Some statistical considerations in the clinical development of cancer immunotherapies.

Abstract

Immuno-oncology has emerged as an exciting new approach to cancer treatment. Common immunotherapy approaches include cancer vaccine, effector cell therapy, and T-cell-stimulating antibody. Checkpoint inhibitors such as cytotoxic T lymphocyte-associated antigen 4 and programmed death-1/L1 antagonists have shown promising results in multiple indications in solid tumors and hematology. However, the mechanisms of action of these novel drugs pose unique statistical challenges in the accurate evaluation of clinical safety and efficacy, including late-onset toxicity, dose optimization, evaluation of combination agents, pseudoprogression, and delayed and lasting clinical activity. Traditional statistical methods may not be the most accurate or efficient. It is highly desirable to develop the most suitable statistical methodologies and tools to efficiently investigate cancer immunotherapies. In this paper, we summarize these issues and discuss alternative methods to meet the challenges in the clinical development of these novel agents. For safety evaluation and dose-finding trials, we recommend the use of a time-to-event model-based design to handle late toxicities, a simple 3-step procedure for dose optimization, and flexible rule-based or model-based designs for combination agents. For efficacy evaluation, we discuss alternative endpoints/designs/tests including the time-specific probability endpoint, the restricted mean survival time, the generalized pairwise comparison method, the immune-related response criteria, and the weighted log-rank or weighted Kaplan-Meier test. The benefits and limitations of these methods are discussed, and some recommendations are provided for applied researchers to implement these methods in clinical practice.