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596 – Statistical Challenges in Drug Development in the Era of Immuno-Oncology

Improved Designs and Analyses of Safety and Ef�cacy in Immuno-Oncology

at Stony Brook University

Immuno-oncology has emerged as a new prominence in oncology. Common immunotherapy approaches include cancer vaccine, effector cell therapy and T-cell stimulating antibodies. Checkpoint inhibitors such as CTLA-4 and PD-1 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, delayed and lasting clinical activity. Traditional statistical methods may not be most accurate or efficient. There is high unmet need to develop the most suitable statistical methodologies to efficiently develop 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 both safety and efficacy endpoints. For safety evaluation, we propose using the time-to-event model-based design to handle late toxicity, a simple three-step procedure for dose optimization, and flexible rule-based or model-based designs for combination agents. For efficacy evaluation, we propose alternative endpoints/designs/tests including optimal designs for time-specific probability endpoint, restricted mean survival time, generalized pairwise comparison, immune-related response criteria and weighted log-rank test. Benefits and limitations of these methods are considered and some recommendations are proposed for applied researchers to implement these methods in clinical practice.