Zhou Group

The major focus of our methodology work is the development of new biostatistical learning algorithms in precision medicine. We work on the methodological development of machine learning for the derivation of individualized treatment rules (ITRs) to tailor treatments for patients with heterogeneous responses to different medications and health care practices. The main goal is to find a treatment rule that will provide patients an individualized treatment that optimizes therapeutic effects.

We are also currently involved in an international clinical trial that compares different treatments for spinal muscular atrophy. A portion of the trial’s data lacks patient-specific information, so we create new methods to incorporate summary statistics from this limited dataset. By developing new methods, we can help derive a better division function to determine an effective precision medicine approach for this cohort.

Beyond these two areas within method development, our group participates in longitudinal and spatio-temporal data analyses.

Outside our research in method development, there is an interest in clinical projects related to maternal health, environmental health, infectious diseases, and transplantation. We currently have a collaboration with the Department of Bone Marrow Transplantation and Cellular Therapy in which we build predictive machine learning models—using established machine learning algorithms—that predict the overall survival for patients receiving bone marrow transplantation at St. Jude.

We are also involved in an ongoing St. Jude clinical trial focused on lymphoma, in which we monitor the trial design and develop a robust statistical analysis plan.

Through our involvement in both the development of novel machine learning algorithms and the support of clinical research, our contribution to the application of precision medicine helps advance clinical research and care.