In a short period of time, advances in genetics has allowed scientists to identify genes (biomarkers) that are linked with certain diseases. To translate these great scientific findings into real-world products for those who need them (personalized medicine), clinical trials play an essential and important role. Personalized medicine is an approach that will allow physicians to tailor a treatment regimen based on an individual patient's characteristics (which could be biomarkers or other covariates). To develop personalized medicine, we need new designs for clinical trials so that genetics information and other biomarkers can be incorporated to assist in treatment selection.

    This talk first provides a brief review of design and statistical inference related with personalized medicine. Personalized medicine raises some new challenges for the design of clinical trials as: (1) more covariates (biomarkers) have to be considered, and (2) particular attention needs to be paid to the interaction between treatment and covariate. Then we discuss several new families of designs for personal medicine. New techniques are introduced to study the theoretical properties of the proposed designs. Advantages of the proposed designs are demonstrated through both theoretical and numerical studies. To deal with the complex data structure arise in clinical trials of personalized medicine, some further and important statistical issues are discussed.