Real-world data is messy. However, significant insights can be gleaned from informative missingness in data. In a study funded by an NCI R35 award, we developed a two-phase model to flexibly account for missingness in predictive models. We are expanding this work to account for missingness and sparsity in mHealth and EHR data.
Research Categories