Abstract

In this talk, I will cover a few ideas in tackling non-standard problems in statistical inference, including Bartlett identity, boundary and identifiability issues. I will show that these considerations are critical in model robustness, statistical power, and validity. I will also present implications of these ideas in addressing key challenges in biomedical research using massive healthcare data, in particular, electronic health records, drug/vaccine safety surveillance data. Case studies using University of Pennsylvania Biobank data will be provided.