“Risk factors associated with COVID-19 Mortality using Multiple Imputation” by Prince Allotey PhD candidate, Statistics Department, University of Connecticut

Missing data is common and avoidable in all research. Although most practitioners are still ignoring the missing data problem, numerous papers and research articles demonstrate that dealing with it correctly is very important. Missing data may lead to bias and misleading results.

In this talk, we will introduce missing data vocabulary and present problems and solutions to the missing data issue. We will emphasize more on ad-hoc procedures for dealing with missing data and also compare results from ad-hoc and Multiple Imputation procedures.