Double sampling for informatively missing data in EHR-based comparative effectiveness research
Sebastien Haneuse, PhD
Professor of Biostatistics | Harvard T.H. Chan School of Public Health
WHEN: Wednesday, November 8, 2023, from 3:30-4:30 p.m.
WHERE: Hybrid | 2001 McGill College, Rm 1140 | &
Note: Dr. Haneuse will present in-person
Abstract
Missing data are ubiquitous in electronic health records-based comparative effectiveness research. When data are informatively missing (or missing not at random), however, standard methods cannot be relied upon to guarantee valid estimation and inference. In such settings, double-sampling facilitates the collection of additional data which, coupled with appropriate assumptions, may provide a means to perform valid causal inference. In this work we present recent methodologic developments on the use of double sampling towards estimation and inference regarding causal average treatment effects and weighted quantile treatment effects. The work is motivated by and illustrated with an EHR-based study of long-term outcomes following bariatric surgery.
Speaker Bio
Dr. Haneuse is a Professor of Biostatistics and Director of Graduate Studies, overseeing the PhD program at Harvard T.H. Chan School of Public Health. He is an AE for Biometrics, and Statistical Editor for JAMA Network Open.
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