Are you overlooking a crucial element in your epidemiological data analysis? This paper tackles the common challenge of missing data in covariates and the nuances of using imputation models. It explores the critical decision of whether to include the outcome variable ('Y') in the imputation model for missing covariates, clarifying the rationale behind this practice. Through mathematical demonstrations, the study reveals that including the outcome variable in stochastic imputation methods isn't merely a recommendation but a necessity for achieving unbiased results. Conversely, it challenges misconceptions surrounding deterministic imputation models, explaining why the outcome variable should be excluded in these cases. The analysis distinguishes between deterministic imputation (i.e. single imputation with fixed values) 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. By bridging the gap between imputation theory and practical application, this article provides a deeper understanding of the considerations involved in imputing missing covariates. It emphasizes the conditions under which including the outcome variable becomes essential for obtaining accurate and reliable results, making it a valuable resource for researchers in epidemiology and related fields.
Published in Statistical Methods in Medical Research, this article fits squarely within the journal's scope by offering a detailed analysis of a statistical technique commonly used in medical research. Specifically, it examines the nuances of imputation methods for handling missing data. By providing a mathematical foundation for best practices in this area, the study contributes to the rigor and reliability of medical research findings.