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Statistical Methods for Survival Trial Design
With Applications to Cancer Clinical Trials Using R. Chapman & Hall/CRC Biostatistics Series
By (author)
Jianrong Wu
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Synopsis
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. New & Used
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What Reviewers Are Saying
". . . 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 ". . . 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
"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 ". . . this book provides a comprehensiv
"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