Missing data is a common challenge when analyzing epidemiological data, and imputation is often used to address this issue. This talk from Lucy D’Agostino McGowan, assistant professor in the Department of Statistical Sciences at Wake Forest University, will investigate the scenario where a covariate used in an analysis has missingness and will be imputed. We examine deterministic imputation (i.e., single imputation with a fixed value) and stochastic imputation (i.e., single or multiple imputation with random values) methods and their implications for estimating the relationship between the imputed covariate and the outcome. We mathematically demonstrate that including the outcome variable in imputation models is not just a recommendation but a requirement to achieve unbiased results when using stochastic imputation methods. Moreover, we dispel common misconceptions about deterministic imputation models and demonstrate why the outcome should not be included in these models. This talk aims to bridge the gap between imputation in theory and in practice, providing mathematical derivations to explain common statistical recommendations.

Connect via Zoom at 2 p.m. on March 14: https://temple.zoom.us/j/98898845323