[Online] Lessons from COVID-19: Non-random Missing Data and Its Consequences (Aug 8, 2023)

There is an upcoming live webinar, that is free and open to the public:
Lessons from COVID-19: Non-random Missing Data and Its Consequences

DATE: Tuesday, Aug 8, 2023
TIME: 12pm ET (16:00 UTC)
WHERE: online (BigMarker is a browser based platform, no download required, use Chrome for best experience)
SIGN-UP: on Meetup
COST: Free


A fundamental challenge for survey and observational datasets is that not all records in the dataset are complete; key pieces of information may be missing.

In this talk I work through the models and methods from the paper

They write:

In emergency situations, such as a surging pandemic, it is easy to see how the disease process itself may induce non-random missingness of covariates. For example, during a period of rapidly increasing caseloads, such as the Delta and Omicron surges of the COVID-19 pandemic, the overwhelming number of cases is likely to limit the ability of case investigators to collect data that are as detailed as those collected during lower-incidence periods. These differences may also be more pronounced when comparing wealthier and poorer jurisdictions with differential resources for case-finding and intervention.

Using the Stan language and CmdStanR interface, together with a simulated dataset of COVID-19 cases and population demographics, where age, gender, race/ethnicity, and neighborhood have varying degrees of missingness, we will demonstrate how different approaches produce different estimates of COVID-19 prevalence among key demographics.


This event is being co-promoted with R-Ladies NYC. R-Ladies NYC is part of a world-wide organization to promote gender diversity in the R community. We aspire to encourage and support women and gender minorities interested in learning and sharing their experiences in R programming by hosting a variety of events including talks, workshops, book clubs, data dives, and socials. (https://www.rladiesnyc.org/)


Mitzi Morris is a member of the Stan Development Team and serves on the Stan Governing Body. Since 2017 she has been a full-time Stan developer, working for Professor Andrew Gelman at Columbia University, where she has contributed to the core Stan C++ platform and developed CmdStanPy, a modern Python interface for Stan. She is also as an active Stan user, developing, publishing, and presenting on Bayesian models for disease mapping. Prior to that she has worked as a software engineer in both academia and industry, working on natural language processing and search applications as well as data analysis pipelines for genomics and bioinformatics.

Mitzi on GitHub github.com/mitzimorris

Video Recording

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