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Methods for Missing Data
Course Description
This course provides an overview of the types of missing data including missing completely at random, missing at random, and missing at random/non-ignorable missing data mechanisms. We will discuss principled approaches for handling missing data, including inverse probability-weighting and multiple imputation. Topics will also include random forest-based imputation and more advanced methods for handling missing not at random data, including pattern mixture models and delta-adjusted multiple imputation.
Instructor
Tanayott Thaweethai, PhD
Associate Director, Biostatistics Research and Engagement,
Massachusetts General Hospital
Assistant Professor of Medicine, Biostatistics, Harvard Medical School
Tanayott (Tony) Thaweethai, PhD is the Associate Director of Biostatistics Research and Engagement at Massachusetts General Hospital Biostatistics, an Assistant Professor of Medicine at Harvard Medical School, and an Assistant Professor in the Department of Biostatistics at Harvard T.H. Chan School of Public Health. He has taught the course “Applied Regression Analysis” every year at Harvard Chan since 2023, which covers regression model building and interpretation. He has also lectured nationally and internationally regarding his statistical research, which focuses on missing data in observational studies, as well as his clinical research, which include diabetes and Long COVID. His work has been published in JAMA, Nature Communications, Annals of Internal Medicine, JAMA Pediatrics, JAMA Network Open, and other high-impact journals.