Advances in biomedicine, particularly, in genomics have fueled the development of targeted agents in cancer therapy. Targeted agents work nicely when drugs are given to patients with the intended targets, for example, TKI for lung cancer patients with EGFR mutation. Targeted agents, however, do not work for everyone and may not work at all. Hence, the development of target agents requires the evaluation of treatment efficacy as well as the identification of prognostic and predictive markers. Furthermore, upon the identification of each patient's marker profile, it is desirable to treat patients with best available treatments in the clinical trial accordingly. In this session, we will discuss clinical trial designs to (1) test the treatment efficacy, (2) identify prognostic and predictive markers, and (3) provide better treatments for patients enrolled in the trial. The concept of Bayesian adaptive designs will be introduced. Via the outcome-adaptive randomization, more patients are treated with more effective treatments based on the available data at the time. With frequent interim monitoring, ineffective treatments can be stopped early for futility, new treatments can be added, and effective treatments can graduate for further evaluation. The concept of the platform design and the N-of-1 design will be discussed as well. Examples and lessons learned from the recently completed BATTLE trial in non-small cell lung cancer will be given. At the conclusion of this activity, the learner will be able to do the following: 1. Discuss the current knowledge of personalized treatment in lung cancer. 2. Discuss the efficient statistical design in drug development. 3. Explain the requirement and limitation of designing and conducting adaptive designs.