Kelly Van Lancker
Postdoctoral Researcher
I am a statistician who is driven by the application of statistics in biomedical sciences. My goal is to develop innovative designs and analytical techniques for drawing causal inferences in health sciences. In general, I seek to make better use of the rich patient data collected within a clinical study, both to reduce uncertainty surrounding treatment effects and to develop better prediction tools for personalized medicine.
A big part of my research focuses on more accurate and faster decision-making in randomized clinical trials by making optimal use of the available data. I thereby mainly focus on covariate adjustment, variable selection, complex designs, estimands and especially a combination of these topics.
My recent research is primarily aimed at learning about the opportunities and challenges in running pragmatic trials within clinical practice. In this project, we will develop a data-informed system to assist personalized treatment decision-making based on predictions of what a patient’s outcome would be if one of multiple treatment options were considered. These predictions will be based on available EHRs, but unlike standard predictions, will consider the causal structure of the problem and account for confounding bias.
Currently, I am a postdoctoral fellow in the Department of Applied Mathematics, Computer Science and Statistics at Ghent University in Belgium, working with Stijn Vansteelandt on the development of a data-informed causal expert system for personalized treatment recommendations. Previously, I was a postdoctoral researcher in the Department of Biostatistics at the Johns Hopkins Bloomberg School of Public Health under supervision of Michael Rosenblum.
