hi everyone, I’m going to be giving an online talk:
Lessons from COVID-19: Non-random Missing Data and Its Consequences
DESCRIPTION:
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
MODELING RACIAL/ETHNIC DIFFERENCES IN COVID-19 INCIDENCE WITH COVARIATES SUBJECT TO NON-RANDOM MISSINGNESS
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.
Co-promotion
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/)
