Workshop in Biostatistics
|DATE:||February 11, 2016|
|TIME:||1:30 - 3:00 pm|
|LOCATION:||Medical School Office Building, Rm x303|
|TITLE:||Precision Medicine, Learning Health Systems, and Improving Surveillance of Low Risk Prostate Cancer
Postdoctoral Research Fellow, Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health
We present a project from the Johns Hopkins Individualized Health Initiative to support a personalized prostate cancer management program. For individuals with a diagnosis of low risk prostate cancer, active surveillance offers an alternative to early curative intervention. The success of surveillance depends on being able to effectively distinguish indolent tumors from those with metastatic potential, a characteristic that cannot be directly observed without surgical removal of the prostate. We have developed a Bayesian hierarchical model for prediction of an individual's latent cancer state by integrating multiple sources of data collected in the practice of active surveillance. Existing modeling approaches are extended to accommodate measurement error in cancer state determinations based on biopsied tissue and to allow observations to possibly be missing not at random. Predictions can be updated in real time with an importance sampling algorithm and communicated with patients and clinicians through a decision support tool. Integration of the model into the clinical workflow will automate model estimation and enable a continuously learning prediction model.
Coley, RY, et al. (2016) http://arxiv.org/abs/1508.07511 . A Bayesian Hierarchical Model for Prediction of Latent Health States from Multiple Data Sources with Application to Active Surveillance of Prostate Cancer.