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  • Handbook of Statistics in Clinical Oncology

    Handbook of Statistics in Clinical Oncology by Hoering, Antje; Othus, Megan; Crowley, John;

      • GET 10% OFF

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      • Publisher's listprice GBP 170.00
      • The price is estimated because at the time of ordering we do not know what conversion rates will apply to HUF / product currency when the book arrives. In case HUF is weaker, the price increases slightly, in case HUF is stronger, the price goes lower slightly.

        81 217 Ft (77 350 Ft + 5% VAT)
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    81 217 Ft

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    Availability

    Estimated delivery time: In stock at the publisher, but not at Prospero's office. Delivery time approx. 3-5 weeks.
    Not in stock at Prospero.

    Why don't you give exact delivery time?

    Delivery time is estimated on our previous experiences. We give estimations only, because we order from outside Hungary, and the delivery time mainly depends on how quickly the publisher supplies the book. Faster or slower deliveries both happen, but we do our best to supply as quickly as possible.

    Product details:

    • Edition number 4
    • Publisher Chapman and Hall
    • Date of Publication 31 December 2025

    • ISBN 9781032596952
    • Binding Hardback
    • No. of pages540 pages
    • Size 254x178 mm
    • Weight 1160 g
    • Language English
    • Illustrations 94 Illustrations, black & white; 14 Halftones, black & white; 80 Line drawings, black & white
    • 700

    Categories

    Short description:

    This revised edition features contributions from leading cancer trial statisticians, providing expert insights into modern oncology trial design and methodology and provides a comprehensive resource for researchers, clinicians, and statisticians involved in the evolving landscape of oncology clinical trials.

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    Long description:

    Since the third edition of this handbook, significant advances have transformed the field of oncology. Most cancer types now offer multiple treatment options, with immunotherapies and targeted therapies becoming the standard of care. Master protocols, which allow the addition of new treatment arms without requiring new protocols, have gained popularity—not only to expedite the approval process for new therapies but also to ensure that patients receive the most beneficial treatments tailored to their individual needs. This revised edition features contributions from leading cancer trial statisticians, providing expert insights into modern oncology trial design and methodology. The handbook is structured into five key parts:



    • Part 1: Cancer prevention and screening trial designs, including risk prediction models and prevention trials

    • Part 2: Early-phase trial designs, covering dose-finding studies, selection designs, and multi-strata trials

    • Part 3: Late-stage trial designs, including approaches for IO therapies, cure-rate models, targeted agents, and considerations for pediatric oncology trials

    • Part 4: Trial conduct and operations, addressing best practices for Data Monitoring Committees (DMCs), SWOG/CRAB calculators, pragmatic trials, and clinical trial innovation

    • Part 5: Beyond primary endpoints, exploring surrogate endpoints, microbiome research, patient-reported outcomes (PROs), and tree-based partitioning methods

    This updated edition provides a comprehensive resource for researchers, clinicians, and statisticians involved in the evolving landscape of oncology clinical trials.

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    Table of Contents:

    SECTION 1 Cancer Prevention & Screening



    Chapter 1 Cancer Screening Trials


    Chapter 2 Cancer Risk Prediction Models


    Chapter 3 Incorporating External Registry Data Into Cohort-based Cancer Risk Prediction Tools


     


    SECTION 2 Trial Design | Early-Phase Trials



    Chapter 4 Phase I – Overview and Recent Trial Designs


    Chapter 5 Statistical and Machine Learning Methods for Phase I Dose-Finding


    Chapter 6 Seamless Phase I/II Trial Design for Assessing Toxicity and Efficacy for Targeted Agents


    Chapter 7 Designs Using Time to Event Endpoints / Single Arm versus Randomized Phase II Designs


    Chapter 8 Phase II Selection Designs


    Chapter 9 Phase II Trials with Multiple Strata



    SECTION 3 Trial Design | Late-Phase Trials



    Chapter 10 Cure Rate Survival Models


    Chapter 11 Phase III Trials for Targeted Agents


    Chapter 12 Phase II and III Clinical Trial Designs for Precision Medicine


    Chapter 13 SMARTs in Oncology


    Chapter 14 Statistical Considerations in the Design and Analysis of Cancer Trials with Immune-Oncology Therapies


    Chapter 15 Alpha Splitting


    Chapter 16 Early Stopping of Clinical Trials Evaluating Targeted Therapies


    Chapter 17 Noninferiority Trials


    Chapter 18 Considerations for Pediatric Oncology Trials


     


    SECTION 4 Trial Conduct


     


    Chapter 19 An Overview of Master Protocols


    Chapter 20 On Use of Covariates in Randomization and Analysis of Clinical Trials


    Chapter 21 Pragmatic Clinical Trials in Clinical Oncology: A Statistical Perspective


    Chapter 22 Dynamic Treatment Regimens and Sequential Multiple Assignment Randomized Trial in Cancer Research


    Chapter 23 Outcome-Adaptive Randomization


    Chapter 24 Current Suggested Practices and Issues for Data and Safety Monitoring Committees in Cancer Clinical Trials


    Chapter 25 Improving Data Collection: EHR-to-EDC Assisted Data Transfer


    Chapter 26 Barriers and Disparities in Access to Cancer Clinical Trials – Causes and Implications 


    Chapter 27 SWOG Statistical Calculators for Design and Analysis of Clinical Trials


    Chapter 28 Streamlining Data Collection and Trial Conduct


     


    SECTION 5 Beyond the Primary Endpoint



    Chapter 29 Use of Circulating Tumor DNA in Oncology – ctMoniTR


    Chapter 30 Statistical Analysis of -Omics Data


    Chapter 31 Principles of Design and Analysis for Patient-Reported Outcomes


    Chapter 32 X Intermediate and Surrogate Endpoints in Phase III Randomized


    Chapter 33 Prognostic Groups via Interpretable Function Approximation: Tree-based and Extreme Regression Models

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