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Statistical Methods for Survival Trial Design: With Applications to Cancer Clinical Trials Using R book cover

1st Edition

Statistical Methods for Survival Trial Design With Applications to Cancer Clinical Trials Using R

By Jianrong Wu Copyright 2018
    273 Pages
    by Chapman & Hall

    273 Pages
    by Chapman & Hall

    273 Pages
    by Chapman & Hall
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    Statistical Methods for Survival Trial Design: With Applications to Cancer Clinical Trials Using R provides a thorough presentation of the principles of designing and monitoring cancer clinical trials in which time-to-event is the primary endpoint. Traditional cancer trial designs with time-to-event endpoints are often limited to the exponential model or proportional hazards model. In practice, however, those model assumptions may not be satisfied for long-term survival trials.



    This book is the first to cover comprehensively the many newly developed methodologies for survival trial design, including trial design under the Weibull survival models; extensions of the sample size calculations under the proportional hazard models; and trial design under mixture cure models, complex survival models, Cox regression models, and competing-risk models. A general sequential procedure based on the sequential conditional probability ratio test is also implemented for survival trial monitoring. All methodologies are presented with sufficient detail for interested researchers or graduate students.

    Preface List of Figures List of Tables 1. Introduction to Cancer Clinical Trials General Aspects of Cancer Clinical Trial Design Study Objectives Treatment Plan Eligibility Criteria Statistical Considerations Statistical Aspects of Cancer Survival Trial Design Randomization Stratification Blinding Sample Size Calculation 2. Survival Analysis Survival Distribution Exponential Distribution Weibull Distribution Gamma Distribution Gompertz Distribution Log-Normal Distribution Log-Logistic Distribution Survival Data Fitting the Parametric Survival Distribution Kaplan-Meier Estimates Median Survival Time Log-Rank Test Cox Regression Model 3. Counting Process and Martingale_ Basic Convergence Concepts Counting Process Definition Martingale Central Limit Theorem Counting Process Formulation of Censored Survival Data 4. Survival Trial Design Under the Parametric Model Introduction Weibull Model Test Statistic Distribution of the MLE test Sample Size Formula Sample Size Calculation Accrual Duration Calculation Example and R code 5. Survival Trial Design Under the Proportional Hazards Model Introduction Proportional Hazards Model Asymptotic Distribution of the Log-rank Test Schoenfeld Formula Rubinstein Formula Freedman Formula Comparison Sample Size Calculation Under Various Models Example Optimal Properties of the Log-Rank Test_ Optimal Sample Size Allocation Optimal Power Precise Formula <

    Biography

    Jianrong (John) Wu is a professor in the Division of Cancer Biostatistics, Department of Biostatistics, Markey Cancer Center, University of Kentucky. He has more than 15 years’ experience of designing and conducting cancer clinical trials at St. Jude Children’s Research Hospital and has developed several novel statistical methods for designing phase II and phase III survival trials.



     



     

    ". . . this book provides a comprehensive introduction to statistical methods in cancer of sample size calculations and survival clinical trial designs from the classical techniques to the newly proposed formulae such as the mixture cure model and a group sequential trial design. This book has a vast list of citations and is an excellent reference for statisticians performing oncology research in the pharmaceutical industry or in other settings, and for graduate students in biostatistics or in related fields." ~ Journal of Biopharmaceutical Statistics

    "I would recommend this book for those that are starting to work with this kind of trial design and would like to have a good overview and source of knowledge
    for some not so common methods for more complex cancer trial designs, including simple formulae to implement in R to calculate sample sizes."
    ~David Manteigas, ISCB Newsletter

     

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