Biostatistics Seminar: A Framework for Personalizing the Timing of Surveillance Testing.

Abstract: Frequent surveillance testing using biomarkers is recommended and routinely conducted in several disease settings. Although surveillance tests provide information about current disease status and present an opportunity to detect disease progression early, the complications and costs of frequent tests may not be justified for patients who are at low risk of progression. I will discuss our recently developed Personalized Risk-Adaptive Surveillance (PRAISE) framework, a method for embedding dynamic predictions into a sequential decision-making framework to determine the time point at which the next collection of biomarker data would be most valuable. I will demonstrate a preliminary application of the framework in cystic fibrosis and ongoing work in colorectal cancer to develop more cost-effective and personalized surveillance strategies. Speaker Bio: Aasthaa Bansal is an Associate Professor at The Comparative Health Outcomes, Policy, and Economics (CHOICE) Institute, University of Washington, with a joint appointment at the Fred Hutchinson Cancer Center, Seattle. Her research interests include the development and evaluation of prediction models for sequential decision making, comparative effectiveness and outcomes research using large healthcare databases, addressing missingness in longitudinal patient data due to intermittent follow-up, and addressing algorithmic fairness to reduce disparities in healthcare decision-making. She completed her PhD and postdoctoral fellowship at the University of Washington Department of Biostatistics. Prior to that, she received her BMath from the University of Waterloo, Canada.