Biostatistics Weekly Seminar

In modern statistical analysis, how to effectively analyze and interpret data with outliers is a challenging problem. Classical mean-based methods such as t-test and generalized linear model (GLM) generally yield uninterpretable results when applied to such data. Rank-based methods such as the Mann-Whitney-Wilcoxon Rank Sum Test (MWWRST) are effective alternatives. We have been developing a new regression paradigm of modeling “between-subject attributes” to allow the MWWRST for broader applications such as in survey data and causal inference as well as to model high-dimensional data such as microbiome beta diversity. These newly developed methods have wide applications in survey, observational and longitudinal studies. In this talk, I will first introduce the distinction between sampling weights in survey studies and “probability weights” in observational and longitudinal studies with missing data. Next, I will elucidate semiparametric methods of modeling between-subject attributes in survey studies along with a study of their efficiency. As machine learning methods have become unprecedently popular in the Big Data Era and motivated by a recent seminal work of Wager and Athey (2018) on asymptotic properties of random forests, I will end my talk by presenting our latest extension of random forests to between-subject attributes for non-parametric regression analysis, with an illustrative example for causal inference using the MWWRST.

Hybrid: Meeting Room and Zoom Link to Follow
20 January 2023
1:30pm