review
    ISSN 2473-4284
    ISSN (online) 2473-4284
    DOI 10.1200/PO.18.00379
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article ; Online: Bayesian cancer clinical trial designs with subgroup-specific decisions.

    Thall, Peter F

    Contemporary clinical trials

    2019  Volume 90, Page(s) 105860

    Abstract: Two illustrative applications are presented of Bayesian clinical trial designs that make adaptive subgroup-specific decisions based on elicited utilities of patient outcomes to quantify risk-benefit trade-offs. The first design is for a randomized trial ... ...

    Abstract Two illustrative applications are presented of Bayesian clinical trial designs that make adaptive subgroup-specific decisions based on elicited utilities of patient outcomes to quantify risk-benefit trade-offs. The first design is for a randomized trial to evaluate effects of nutritional prehabilitation on post-operative morbidity in esophageal cancer patients undergoing surgery. The second design is for a dose-finding trial of natural killer cells to treat advanced hematologic malignancies, with five time-to-event outcomes. Each design is based on a Bayesian hierarchical model that borrows strength between subgroups. Computer simulation is used to evaluate each design's properties, including comparison to a simpler design ignoring treatment-subgroup interactions. The simulations show that accounting prospectively for treatment-subgroup interactions yields designs with very desirable properties, is greatly superior to a simplified comparator design that ignores subgroups if treatment-subgroup interactions actually exist, and each design is robust to deviations from the assumed underlying model.
    MeSH term(s) Bayes Theorem ; Biomarkers ; Clinical Trials, Phase I as Topic/methods ; Clinical Trials, Phase II as Topic/methods ; Computer Simulation ; Esophageal Neoplasms/diet therapy ; Hematologic Neoplasms/therapy ; Humans ; Killer Cells, Natural/metabolism ; Precision Medicine ; Research Design
    Chemical Substances Biomarkers
    Language English
    Publishing date 2019-10-31
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Review
    ZDB-ID 2182176-8
    ISSN 1559-2030 ; 1551-7144
    ISSN (online) 1559-2030
    ISSN 1551-7144
    DOI 10.1016/j.cct.2019.105860
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    In stock of ZB MED Cologne/Königswinter

    Zs.A 1581: Show issues Location:
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  7. Article: Interpreting Randomized Controlled Trials.

    Msaouel, Pavlos / Lee, Juhee / Thall, Peter F

    Cancers

    2023  Volume 15, Issue 19

    Abstract: This article describes rationales and limitations for making inferences based on data from randomized controlled trials (RCTs). We argue that obtaining a representative random sample from a patient population is impossible for a clinical trial because ... ...

    Abstract This article describes rationales and limitations for making inferences based on data from randomized controlled trials (RCTs). We argue that obtaining a representative random sample from a patient population is impossible for a clinical trial because patients are accrued sequentially over time and thus comprise a convenience sample, subject only to protocol entry criteria. Consequently, the trial's sample is unlikely to represent a definable patient population. We use causal diagrams to illustrate the difference between random allocation of interventions within a clinical trial sample and true simple or stratified random sampling, as executed in surveys. We argue that group-specific statistics, such as a median survival time estimate for a treatment arm in an RCT, have limited meaning as estimates of larger patient population parameters. In contrast, random allocation between interventions facilitates comparative causal inferences about between-treatment effects, such as hazard ratios or differences between probabilities of response. Comparative inferences also require the assumption of transportability from a clinical trial's convenience sample to a targeted patient population. We focus on the consequences and limitations of randomization procedures in order to clarify the distinctions between pairs of complementary concepts of fundamental importance to data science and RCT interpretation. These include internal and external validity, generalizability and transportability, uncertainty and variability, representativeness and inclusiveness, blocking and stratification, relevance and robustness, forward and reverse causal inference, intention to treat and per protocol analyses, and potential outcomes and counterfactuals.
    Language English
    Publishing date 2023-09-22
    Publishing country Switzerland
    Document type Journal Article ; Review
    ZDB-ID 2527080-1
    ISSN 2072-6694
    ISSN 2072-6694
    DOI 10.3390/cancers15194674
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  8. Article ; Online: Risk-benefit trade-offs and precision utilities in phase I-II clinical trials.

    Msaouel, Pavlos / Lee, Juhee / Thall, Peter F

    Clinical trials (London, England)

    2023  , Page(s) 17407745231214750

    Abstract: Background: Identifying optimal doses in early-phase clinical trials is critically important. Therapies administered at doses that are either unsafe or biologically ineffective are unlikely to be successful in subsequent clinical trials or to obtain ... ...

    Abstract Background: Identifying optimal doses in early-phase clinical trials is critically important. Therapies administered at doses that are either unsafe or biologically ineffective are unlikely to be successful in subsequent clinical trials or to obtain regulatory approval. Identifying appropriate doses for new agents is a complex process that involves balancing the risks and benefits of outcomes such as biological efficacy, toxicity, and patient quality of life.
    Purpose: While conventional phase I trials rely solely on toxicity to determine doses, phase I-II trials explicitly account for both efficacy and toxicity, which enables them to identify doses that provide the most favorable risk-benefit trade-offs. It is also important to account for patient covariates, since one-size-fits-all treatment decisions are likely to be suboptimal within subgroups determined by prognostic variables or biomarkers. Notably, the selection of estimands can influence our conclusions based on the prognostic subgroup studied. For example, assuming monotonicity of the probability of response, higher treatment doses may yield more pronounced efficacy in favorable prognosis compared to poor prognosis subgroups when the estimand is mean or median survival. Conversely, when the estimand is the 3-month survival probability, higher treatment doses produce more pronounced efficacy in poor prognosis compared to favorable prognosis subgroups.
    Methods and conclusions: Herein, we first describe why it is essential to consider clinical practice when designing a clinical trial and outline a stepwise process for doing this. We then review a precision phase I-II design based on utilities tailored to prognostic subgroups that characterize efficacy-toxicity risk-benefit trade-offs. The design chooses each patient's dose to optimize their expected utility and allows patients in different prognostic subgroups to have different optimal doses. We illustrate the design with a dose-finding trial of a new therapeutic agent for metastatic clear cell renal cell carcinoma.
    Language English
    Publishing date 2023-12-18
    Publishing country England
    Document type Journal Article
    ZDB-ID 2138796-5
    ISSN 1740-7753 ; 1740-7745
    ISSN (online) 1740-7753
    ISSN 1740-7745
    DOI 10.1177/17407745231214750
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    Zs.A 5831: Show issues Location:
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  9. Article ; Online: Generalized phase I-II designs to increase long term therapeutic success rate.

    Thall, Peter F / Zang, Yong / Yuan, Ying

    Pharmaceutical statistics

    2023  Volume 22, Issue 4, Page(s) 692–706

    Abstract: Designs for early phase dose finding clinical trials typically are either phase I based on toxicity, or phase I-II based on toxicity and efficacy. These designs rely on the implicit assumption that the dose of an experimental agent chosen using these ... ...

    Abstract Designs for early phase dose finding clinical trials typically are either phase I based on toxicity, or phase I-II based on toxicity and efficacy. These designs rely on the implicit assumption that the dose of an experimental agent chosen using these short-term outcomes will maximize the agent's long-term therapeutic success rate. In many clinical settings, this assumption is not true. A dose selected in an early phase oncology trial may give suboptimal progression-free survival or overall survival time, often due to a high rate of relapse following response. To address this problem, a new family of Bayesian generalized phase I-II designs is proposed. First, a conventional phase I-II design based on short-term outcomes is used to identify a set of candidate doses, rather than selecting one dose. Additional patients then are randomized among the candidates, patients are followed for a predefined longer time period, and a final dose is selected to maximize the long-term therapeutic success rate, defined in terms of duration of response. Dose-specific sample sizes in the randomization are determined adaptively to obtain a desired level of selection reliability. The design was motivated by a phase I-II trial to find an optimal dose of natural killer cells as targeted immunotherapy for recurrent or treatment-resistant B-cell hematologic malignancies. A simulation study shows that, under a range of scenarios in the context of this trial, the proposed design has much better performance than two conventional phase I-II designs.
    MeSH term(s) Humans ; Bayes Theorem ; Reproducibility of Results ; Research Design ; Computer Simulation ; Neoplasms/drug therapy ; Dose-Response Relationship, Drug ; Maximum Tolerated Dose
    Language English
    Publishing date 2023-04-11
    Publishing country England
    Document type Randomized Controlled Trial ; 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.2301
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  10. Article ; Online: Current issues in dose-finding designs: A response to the US Food and Drug Adminstrations's Oncology Center of Excellence Project Optimus.

    Thall, Peter F / Garrett-Mayer, Elizabeth / Wages, Nolan A / Halabi, Susan / Cheung, Ying Kuen

    Clinical trials (London, England)

    2024  , Page(s) 17407745241234652

    Abstract: With the advent of targeted agents and immunological therapies, the medical research community has become increasingly aware that conventional methods for determining the best dose or schedule of a new agent are inadequate. It has been well established ... ...

    Abstract With the advent of targeted agents and immunological therapies, the medical research community has become increasingly aware that conventional methods for determining the best dose or schedule of a new agent are inadequate. It has been well established that conventional phase I designs cannot reliably identify safe and effective doses. This problem applies, generally, for cytotoxic agents, radiation therapy, targeted agents, and immunotherapies. To address this, the US Food and Drug Administration's Oncology Center of Excellence initiated Project Optimus, with the goal "to reform the dose optimization and dose selection paradigm in oncology drug development." As a response to Project Optimus, the articles in this special issue of
    Language English
    Publishing date 2024-04-03
    Publishing country England
    Document type Journal Article
    ZDB-ID 2138796-5
    ISSN 1740-7753 ; 1740-7745
    ISSN (online) 1740-7753
    ISSN 1740-7745
    DOI 10.1177/17407745241234652
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