Seminar 2025-03-19: Arvid Sjölander

Topic Testable implications of outcome-independent MNAR

Abstract The standard taxonomy for missing data analysis separates missingness mechanisms into “missing completely at random” (MCAR), “missing at random” (MAR) and “missing not at random” (MNAR). Whereas multiple imputation requires MAR for unbiasedness, it is often argued that the simpler complete-case analysis requires the stronger condition MCAR. In this presentation, we will show that a complete-case analysis can be unbiased under a realistic special case of MNAR, which we label outcome-independent MNAR, and we show that multiple imputation is generally biased under this missingness mechanism. This challenges the common assertion that multiple imputation is always preferable to a complete-case analysis, from a bias perspective. We further show that the assumption of outcome independent MNAR can be tested with data. This stands in contrast to MAR, which is fundamentally untestable.