Statistical adaptation to oncology drug development evolution

Abstract

Cancer treatment started with surgery at least three thousand years ago. Radiation therapy was added in 1896 with chemotherapy started 50 years later. These "cut, burn, and poison" techniques try to kill cancer cells directly and have been the main approaches in treating cancer until recently. In the past few years, immunotherapies have revolutionized cancer treatment. Instead of treating the disease, immunotherapies treat the patient with the disease; more precisely, correct the patient's immune system so that it can fight cancer in a long term, which makes the cure of metastatic cancers a real possibility. To adapt to the evolution of oncology treatment, clinical trial designs and statistical analysis methodologies are required to change accordingly in order to efficiently bring novel oncology medicines to cancer patients. For example, one of the major differences between immunotherapies and chemotherapies is that immunotherapies may take longer to have an effect but generally last longer with some patients cured. Trial design assumptions and adaptation rules (if adaptive design is used) need to take account of this delayed effect and long-term cure effect phenomenon. At the same time, more efficient statistical tests such as Fleming-Harrington test and Zmax test can be used to improve statistical power over the conventional logrank test for the analyses of time-to-event data that often exhibit non-proportional hazards. This article intends to describe how oncology drug development evolves over time and how statistical methods change accordingly.

Keywords: Delayed effect; Immunotherapy; Logrank test; Long-term cure effect; Oncology drug development; Zmax test.