Statistical methods

Broadly, I am interested in developing statistical methods and software for assessing the time-varying predictive accuracy of survival models. The below references give a sample of the current state of the art.

• Saha-Chaudhuri, P., & Heagerty, P. J. (2013). Non-parametric estimation of a time-dependent predictive accuracy curve. Biostatistics, 14(1), 42-59.
• Uno, H., Tian, L., Cai, T., Kohane, I. S., & Wei, L. J. (2012). A unified inference procedure for a class of measures to assess improvement in risk prediction systems with survival data. Statistics in Medicine.
• Pencina, M. J., D'Agostino, R. B., & Vasan, R. S. (2008). Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Statistics in medicine, 27(2), 157-172.
• Heagerty, P. J., & Zheng, Y. (2005). Survival model predictive accuracy and ROC curves. Biometrics, 61(1), 92-105.

My methodological work also lends itself to interesting visualizations of survival data. The recent proliferation of tools (rCharts, ggvis, Shiny, D3.js) are greatly lowering the barrier for creating and sharing interactive visualizations. I am interested in using these tools to make my methods even easier to pick up and use.

Applications

I am currently working with collaborators in cardiovascular research, medical imaging, and pharmacokinetics.