Targeted therapies and immunotherapies have revolutionized the treatment of cancers. These novel therapies demonstrate unique characteristics and challenges that cannot be handled well by existing trial designs developed for conventional chemotherapies. For example, increasing doses of targeted therapies and immunotherapies beyond a certain level may not enhance antitumor activity; treatment effects are often delayed and heterogeneous; and targeted therapies and immunotherapies are often combined with standard chemotherapies. In this session, we invited four experts to present recent developments on Bayesian adaptive designs to address these challenges. Specifically, Dr. Yong Zang (Indiana University) will present a practice phase I/II clinical trial design to identify optimal biological dose. Dr. Suyu Liu (University of MD Anderson Cancer Center) will introduce a novel Bayesian adaptive design to optimize the combination of immunotherapy with chemotherapy. Dr. Depeng Jiang (University of Manitoba, Canada) will describe a Bayesian adaptive promising zone design for cancer immunotherapy to handle delayed treatment effect and treatment heterogeneity. Dr. Ying Yuan (University of MD Anderson Cancer Center) will discuss these novel methodology, existing challenges and future directions for Bayesian adaptive designs for targeted therapies and immunotherapies.
 

Modified isotonic regression based phase I/II clinical trial designs identifying optimal biological dose
Yong Zang
Indiana University
 
Conventional phase I/II clinical trial designs often use complicated parametric models to characterize the dose-response relationships and conduct the trials. However, the parametric models are hard to justify in practice, and the misspecification of parametric models can lead to substantially undesirable performances in phase I/II trials. Moreover, it is difficult for the physicians conducting phase I/II trials to clinically interpret the parameters of these complicated models, and such significant learning costs impede the translation of novel statistical designs into practical trial implementation. To solve these issues, we propose a transparent and efficient phase I/II clinical trial design, referred to as the modified isotonic regression-based design (mISO), to identify the optimal biological doses for molecularly targeted agents and immunotherapy. The mISO design makes no parametric model assumptions on the dose-response relationship and yields desirable performances under any clinically meaningful dose-response curves.  Our comprehensive simulation studies show that the mISO and mISO-B designs are highly efficient in optimal biological dose selection and patients allocation and outperform many existing phase I/II clinical trial designs.

A Bayesian Phase I/II  Trial Design for Cancer clinical Trials Combining Immunotherapy and Chemotherapy
Suyu Liu
University of Texas MD Anderson Cancer Center
 
Immunotherapy is an innovative treatment approach that harnesses a patient's immune system to treat cancer. It has provided an alternative and complementary treatment modality to conventional chemotherapy. Combining immunotherapy with cytotoxic chemotherapy agent has become the leading trend and the most active research field in oncology. To accommodate this growing trend, we propose a Bayesian phase I/II dose-finding design to identify the optimal biological dose combination (OBDC), defined as the dose combination with the highest desirability in the risk-benefit tradeoff. We propose new statistical models to describe the relationship between the doses and treatment outcomes, including immune response, toxicity, and progression-free survival (PFS). During the trial, based on accrued data, we continuously update model estimates and adaptively assign patients to dose combinations with high desirability. The simulation study shows that our design has desirable operating characteristics.
 
Bayesian adaptive promising zone design for cancer immunotherapy 
Depeng Jiang1
University of Manitoba

The indirect mechanism of immunotherapy for cancer might lead to the delay of treatment effect and the delay times are often heterogeneous among patients. In this paper, we proposed an adaptive design with the sample size being adjusted in an interim analysis. We first used the interim data to re-estimate the survival parameters and parameters in the individual delay time distribution. Then we calculated the conditional power and adjusted the sample size based on this conditional power. The results indicate that our proposed promising zone design improved the conditional power remarkably over the existing designs.

Organiser: Dr Ying Yuan 

Chair: Beibei Guo 

Speaker: Yong Zang 

Speaker: Suyu Liu 

Speaker: Depeng Jiang  

Discussant:  Dr Ying Yuan