Robust Inference in High-Dimensional Multivariable Mendelian Randomization with Potentially Invalid Instruments

September 30, 2022
12:00 pm to 1:00 pm

Event sponsored by:

Biostatistics and Bioinformatics


Ethan Fang


Lan Wang, PhD


Lan Wang, PhD
Professor & Chair, Management Science, University of Miami

We consider a new framework for Mendelian Randomization analysis with multivariate exposures and high-dimensional confounders and genetic instruments based on individual-level data without specifying an exposure model. We propose a novel confidence interval for the causal effects in the challenging setting where many instruments may have direct effects on the outcome and/or be correlated with an unmeasured confounder. The validity of the confidence intervals is established under relatively weak conditions without requiring prior knowledge of a subset of valid instruments. The new procedure explores the sparsity of the outcome model and requires weaker conditions for identifying the causal effects with potentially invalid instruments or many weak instruments. We also extend the approach to nonlinear outcome models with Poisson-type responses. Numerically, we demonstrate that the new method has satisfactory performance and is robust to invalid instruments. The proposed method is illustrated on two real data examples from the UK Biobank.

Bio: Dr. Lan Wang is a tenured Professor and department chair of the Department of Management Science at the Miami Herbert Business School of the University of Miami, with a secondary appointment as Professor of Public Health Sciences at the Miller School of Medicine, University of Miami. She currently serves as the Co-Editor for Annals of Statistics (2022-2024), jointly with Professor Enno Mammen. Before joining the University of Miami, she was a Professor of Statistics at School of Statistics, University of Minnesota. She got her Ph.D. in Statistics from the Pennsylvania State University and her Bachelor's degree in Applied Mathematics from Tsinghua University, China. Dr. Wang's research covers several interrelated areas: high-dimensional statistical learning, quantile regression, optimal personalized decision recommendation, and survival analysis. She is interested in interdisciplinary collaboration, driven by applications in healthcare, business, economics, and other domains. Dr. Wang is an elected Fellow of the American Statistical Association, an elected Fellow of the Institute of Mathematical Statistics, and an elected member of the International Statistical Institute. She was the associate editor for several leading statistical journals.

Host: Ethan Fang

Passcode: 936337