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The clinical trial has become a major research tool for developing advanced cancer treatments. In recent years, the pharmaceutical industry, hospitals, and other research centers have been increasingly active in conducting cancer clinical trials with time-to-event endpoints. Statistical methods for designing and monitoring such trials have been available for many years, and a number of books have addressed this topic, including those by Chow et al. (2003), Julious (2010), Cook and DeMets (2008), Jennison and Turnbull (2000), and Proschan et al. (2006). However, as none of these books was written specifically to address the design and monitoring of survival clinical trials, they contain only limited material on that topic. For example, in the book by Julious, only one chapter discusses sample size calculation under the exponential model and proportional hazards model, and some of the presented approaches to sample size calculation and adjusting for loss to follow-up are inappropriate. The book by Chow et al. is also limited with regard to its discussion of the exponential model, and its presentation of the method of Lakatos for complex survival trial design not only lacks sufficient detail but also contains errors. The book by Jennison and Turnbull is an excellent reference for group sequential trial design, but its coverage of survival trial design is limited to a single chapter.
Cancer treatment has progressed dramatically in recent decades, such that it is now common to see a cure or long-term survival in a significant proportion of patients with various types of cancer, e.g., breast cancer, non-Hodgkin lymphoma, leukemia, prostate cancer, melanoma, or head and neck cancer (Ewell and Ibrahim, 1997). Until now, however, the principles of designing survival trials in which a proportion of the participants are expected to be cured have not been published in any book or implemented in any commercially available software.
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