Bridging the gap between imputation theory and practice

Handling missing data presents a significant challenge in epidemiological data analysis, with imputation frequently employed to handle this issue. It is often advised to use the outcome variable in the imputation model for missing covariates, though the rationale of this advice is not always clear. This presentation will explore both deterministic imputation (i.e., single imputation using fixed values) and stochastic imputation (i.e., single or multiple imputation using random values) approaches and their effects on estimating the association between an imputed covariate and outcome. We will show that the inclusion of the outcome variable in imputation models is not merely a suggestion but a necessity for obtaining unbiased estimates in stochastic imputation approaches. Furthermore, we will clarify misconceptions regarding deterministic imputation models and explain why the outcome variable should be excluded from these models. The goal of this presentation is to connect theory behihnd imputation and its practical application, offering mathematical proofs to elucidate common statistical guidelines.

March 5, 2024

10:00 AM – 11:00 AM

National Institute for Research in Digital Science and Technology 2024