James J Dignam Theodore G Karrison and John Bryant

In this chapter, we discuss the design and analysis of oncology clinical trials. Because analysis follows naturally from design and is specified a priori in any well-planned trial, it is appropriate to discuss these topics together. We review clinical trial designs and associated analytical methods used in the different phases of therapy development. Along the way we identify areas in which the methodology is adapting to new approaches to therapeutic intervention. This chapter provides only a brief sketch of the main concepts and current research areas, and we refer the reader to primary sources and comprehensive texts on clinical trials in oncology for further details. Two excellent recent texts in particular, the Handbook of Statistics in Clinical Oncology1 and Clinical Trials in Oncology,2 provide the fundamentals of trial design, conduct, and analysis, as well as up-to-date discussion of new challenges and active research in statistical methods for oncology clinical trials.

not find a new treatment to be superior is informative, and resources can then appropriately be directed into other more promising alternatives.

In statistical hypothesis testing, type I or a error refers to the probability of incorrectly deciding in favor of a treatment difference when in fact none exists. Typically, the a probability is fixed at some small value, such as 0.05 or 0.01. When hypothesis tests are repeated, the probability increases that at least one test result will be erroneous. A particular complication arising in clinical trials is the need to periodically evaluate the primary hypothesis as information accumulates. These interim analyses are conducted to ensure that if definitive evidence of benefit or harm emerges before the anticipated end of the trial, then actions can be taken for the protection and benefit of trial participants. Appropriate statistical methodology to accommodate multiple serial analyses is discussed later.

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