Ping Gao will serve as the Sr. Director and DDM/DTD Development Team Leader and received his Ph.D. at the Department of Mathematics, University of Washington, in the field of stochastic analysis. Ping’s working experience includes therapeutic areas such as cardiology, anti-infective, oncology, hemostasis, and rare diseases. Ping’s research interest includes non-inferiority and adaptive designs. His experience and knowledge of clinical trial designs includes non-inferiority, phase 2/3 seamless combination, population enrichment, multiple dose selection, adaptive designs and dynamic data monitoring with a number of papers published in peer reviewed journals and implemented in several commercial statistical software. Ping has designed large cardiovascular trials that enrolled more than 10,000 patients, as well as phase 3 trials for rare diseases that enrolls very small sample sizes.
Kuang-Kuo Gordon Lan will serve as a Sr. Advisor and received his PhD in Mathematical Statistics (1974) from Columbia University and is serving as CIMS Senior Statistical Advisor. Dr. Lan has published over 70 research papers on statistical methods in medical research and has given over 230 invited talks at universities and professional meetings worldwide. His most notable contribution is the development, with Dr. David DeMets, of the alpha spending approach to the design and interim analysis of clinical trials. He is also a co-author (with Drs. Mike Proschan and Janet Wittes) of the guiding book titled “Statistical Monitoring of Clinical Trials: A Unified Approach,” which is widely used by industry and academia as a textbook in clinical trial design. Gordon was elected Fellow of the American Statistical Association in 1992, and Fellow of the Society of Clinical Trials in 2009.
CIMS was founded by Dr. Tai Xie, an industry pioneer focused on reshaping the future of clinical trials through the convergence of EDC and IWRS, statistical modeling and machine learning.
Contact us to learn more about how CIMS’ DDM and DTD allows sponsors to dynamically monitor and optimize their clinical trials — overcoming the drawbacks of classical adaptive group sequential designs to dynamically monitor trials without using an Independent Statistical Group (ISG) and/or Independent Data Monitoring Committee (IDMC).